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feat(local-model): Ornith 1.0 9B, the first model a 16 GB Mac can hold
A 16 GB Mac got no model: the two 9B builds of the catalogue need 10.2 GB at 65,536 tokens, 0.987 of the estimated budget, just past the 0.98 a tight fit may reach. New row `ornith:9b-q4_k_m` (Ornith 1.0 9B, MIT), a fine-tune of Qwen3.5-9B for coding agents whose GGUF build has no vision projector: 5.63 GB against 6.55 GB, so 9.3 GB at 65,536 tokens, 0.898 of that budget. SWE-bench Verified 69.4, SWE-bench Pro 42.9, Terminal-Bench 2.1 43.1 on Ornith's card. Its config.json and its GGUF header are those of Qwen3.5-9B (no prediction layer), so it shares the figures of the two other 9B rows. Never run on a Mac: `estimated`. - What changes for a Mac: 16 GB goes from no model to `limited` with this row, tight. That is the only pick that changes: the row is ranked under MiMo V2.6 9B, so every Mac that could already run a 9B keeps its pick and gains an alternative. With the budget a 16 GB Mac mini measured (10.7 GB) the pick is MiMo as before. - Why under MiMo although its own card's numbers are higher: the two vendors' tables put their common base 7 points apart, and runtime issue 16992 (open) was filed on the 35B sibling, which has the same renderer and parser. The 35B stays set aside. - The 16 GB rule itself is untouched: same budget estimate, same 0.98, same eight context checkpoints. - Ranks renumbered 1 to 10; the pins of test/local-model-recommend.test.ts and the table of docs/local-models.md follow. Co-Authored-By: Claude Fable 5.2 <noreply@anthropic.com>
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@@ -18,16 +18,16 @@ in [architecture-invariants.md](architecture-invariants.md#local-model-install).
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The window offers the best model of the catalogue (`src/config/local-model-catalog.ts`) that
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fits this Mac's memory at a context a coding agent can work with, and under it every other
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model of the catalogue that fits (nine models today: an 18 GB Mac sees two of them, a 512 GB
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Mac all nine). What the catalogue answers today, before the runtime measured anything (the
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model of the catalogue that fits (ten models today: a 16 GB Mac sees one of them, a 512 GB
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Mac all ten). What the catalogue answers today, before the runtime measured anything (the
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budget is then an estimate: 2/3 of the memory below 36 GB, 3/4 from 36 GB, less 1 GiB, and
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never more than the memory less 6, 8 or 12 GiB for macOS, Codeman, a browser and the agent):
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| Memory | Budget | What it gets |
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| ---------------- | ------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| 8 GB | 2.1 GB | Nothing. The smallest model a coding agent can work with needs 10.2 GB at 65,536 tokens |
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| 16 GB | 10.4 GB | Nothing. MiMo V2.6 9B at 65,536 tokens needs 10.2 GB, 98.7% of the budget, just past the 98% a tight fit may reach (it is 95% of the 10.7 GB a 16 GB Mac mini measured). On the edge either way: only a run on such a Mac settles it |
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| 18 GB | 11.8 GB | A small model (`limited`): MiMo V2.6 9B at 131,072 tokens, 11.4 GB, tight. Its base, Qwen3.5 9B, is the one alternative |
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| 8 GB | 2.1 GB | Nothing. The smallest model a coding agent can work with needs 9.3 GB at 65,536 tokens |
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| 16 GB | 10.4 GB | A small model (`limited`): Ornith 1.0 9B at 65,536 tokens, 9.3 GB, tight (90%). Its build has no vision projector, which makes it 0.9 GB smaller than the other two 9B builds: MiMo V2.6 9B needs 10.2 GB there, 98.7% of the budget, just past the 98% a tight fit may reach. Once the runtime measures what a 16 GB Mac mini measured (10.7 GB), all three fit and MiMo is the pick. No 9B was run on such a Mac by us: only a run settles it |
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| 18 GB | 11.8 GB | A small model (`limited`): MiMo V2.6 9B at 131,072 tokens, 11.4 GB, tight. Ornith 1.0 9B (10.5 GB) and Qwen3.5 9B are the alternatives |
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| 24 GB | 16.1 GB | The same, with room. On a base M2 or M3 (100 GB/s) it computes to 9.3 tokens per second: slow |
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| 32 GB | 21.8 GB | No longer a small-model Mac: Muse Glimmer 30B fits at 65,536 tokens (21.2 GB, 97%, tight). It is a dense model. On an M1 Max or M2 Max it computes to 11.8 tokens per second and is the pick; on a base or Pro chip it computes to under 6 and is listed as slow, and the pick stays MiMo V2.6 9B at 131,072 tokens with the note that it is a small model. North Mini Code 1.0 needs 23.2 GB at 65,536 tokens; once the runtime measures about 3/4 of the memory, as it did on a 16 and a 48 GB Mac, the Mac gets it at 131,072 tokens, or Qwen3.8 27B at 65,536 tokens on a Max chip |
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| 36 GB | 27.9 GB | Qwen3.8 27B at 131,072 tokens on an M4 or M5 Max (27.0 GB, 97%); on a slower chip, where the dense 27B computes to under 10 tokens per second, Qwen3.6 35B-A3B as a GGUF build (27.1 GB, 97%). Both tight. Muse Glimmer 30B at 131,072 tokens (21.6 GB, 78%) is the alternative with room to spare |
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@@ -78,6 +78,7 @@ Where every figure comes from:
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| Model | Runner | Memory figures | Speed (tokens per second per 100 GB/s) |
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| ---------------------------- | --------- | ----------------------------------------------------------------------------------- | --------------------------------------------------------- |
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| `qwen3.5:9b-q4_k_m` | llama.cpp | computed from the Qwen3.5-9B config | 9.3: a published run on an M1 Max |
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| `ornith:9b-q4_k_m` | llama.cpp | the same (a fine-tune of Qwen3.5-9B; its config and its GGUF header say so) | 9.3: the same architecture and weights size |
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| `mimo-v2.6:9b-q4_k_m` | llama.cpp | the same (a fine-tune of Qwen3.5-9B) | 9.3: the same architecture and size |
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| `north-mini-code-1.0:q4_k_m` | llama.cpp | computed from its config | 13.6: the 35B-A3B's, the same 3 billion active parameters |
