primitive-ai/Laguna-S-2.1-mixed-NVFP4-MXFP8

219 GB of weights, served from one GPU, at the fastest throughput of the 4-bit field.

Mixed-precision quantization of poolside/Laguna-S-2.1 to 64 GiB
at 4.67 bits per weight, 3.4× smaller than BF16, on stock vLLM,
joint-smallest of the 4-bit field and the fastest of it at concurrency 32.

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Why this quant

• 🖥️ Runs on one 96 GiB GPU. BF16 needs a multi-GPU host, and poolside's own INT4/NVFP4 releases are now published at 92.9 GB, which no longer loads on a 95 GiB card.
• 📦 Joint-smallest of the 4-bit field at 64.0 GiB, level with olka-fi's MXFP4 and 3–8 GiB under the NVFP4 and AWQ builds.
• ⚡ The fastest at concurrency 32. 670 tok/s against 626–663 for the other three builds, measured back to back in one sitting.
• 🎯 Accuracy mid-field in a narrow field. 84.3 pooled over both suites against 85.6 for the AWQ build and 83.5 for the NVFP4 one.
• 🔧 No calibration data. Weights-only round-to-nearest, so the conversion is data-free and reproducible.

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Serve it

vllm serve primitive-ai/Laguna-S-2.1-mixed-NVFP4-MXFP8

Sized for one 96 GiB card at --gpu-memory-utilization 0.90. Give it a generous --max-model-len: this model reasons at length and a short cap will cut answers off mid-thought. No sampling workarounds are needed. See the revision note at the bottom if you pulled this repo before 19 Aug 2026.

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Measured

1,370 items across fourteen public benchmarks. A 1,170-item knowledge suite and a 200-item tool-calling suite, under one fixed protocol for every row: temperature 0.6 / top_p 0.95 / top_k 20, thinking forced on, a 16,384-token budget, concurrency 32, all rows back to back in one sitting on one RTX PRO 6000 Blackwell.

overall is one number over both suites: the 1,170 knowledge and 200 tool-calling items pooled as 1,370, weighted 85.4% and 14.6% by item count. Repeat runs of one checkpoint moved it by about a point, so treat a gap below 1.0 as a tie.

Where the tool-calling half goes: call vs abstain

call is accuracy on the 160 rows that require a call; abstain is the 40 whose correct action is to call nothing. Benchmarks: BFCL v4 (30, including irrelevance), xLAM/APIGen (45), ToolACE (45), Glaive v2 (40), nvidia When2Call (40, the abstention rows). Tool schemas go in the system prompt and the model answers with a JSON array of calls, the same way for every model. An item counts only if every expected call is present with acceptable arguments and no call is invented.

Abstention is the weak axis for every model we have measured (52–82%), so a build can look strong on overall and still over-call.

The last row is a drift control: the same checkpoint re-measured after four hours of other work came back within 0.5% on throughput and 0.2 points on the knowledge half. The agentic half was not re-run on this model, which is why that row carries no overall.

Thinking is forced on for every row here, and it has to be. olka-fi's checkpoint ships a chat template that defaults enable_thinking to false; left at its default it answers in 198 tokens and posts a score that describes a different product, not a faster one. Every row in this table reasons because the protocol makes it, not because the templates agree.

How to read this. The AWQ build is 1.3 points ahead on overall, at the edge of this suite's ±1-point spread, and it costs 8 GiB more memory, 6% less throughput at concurrency 32, and 30% more tokens for every answer. This build takes the size, the aggregate throughput and the token bill. If a point of accuracy is worth more to you than any of those, the AWQ build is the better choice; no single build here wins every column.

Tool calling is the whole field's weak spot on this model, ours included. Every build lands between 67.5 and 71.0 on the 200 tool-calling items, and every one of them declines in prose rather than returning an empty call list, which is correct behaviour and scored as such here. If tool calling is your workload, this is a model-level limit rather than a quantization one.

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Comparable with our other models

Accuracy numbers move for reasons that have nothing to do with the model: a shorter token budget, a different temperature, or whether the model was allowed to reason at all. So every number in this table, on this card and on our other cards, comes from one fixed protocol.

The same 1,370 items: a 1,170-item knowledge suite (MMLU-Pro, ARC-Challenge, HellaSwag, WinoGrande, CommonsenseQA, BoolQ, OpenBookQA, GSM8K, MATH-500) and a 200-item tool-calling suite (BFCL v4, xLAM/APIGen, ToolACE, Glaive v2, nvidia When2Call). temperature 0.6, top_p 0.95, top_k 20, thinking forced on, a 16,384-token budget, no reasoning parser, scoring the last ANSWER: in the reply. Concurrency 32 on one RTX PRO 6000 Blackwell, each model's rows in one sitting. Auto-scored, no LLM judge. Both halves are means of at least three runs per build.

Read overall with finished. overall scores an answer that overran the token budget as wrong, but it cannot say whether the model needed the room or failed to stop; finished and out separate those. A gap under 1.0 is a tie. The tok/s column comes from each model's own sitting and drifts a few percent between sittings, so read it as a bracket.

call and abstain are the tool-calling suite's two halves, reported separately. call is accuracy on the 160 items that require a tool call; abstain is the 40 whose correct action is to call nothing. They used to be pooled into one agentic number, and the pooling misled: a model with ordinary call accuracy and unusual abstention discipline outscored models that are better at actually making calls. Weight them by your own workload's mix.

Per-benchmark detail, both halves

What's quantized to what

Weights-only round-to-nearest, no calibration data required or embedded.

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Revision note: if you pulled this repo before 19 Aug 2026

The first published revision had a packaging bug and you should re-pull. vLLM fuses each expert's gate_proj and up_proj into one tensor, which carries exactly one NVFP4 global scale; that revision wrote the two halves' scales independently, so 11,815 of 12,032 expert pairs disagreed, the worst by a factor of 6.9, and one half of most experts was served mis-scaled. Every tensor was correct on disk, so it loaded and mostly worked. The visible symptom was a failure to stop, leaving 21.5% of answers unfinished at a 16k budget.

The current revision fixes it, at the same size and the same format map:

If you worked around it with repetition_penalty, you no longer need to.

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