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| `muse-glimmer:30b-q4_k_m` | llama.cpp | computed from its config: 13 full-attention and 39 sliding-window layers | 2.95: the dense 27B's, whose weights are the same size |
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@@ -88,7 +89,7 @@ Where every figure comes from:
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| `ornith-1.5:122b` | MLX | computed from the Qwen3.5-122B-A10B config, scaled by the measured MLX row, + 8 GiB | 5.0: the 35B-A3B's, x 3/10 active parameters |
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Sizes are what the registry serves a Mac for each tag (every layer plus the config), read on
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2026-11-08 (the Muse Glimmer row on 2026-11-10). Only one row was ever run on
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2026-11-08 (the Ornith 1.0 and Muse Glimmer rows on 2026-11-10). Only one row was ever run on
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a Mac, so every other answer in the table above is a calculation. For a llama.cpp build the check after the download catches a load that was
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judged too kindly, but not what grows after it in host memory (the checkpoints and the prompt
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cache, which are counted, not checked); for an MLX build it catches neither (see "The check"
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@@ -24,9 +24,9 @@
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* throws on a bad row. test/local-model-catalog.test.ts pins that the stock constant parses,
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* so a bad row fails the gate and never reaches the route.
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*
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* Where each figure of the stock rows comes from, and what was checked (2026-11-08; the row
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* added on 2026-11-10, `muse-glimmer:30b-q4_k_m`, was read that day). [V] read at the source
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* that day, [M] measured on a Mac, [C] computed from [V] inputs:
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* Where each figure of the stock rows comes from, and what was checked (2026-11-08; the two rows
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* added on 2026-11-10, `ornith:9b-q4_k_m` and `muse-glimmer:30b-q4_k_m`, were read that day).
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* [V] read at the source that day, [M] measured on a Mac, [C] computed from [V] inputs:
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*
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* | Figure | Source | Checked |
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* |----------------------|-----------------------------------------------------------|--------------------------------------|
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@@ -76,8 +76,8 @@
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* "MTP" below is a GGUF with a multi-token-prediction layer (`nextn_predict_layers` 1 in
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* its header, read on 2026-11-08 from the first bytes of each model layer: the two `-mtp`
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* tags and `qwen3.8:27b-q8_0` have it, the two 9B tags and North Mini Code do not; read
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* the same way on 2026-11-10, `muse-glimmer:30b-q4_k_m` does not either). For such a
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* model the runtime always passes
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* the same way on 2026-11-10, `ornith:9b-q4_k_m` and `muse-glimmer:30b-q4_k_m` do not
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* either). For such a model the runtime always passes
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* `--spec-type draft-mtp --spec-draft-n-max N`, N
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* being the tag's own `draft_num_predict` (its params layer: 4 for
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* `qwen3.8:27b-mtp-q4_k_m`, 2 for `qwen3.6:35b-a3b-mtp-q4_k_m`) or the runtime's default
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@@ -146,11 +146,11 @@
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* manifests name no renderer and no tool parser, so whether tool calls work through the
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* runtime is unknown (runtime pull request 17965 is still open). `ornith-1.5:122b` has
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* both.
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* - `ornith:9b`, `ornith:35b` (Ornith 1.0, MIT, with a renderer and a tool parser; SWE-bench
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* Verified 69.4 and 75.6 on Ornith's card, against 73.4 for Qwen3.6 35B-A3B): runtime issue
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* 16992, open, reports the 35B's tool calls arriving as text in long agent chains, and the
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* two share the renderer and the parser. The 9B (5.6 GB, no vision projector) is the one
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* model that would fit a 16 GB Mac on the estimated budget: 9.3 GB at 65,536 tokens.
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* - `ornith:35b` (Ornith 1.0 35B-A3B, MIT, with a renderer and a tool parser; SWE-bench
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* Verified 75.6 against 73.4 for Qwen3.6 35B-A3B on Ornith's card): runtime issue 16992,
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* open, reports its tool calls arriving as text in long agent chains, and its size is
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* served by the pair we ran. Its 9B sibling `ornith:9b-q4_k_m` is listed: see that row
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* for why.
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* - `gpt-oss:20b`: weak in agentic coding (SWE-bench Verified 52.4, Terminal-Bench 2.1 15.2
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* in NVIDIA's table).
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* - `flint:40b`: runtime issue 19191 (open) breaks integer tool arguments after the first
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@@ -382,7 +382,7 @@ export const STOCK_LOCAL_MODEL_CATALOG: LocalModelCatalog = {
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// that are left out and why.
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models: [
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{
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// Small Macs (18 to 32 GB): the base model of the row below.
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// Small Macs (18 to 32 GB): the base model of the two rows below.
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id: 'qwen3.5:9b-q4_k_m',
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label: 'Qwen3.5 9B',
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license: 'Apache-2.0',
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@@ -402,6 +402,34 @@ export const STOCK_LOCAL_MODEL_CATALOG: LocalModelCatalog = {
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speedPer100Gbps: 9.3,
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tested: [],
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},
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{
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// The model a 16 GB Mac can hold: 9.3 GB at 65,536 tokens, tight. Ornith 1.0 9B, a
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// fine-tune of Qwen3.5-9B for coding agents, as a GGUF build without a vision projector,
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// so 0.9 GB smaller than the rows around it. SWE-bench Verified 69.4, SWE-bench Pro 42.9
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// and Terminal-Bench 2.1 43.1, against 53.2, 31.3 and 21.3 for its base (Ornith's card,
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// which puts that base 7 points under Xiaomi's figure for it: the two cards do not
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// compare). Ranked under MiMo for that, and because runtime issue 16992 was filed on its
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// 35B sibling, which has the same renderer and parser (see the header): it is the pick
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// only where nothing else fits. Text only.
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id: 'ornith:9b-q4_k_m',
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label: 'Ornith 1.0 9B',
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license: 'MIT',
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paramsB: 9,
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runner: 'llamacpp',
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bytes: 5_629_110_568,
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// The architecture of the row above (its config.json and its GGUF header say so), so
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// its figures.
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heldBytesPerToken: 17_408,
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overheadBytes: 52_690_944 * (1 + LLAMACPP_CTX_CHECKPOINTS) + LLAMACPP_RUNNER_BYTES + LLAMACPP_PROMPT_CACHE_BYTES,
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peakExtraBytes: 0,
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figures: 'estimated',
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contexts: [131072, 65536],
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rank: 2,
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// Not measured: the 9.3 of the row above, the same architecture and the same weights file
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// size (5.63 GB without the projector).
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speedPer100Gbps: 9.3,
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tested: [],
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},
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{
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// Small Macs (18 to 32 GB). MiMo-V2.6-Distill-Qwen-9B, a fine-tune of
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// Qwen3.5-9B for code and agents: SWE-bench Verified 61.1 and SWE-bench Pro 44.6,
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@@ -413,14 +441,14 @@ export const STOCK_LOCAL_MODEL_CATALOG: LocalModelCatalog = {
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paramsB: 9,
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runner: 'llamacpp',
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bytes: 6_550_811_033,
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// The architecture and the quantization of the row above, so its figures.
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// The architecture and the quantization of the base model's row, so its figures.
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heldBytesPerToken: 17_408,
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overheadBytes: 52_690_944 * (1 + LLAMACPP_CTX_CHECKPOINTS) + LLAMACPP_RUNNER_BYTES + LLAMACPP_PROMPT_CACHE_BYTES,
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peakExtraBytes: 0,
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figures: 'estimated',
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contexts: [131072, 65536],
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rank: 2,
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// Not measured: the 9.3 of the row above, the same architecture and file size.
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rank: 3,
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// Not measured: the 9.3 of the base model's row, the same architecture and file size.
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speedPer100Gbps: 9.3,
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tested: [],
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},
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@@ -442,7 +470,7 @@ export const STOCK_LOCAL_MODEL_CATALOG: LocalModelCatalog = {
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peakExtraBytes: 0,
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figures: 'estimated',
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contexts: [262144, 131072, 65536],
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rank: 3,
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rank: 4,
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// Not measured: the 13.6 measured for Qwen3.6 35B-A3B on llama.cpp, which has the same
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// 3 billion active parameters.
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speedPer100Gbps: 13.6,
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@@ -469,7 +497,7 @@ export const STOCK_LOCAL_MODEL_CATALOG: LocalModelCatalog = {
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peakExtraBytes: 0,
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figures: 'estimated',
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contexts: [131072, 65536],
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rank: 4,
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rank: 5,
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// Not measured: the 2.95 of Qwen3.8 27B, a dense model whose weights are the same size on
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// llama.cpp (16.8 GB each without the projector).
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speedPer100Gbps: 2.95,
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@@ -495,7 +523,7 @@ export const STOCK_LOCAL_MODEL_CATALOG: LocalModelCatalog = {
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peakExtraBytes: 0,
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figures: 'estimated',
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contexts: [262144, 131072, 65536],
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rank: 5,
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rank: 6,
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// Measured: 54.5 tokens per second on 400 GB/s (llama.cpp, without prediction).
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speedPer100Gbps: 13.6,
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tested: [],
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@@ -513,7 +541,7 @@ export const STOCK_LOCAL_MODEL_CATALOG: LocalModelCatalog = {
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...MEASURED_MEMORY,
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figures: 'measured',
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contexts: [131072, 65536],
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rank: 6,
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rank: 7,
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// Measured: 67.8 tokens per second on 400 GB/s with MLX. The one Mac we tested ran it at
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// 67.7 on 307 GB/s, so on other chips the estimate is on the low side.
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speedPer100Gbps: 16.9,
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@@ -538,7 +566,7 @@ export const STOCK_LOCAL_MODEL_CATALOG: LocalModelCatalog = {
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peakExtraBytes: 0,
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figures: 'estimated',
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contexts: [262144, 131072, 65536],
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rank: 7,
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rank: 8,
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// Not measured: the 2.95 of the row below scaled by file size.
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speedPer100Gbps: 1.7,
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tested: [],
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@@ -562,7 +590,7 @@ export const STOCK_LOCAL_MODEL_CATALOG: LocalModelCatalog = {
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peakExtraBytes: 0,
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figures: 'estimated',
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contexts: [262144, 131072, 65536],
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rank: 8,
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rank: 9,
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// Measured: 11.8 tokens per second on 400 GB/s (llama.cpp, Q4_K_M, without prediction).
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// This build predicts several tokens at a time, so it is faster than this figure says.
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speedPer100Gbps: 2.95,
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@@ -586,7 +614,7 @@ export const STOCK_LOCAL_MODEL_CATALOG: LocalModelCatalog = {
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peakExtraBytes: MEASURED_MEMORY.peakExtraBytes,
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figures: 'estimated',
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contexts: [262144, 131072],
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rank: 9,
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rank: 10,
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// Not measured: the 16.9 of Qwen3.6 35B-A3B on MLX x 3/10, its active parameters
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// against these, rounded down.
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speedPer100Gbps: 5,
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@@ -112,7 +112,7 @@ export const LIMITED_BELOW_PARAMS_B = 12;
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export const DISK_HEADROOM_BYTES = 10 * GIB;
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/**
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* How many other models are listed under the pick, at most. Above what the stock catalogue
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* holds (9 models: 8 beside the pick), so a Mac is shown every model that fits it and the
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* holds (10 models: 9 beside the pick), so a Mac is shown every model that fits it and the
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* install accepts every one of them. Still a bound: test/local-model-catalog.test.ts fails for
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* a catalogue that outgrows it, so nothing drops out of the list unseen.
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*/
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@@ -67,7 +67,8 @@ const GIB = 1024 * MIB;
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* Hugging Face (2026-11-08; Muse Glimmer on 2026-11-10): full-attention layers, their K/V heads
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* and head size, and the layers whose cache does not grow with the context (Gated DeltaNet
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* layers with their value and key heads, or sliding-window layers with their window).
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* Fine-tunes share their base's.
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* Fine-tunes share their base's: the config.json of Ornith 1.0 9B (2026-11-10) is that of
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* Qwen3.5-9B in every field below, and the GGUF header of its build says the same.
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*/
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const ARCHITECTURES = {
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'Qwen3.5-9B': { attentionLayers: 8, kvHeads: 4, headDim: 256, linear: { layers: 24, vHeads: 32, kHeads: 16 } },
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@@ -91,7 +92,7 @@ type Architecture = (typeof ARCHITECTURES)[ArchitectureName];
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* Which architecture each stock row runs, and how many tokens the runtime drafts per step with
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* its multi-token-prediction layer: 0 when the GGUF has none (`nextn_predict_layers` in the GGUF
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* header, read on 2026-11-08 from the first bytes of each model layer; on 2026-11-10 for the
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* Muse Glimmer row), else the tag's own
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* Ornith 1.0 and Muse Glimmer rows), else the tag's own
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* `draft_num_predict` (its params layer) or the runtime's default of 4 when the tag sets none.
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* Prediction costs a cache for that layer and one rollback copy of the recurrent state per
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* drafted token (see the catalogue header). A row the catalogue gains must be added here, or the
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@@ -99,6 +100,8 @@ type Architecture = (typeof ARCHITECTURES)[ArchitectureName];
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*/
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const ROW_ARCHITECTURE: Record<string, { architecture: ArchitectureName; draftTokens: number }> = {
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'qwen3.5:9b-q4_k_m': { architecture: 'Qwen3.5-9B', draftTokens: 0 },
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// A fine-tune of Qwen3.5-9B; its GGUF has 32 blocks and no prediction layer.
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'ornith:9b-q4_k_m': { architecture: 'Qwen3.5-9B', draftTokens: 0 },
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'mimo-v2.6:9b-q4_k_m': { architecture: 'Qwen3.5-9B', draftTokens: 0 },
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'north-mini-code-1.0:q4_k_m': { architecture: 'North-Mini-Code-1.0', draftTokens: 0 },
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// No prediction layer; the tags with a separate draft model (`-dflash`) are not listed.
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@@ -190,9 +193,10 @@ describe('the stock catalogue', () => {
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expect(getLocalModelCatalog()).toBe(first);
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});
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it('lists nine models, by rank, and none of those set aside in the header', () => {
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it('lists ten models, by rank, and none of those set aside in the header', () => {
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expect(models.map((m) => m.id)).toEqual([
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'qwen3.5:9b-q4_k_m',
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'ornith:9b-q4_k_m',
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'mimo-v2.6:9b-q4_k_m',
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'north-mini-code-1.0:q4_k_m',
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'muse-glimmer:30b-q4_k_m',
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@@ -202,9 +206,9 @@ describe('the stock catalogue', () => {
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'qwen3.8:27b-mtp-q4_k_m',
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'ornith-1.5:122b',
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]);
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expect(models.map((m) => m.rank)).toEqual([1, 2, 3, 4, 5, 6, 7, 8, 9]);
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expect(models.map((m) => m.rank)).toEqual([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]);
|
||||
const setAside =
|
||||
/^(bonsai-2|gpt-oss|flint|lfm2\.5|laguna|qwen3\.8-flash-next|qwen3\.5:(122b|4b)|ripple-preview|nemotron-3\.5-super|qwen3-coder-next)|^ornith-1\.5:(9b|35b|397b)|^ornith:(9b|35b)|^muse-glimmer:.*(mlx|dflash)/;
|
||||
/^(bonsai-2|gpt-oss|flint|lfm2\.5|laguna|qwen3\.8-flash-next|qwen3\.5:(122b|4b)|ripple-preview|nemotron-3\.5-super|qwen3-coder-next)|^ornith-1\.5:(9b|35b|397b)|^ornith:35b|^muse-glimmer:.*(mlx|dflash)/;
|
||||
expect(models.filter((m) => setAside.test(m.id)).map((m) => m.id)).toEqual([]);
|
||||
// The plain 9B and 27B tags are MLX builds on a Mac: their GGUF builds are listed instead.
|
||||
for (const plain of ['qwen3.5:9b', 'mimo-v2.6:9b', 'qwen3.8:27b']) {
|
||||
@@ -215,6 +219,7 @@ describe('the stock catalogue', () => {
|
||||
it('names the runner a Mac uses for each tag', () => {
|
||||
expect(Object.fromEntries(models.map((m) => [m.id, m.runner]))).toEqual({
|
||||
'qwen3.5:9b-q4_k_m': 'llamacpp',
|
||||
'ornith:9b-q4_k_m': 'llamacpp',
|
||||
'mimo-v2.6:9b-q4_k_m': 'llamacpp',
|
||||
'north-mini-code-1.0:q4_k_m': 'llamacpp',
|
||||
'muse-glimmer:30b-q4_k_m': 'llamacpp',
|
||||
@@ -231,6 +236,8 @@ describe('the stock catalogue', () => {
|
||||
expect(Object.fromEntries(models.map((m) => [m.id, m.bytes]))).toEqual({
|
||||
// The registry's manifests as fetched on 2026-11-08, in lowercase.
|
||||
'qwen3.5:9b-q4_k_m': 6_550_825_550,
|
||||
// Fetched on 2026-11-10. No vision projector: the weights, a system prompt, the licence.
|
||||
'ornith:9b-q4_k_m': 5_629_110_568,
|
||||
'mimo-v2.6:9b-q4_k_m': 6_550_811_033,
|
||||
'north-mini-code-1.0:q4_k_m': 18_593_967_008,
|
||||
// Fetched on 2026-11-10: 16.76 GB of weights and a 1.40 GB projector.
|
||||
@@ -265,6 +272,7 @@ describe('the stock catalogue', () => {
|
||||
it('gives every model a parameter count, an id of the default registry and a license', () => {
|
||||
expect(Object.fromEntries(models.map((m) => [m.id, [m.paramsB, m.license]]))).toEqual({
|
||||
'qwen3.5:9b-q4_k_m': [9, 'Apache-2.0'],
|
||||
'ornith:9b-q4_k_m': [9, 'MIT'],
|
||||
'mimo-v2.6:9b-q4_k_m': [9, 'MIT'],
|
||||
'north-mini-code-1.0:q4_k_m': [30, 'Apache-2.0'],
|
||||
// The card's total: a 27.9B language model and a vision encoder.
|
||||
@@ -276,8 +284,12 @@ describe('the stock catalogue', () => {
|
||||
'ornith-1.5:122b': [122, 'MIT'],
|
||||
});
|
||||
for (const model of models) expect(model.id).toMatch(LOCAL_MODEL_ID_PATTERN);
|
||||
// The two 9B rows are the ones under the 12 billion line of the `limited` verdict.
|
||||
expect(models.filter((m) => m.paramsB < 12).map((m) => m.id)).toEqual(['qwen3.5:9b-q4_k_m', 'mimo-v2.6:9b-q4_k_m']);
|
||||
// The three 9B rows are the ones under the 12 billion line of the `limited` verdict.
|
||||
expect(models.filter((m) => m.paramsB < 12).map((m) => m.id)).toEqual([
|
||||
'qwen3.5:9b-q4_k_m',
|
||||
'ornith:9b-q4_k_m',
|
||||
'mimo-v2.6:9b-q4_k_m',
|
||||
]);
|
||||
});
|
||||
|
||||
it('offers no model below the 65536 tokens a harness needs, nor beyond its own window', () => {
|
||||
@@ -289,8 +301,9 @@ describe('the stock catalogue', () => {
|
||||
|
||||
it('has a speed figure only where one was measured or scaled from a measurement', () => {
|
||||
expect(Object.fromEntries(models.map((m) => [m.id, m.speedPer100Gbps ?? null]))).toEqual({
|
||||
// 37.1 tokens per second on 400 GB/s, and the same for the fine-tune of the same size.
|
||||
// 37.1 tokens per second on 400 GB/s, and the same for the two fine-tunes of the same size.
|
||||
'qwen3.5:9b-q4_k_m': 9.3,
|
||||
'ornith:9b-q4_k_m': 9.3,
|
||||
'mimo-v2.6:9b-q4_k_m': 9.3,
|
||||
// The 35B-A3B's llama.cpp figure: both have 3 billion active parameters.
|
||||
'north-mini-code-1.0:q4_k_m': 13.6,
|
||||
@@ -370,6 +383,7 @@ describe('the memory figures of the stock rows: each row has its own', () => {
|
||||
expect(Object.fromEntries(models.map((m) => [m.id, figuresOf(m)]))).toEqual({
|
||||
// State x (1 live + 8 checkpoints) + 1.0 GB + 1 GiB.
|
||||
'qwen3.5:9b-q4_k_m': [17_408, 2_547_960_320, 0],
|
||||
'ornith:9b-q4_k_m': [17_408, 2_547_960_320, 0],
|
||||
'mimo-v2.6:9b-q4_k_m': [17_408, 2_547_960_320, 0],
|
||||
// The sliding window x (1 live + 8 checkpoints) + 1.0 GB + 1 GiB.
|
||||
'north-mini-code-1.0:q4_k_m': [14_144, 3_698_117_120, 0],
|
||||
@@ -398,9 +412,9 @@ describe('the memory figures of the stock rows: each row has its own', () => {
|
||||
byFigures.set(key, [...(byFigures.get(key) ?? []), model]);
|
||||
}
|
||||
const shared = [...byFigures.values()].filter((group) => group.length > 1);
|
||||
// The two 9B rows (a fine-tune and its base) and the two 27B rows (two quantizations of one model).
|
||||
// The three 9B rows (a base and two fine-tunes of it) and the two 27B rows (two quantizations of one model).
|
||||
expect(shared.map((group) => group.map((m) => m.id))).toEqual([
|
||||
['qwen3.5:9b-q4_k_m', 'mimo-v2.6:9b-q4_k_m'],
|
||||
['qwen3.5:9b-q4_k_m', 'ornith:9b-q4_k_m', 'mimo-v2.6:9b-q4_k_m'],
|
||||
['qwen3.8:27b-q8_0', 'qwen3.8:27b-mtp-q4_k_m'],
|
||||
]);
|
||||
for (const group of shared) {
|
||||
|
||||
@@ -487,6 +487,8 @@ describe('needBytes', () => {
|
||||
it('states the need of every stock row at its largest context', () => {
|
||||
expect(Object.fromEntries(STOCK.models.map((m) => [m.id, needBytes(m, m.contexts[0])]))).toEqual({
|
||||
'qwen3.5:9b-q4_k_m': 11_380_487_246,
|
||||
// 0.92 GB under the other two 9B builds: it has no vision projector.
|
||||
'ornith:9b-q4_k_m': 10_458_772_264,
|
||||
'mimo-v2.6:9b-q4_k_m': 11_380_472_729,
|
||||
'north-mini-code-1.0:q4_k_m': 25_999_848_864,
|
||||
// At its own window of 131072 tokens: 18.2 GB + 0.9 GB of K/V cache + 2.6 GB.
|
||||
@@ -656,6 +658,7 @@ describe('the MacBook Pro (Mac17,13: Apple M5 Pro, 48 GB)', () => {
|
||||
['qwen3.6:35b-a3b-mtp-q4_k_m', 262144, 'good', 'ok', 41.8, undefined],
|
||||
['north-mini-code-1.0:q4_k_m', 262144, 'good', 'ok', 41.8, undefined],
|
||||
['mimo-v2.6:9b-q4_k_m', 131072, 'good', 'ok', 28.6, 'small'],
|
||||
['ornith:9b-q4_k_m', 131072, 'good', 'ok', 28.6, 'small'],
|
||||
['qwen3.5:9b-q4_k_m', 131072, 'good', 'ok', 28.6, 'small'],
|
||||
// The dense models compute to 2.95 x 3.07 = 9.1 tokens per second on this chip (5.2 in
|
||||
// 8 bits): slow, so they come after every fast model, in the order of their rank.
|
||||
@@ -704,6 +707,7 @@ describe('the MacBook Pro (Mac17,13: Apple M5 Pro, 48 GB)', () => {
|
||||
['muse-glimmer:30b-q4_k_m', 'unknown'],
|
||||
['north-mini-code-1.0:q4_k_m', 'unknown'],
|
||||
['mimo-v2.6:9b-q4_k_m', 'unknown'],
|
||||
['ornith:9b-q4_k_m', 'unknown'],
|
||||
['qwen3.5:9b-q4_k_m', 'unknown'],
|
||||
]);
|
||||
});
|
||||
@@ -711,34 +715,57 @@ describe('the MacBook Pro (Mac17,13: Apple M5 Pro, 48 GB)', () => {
|
||||
|
||||
describe('the Mac mini (Mac16,10: Apple M4, 16 GB)', () => {
|
||||
/**
|
||||
* Expected: verdict `none` (reason `memory`) on the estimated budget, and `limited` with
|
||||
* MiMo V2.6 9B (a GGUF build) at 65,536 tokens, a TIGHT fit, once the runtime measured its
|
||||
* budget.
|
||||
* Expected: `limited` with Ornith 1.0 9B at 65,536 tokens, a TIGHT fit, on the estimated
|
||||
* budget, and MiMo V2.6 9B at 65,536 tokens, also tight, once the runtime measured its budget.
|
||||
*
|
||||
* Why. The estimated budget is 9.67 GiB (10.4 GB: 2/3 of 16 GiB minus 1). The 9B on
|
||||
* llama.cpp needs 6.55 GB of weights + 1.14 GB for a q8_0 K/V cache of 65,536 tokens +
|
||||
* 2.55 GB beside them (its recurrent state live and in 8 context checkpoints, the runner, and
|
||||
* the 1 GiB prompt cache the runtime allows) = 10.2 GB: 0.987 of the estimate, just past the
|
||||
* 0.98 a tight fit may reach. The runtime measured 11.8 GiB on this Mac; the 6 GiB reserve
|
||||
* leaves 10 GiB (10.7 GB) of it, and the same 10.2 GB is 0.954 of that.
|
||||
* Why. The estimated budget is 9.67 GiB (10.4 GB: 2/3 of 16 GiB minus 1). A 9B on llama.cpp
|
||||
* needs its weights + 1.14 GB for a q8_0 K/V cache of 65,536 tokens + 2.55 GB beside them (its
|
||||
* recurrent state live and in 8 context checkpoints, the runner, and the 1 GiB prompt cache
|
||||
* the runtime allows). MiMo and its base are 6.55 GB (a vision projector of 0.92 GB
|
||||
* included): 10.2 GB, 0.987 of the estimate, just past the 0.98 a tight fit may reach. The
|
||||
* Ornith build has no projector, 5.63 GB: 9.3 GB, 0.898 of the estimate. The runtime measured
|
||||
* 11.8 GiB on this Mac; the 6 GiB reserve leaves 10 GiB (10.7 GB) of it, which admits all
|
||||
* three, and the rank puts MiMo first.
|
||||
*
|
||||
* Every figure of this row is computed from the model's architecture, none was measured
|
||||
* (`needSource: 'estimated'`), so this Mac is on the edge either way. Only a run of the 9B on
|
||||
* it settles which side.
|
||||
* Every figure of these rows is computed from the model's architecture, none was measured
|
||||
* (`needSource: 'estimated'`), and no 9B was ever run on such a Mac by us.
|
||||
*/
|
||||
it('gets no model on the estimated budget: the 9B needs 0.987 of it', async () => {
|
||||
it('gets Ornith 1.0 9B at 65536 tokens on the estimated budget, tight: the other 9B builds need 0.987 of it', async () => {
|
||||
const hw = await macMini();
|
||||
expect(hw.chip).toBe('Apple M4');
|
||||
expect(hw.memClassGb).toBe(16);
|
||||
|
||||
const result = recommend(hw, STOCK, ALL_INSTALLED);
|
||||
|
||||
expect(result).toMatchObject({ verdict: 'none', reason: 'memory', budgetSource: 'estimated' });
|
||||
expect(result.headline).toEqual({ text: LOCAL_MODEL_LABELS.noneMemory, vars: { memory: '16' } });
|
||||
expect(result.pick).toBeUndefined();
|
||||
expect(result).toMatchObject({ verdict: 'limited', budgetSource: 'estimated' });
|
||||
expect(result.headline).toEqual({ text: LOCAL_MODEL_LABELS.headlineLimited });
|
||||
expect(result.notes).toEqual([{ text: LOCAL_MODEL_LABELS.noteUntested }]);
|
||||
// floor(17,179,869,184 x 2/3) - 1 GiB.
|
||||
expect(result.budgetBytes).toBe(10_379_504_298);
|
||||
expect(result.budgetGb).toBe('10.4');
|
||||
expect(result.pick).toMatchObject({
|
||||
modelId: 'ornith:9b-q4_k_m',
|
||||
modelLabel: 'Ornith 1.0 9B',
|
||||
license: 'MIT',
|
||||
context: 65536,
|
||||
cli: 'opencode',
|
||||
// 5,629,110,568 + 17,408 x 65,536 + 2,547,960,320.
|
||||
needBytes: 9_317_921_576,
|
||||
needGb: '9.3',
|
||||
memory: { modelBytes: 8_177_070_888, contextBytes: 1_140_850_688, peakBytes: 0 },
|
||||
downloadGb: '5.6',
|
||||
budgetShare: 0.898,
|
||||
fit: 'tight',
|
||||
// 9.3 x 120 / 100.
|
||||
speed: 'ok',
|
||||
tokensPerSecond: 11.2,
|
||||
tested: false,
|
||||
needSource: 'estimated',
|
||||
tag: { kind: 'best-tight', label: 'Best fit, tight' },
|
||||
});
|
||||
// It is the only model that fits, and only at this context: at 131072 it needs 10.5 GB.
|
||||
expect(order(result)).toEqual(['ornith:9b-q4_k_m@65536']);
|
||||
expect(modelFitsBudget(hw, STOCK, 'ornith:9b-q4_k_m', 131072)).toBe(false);
|
||||
const nineB = stockModel('mimo-v2.6:9b-q4_k_m');
|
||||
// 6,550,811,033 + 17,408 x 65,536 + 2,547,960,320.
|
||||
expect(needBytes(nineB, 65536)).toBe(10_239_622_041);
|
||||
@@ -746,7 +773,7 @@ describe('the Mac mini (Mac16,10: Apple M4, 16 GB)', () => {
|
||||
expect(needBytes(nineB, 65536) / result.budgetBytes).toBeLessThan(0.99);
|
||||
});
|
||||
|
||||
it('would need 11.4 GB at 131072 tokens, more than the whole budget', async () => {
|
||||
it('would need 11.4 GB for MiMo at 131072 tokens, more than the whole budget', async () => {
|
||||
const hw = await macMini();
|
||||
const nineB = stockModel('mimo-v2.6:9b-q4_k_m');
|
||||
const budget = deviceBudget(hw).budgetBytes;
|
||||
@@ -756,7 +783,7 @@ describe('the Mac mini (Mac16,10: Apple M4, 16 GB)', () => {
|
||||
expect(modelFitsBudget(hw, STOCK, 'mimo-v2.6:9b-q4_k_m', 65536)).toBe(false);
|
||||
});
|
||||
|
||||
it('gets a small model with the budget its runtime measured: the 9B at 65536 tokens, tight', async () => {
|
||||
it('gets MiMo with the budget its runtime measured: at 65536 tokens, tight', async () => {
|
||||
// The runtime reported 11.8 GiB here; the 6 GiB reserve leaves 10 GiB (10.7 GB) of it.
|
||||
const result = recommend(await macMini(), STOCK, ALL_INSTALLED, { measuredTotalBytes: MACMINI_MEASURED_TOTAL });
|
||||
expect(result).toMatchObject({
|
||||
@@ -785,13 +812,20 @@ describe('the Mac mini (Mac16,10: Apple M4, 16 GB)', () => {
|
||||
needSource: 'estimated',
|
||||
tag: { kind: 'best-tight', label: 'Best fit, tight' },
|
||||
});
|
||||
// Its base model, the same figures, is the one alternative.
|
||||
expect(order(result)).toEqual(['mimo-v2.6:9b-q4_k_m@65536', 'qwen3.5:9b-q4_k_m@65536']);
|
||||
// The other two 9B builds follow by rank. The Ornith build is small enough for 131072
|
||||
// tokens here (10.5 GB, 0.974), and a model is listed at the largest context it fits at.
|
||||
expect(order(result)).toEqual([
|
||||
'mimo-v2.6:9b-q4_k_m@65536',
|
||||
'ornith:9b-q4_k_m@131072',
|
||||
'qwen3.5:9b-q4_k_m@65536',
|
||||
]);
|
||||
});
|
||||
|
||||
it('gets the same with Claude Code as its only coding agent, which also needs 65536 tokens', async () => {
|
||||
const hw = await macMini();
|
||||
expect(recommend(hw, STOCK, [fact('claude')])).toMatchObject({ verdict: 'none', reason: 'memory' });
|
||||
const estimated = recommend(hw, STOCK, [fact('claude')]);
|
||||
expect(estimated.verdict).toBe('limited');
|
||||
expect(estimated.pick).toMatchObject({ modelId: 'ornith:9b-q4_k_m', context: 65536, cli: 'claude' });
|
||||
const measured = recommend(hw, STOCK, [fact('claude')], { measuredTotalBytes: MACMINI_MEASURED_TOTAL });
|
||||
expect(measured.verdict).toBe('limited');
|
||||
expect(measured.pick).toMatchObject({ modelId: 'mimo-v2.6:9b-q4_k_m', context: 65536, cli: 'claude' });
|
||||
@@ -803,12 +837,13 @@ describe('every memory size, with the stock catalogue', () => {
|
||||
// size. A change of a catalogue row or of a threshold that moves one of these answers has to
|
||||
// change this table on purpose. docs/local-models.md has the same table for people.
|
||||
const EXPECTED: [soldGb: number, chip: string, answer: string][] = [
|
||||
// The smallest need at 65536 tokens is 10.2 GB; the budget is 2.1 GB.
|
||||
// The smallest need at 65536 tokens is 9.3 GB; the budget is 2.1 GB.
|
||||
[8, 'Apple M2', 'none memory'],
|
||||
// The 9B needs 10.2 GB at 65536 tokens, 0.987 of the 10.4 GB estimate: just out. A runtime
|
||||
// that measured the budget a real 16 GB Mac measured admits it (see the Mac mini above).
|
||||
[16, 'Apple M4', 'none memory'],
|
||||
[16, 'Apple M1', 'none memory'],
|
||||
// The 9B build without a vision projector needs 9.3 GB at 65536 tokens, 0.898 of the 10.4 GB
|
||||
// estimate. The other two need 10.2 GB, 0.987: just out, until a runtime measures what a
|
||||
// real 16 GB Mac measured (see the Mac mini above). On an M1 (68 GB/s) a 9B is slow: 6.3.
|
||||
[16, 'Apple M4', 'limited ornith:9b-q4_k_m@65536 tight ok'],
|
||||
[16, 'Apple M1', 'limited ornith:9b-q4_k_m@65536 tight slow'],
|
||||
// 11.4 GB at 131072 tokens, 0.964 of 11.8 GB, and nothing of 12 billion fits: `limited`.
|
||||
[18, 'Apple M3 Pro', 'limited mimo-v2.6:9b-q4_k_m@131072 tight ok'],
|
||||
// On a base chip with 100 GB/s the 9B computes to 9.3 tokens per second: slow.
|
||||
@@ -861,15 +896,15 @@ describe('every memory size, with the stock catalogue', () => {
|
||||
expect(result.headline).toEqual({ text: LOCAL_MODEL_LABELS.noneMemory, vars: { memory: '8' } });
|
||||
// The smallest model a harness can work with needs more than four times the budget.
|
||||
const smallest = Math.min(...STOCK.models.map((m) => needBytes(m, 65536)));
|
||||
expect(formatGb(smallest)).toBe('10.2');
|
||||
expect(formatGb(smallest)).toBe('9.3');
|
||||
expect(smallest / result.budgetBytes).toBeGreaterThan(4);
|
||||
});
|
||||
|
||||
it('gives no model to 8 and 16 GB, a small model to every Mac of 18 and 24 GB, and every Mac from 36 GB a model of 27 billion or more', () => {
|
||||
it('gives no model to 8 GB, a small model to every Mac from 16 to 24 GB, and every Mac from 36 GB a model of 27 billion or more', () => {
|
||||
for (const hw of SWEEP) {
|
||||
const result = recommend(hw, STOCK, ALL_INSTALLED);
|
||||
const label = `${hw.memClassGb} GB ${hw.chip}`;
|
||||
if (hw.memClassGb <= 16) expect(result.reason, label).toBe('memory');
|
||||
if (hw.memClassGb <= 8) expect(result.reason, label).toBe('memory');
|
||||
else if (hw.memClassGb <= 24) {
|
||||
expect(result.verdict, label).toBe('limited');
|
||||
expect(result.pick?.paramsB, label).toBe(9);
|
||||
@@ -2125,8 +2160,12 @@ describe('the measured budget, end to end', () => {
|
||||
const stepped = order(estimated).filter((entry, index) => order(measured)[index] !== entry);
|
||||
expect(stepped).toEqual(['qwen3.8:27b-q8_0@65536']);
|
||||
expect(order(measured)).toContain('qwen3.8:27b-q8_0@131072');
|
||||
expect(rows(estimated).map((c) => c.budgetShare)).toEqual([0.957, 0.767, 0.692, 0.303, 0.303, 0.853, 0.975, 0.576]);
|
||||
expect(rows(measured).map((c) => c.budgetShare)).toEqual([0.895, 0.717, 0.647, 0.283, 0.283, 0.798, 0.976, 0.539]);
|
||||
expect(rows(estimated).map((c) => c.budgetShare)).toEqual([
|
||||
0.957, 0.767, 0.692, 0.303, 0.278, 0.303, 0.853, 0.975, 0.576,
|
||||
]);
|
||||
expect(rows(measured).map((c) => c.budgetShare)).toEqual([
|
||||
0.895, 0.717, 0.647, 0.283, 0.26, 0.283, 0.798, 0.976, 0.539,
|
||||
]);
|
||||
for (const candidate of rows(measured)) {
|
||||
expect(candidate.budgetShare).toBeCloseTo(candidate.needBytes / MACBOOK_MEASURED_TOTAL, 3);
|
||||
}
|
||||
|
||||
Reference in new issue
Block a user