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rtx-5090-benchmarks

Rig: RTX 5090 32GB (capsule) · Date: 2026-06-08 · Task: local-ai-roadmap t035 Model: Mistral Small 4 (119B / 6B-active MoE, 128 experts/4 active, MXFP4_MOE GGUF) vs gpt-oss-120B (117B / 5.1B-active, MXFP4)

The point

Mistral Small 4 is a 119-billion-parameter model. The card has 32GB of VRAM. It runs anyway — and not as a party trick: ~36 tokens/sec of usable generation. That's the whole wedge of this rig — data-center-class models on one buyable card — so the question is how, and how it stacks against the only comparable thing we've run: gpt-oss-120B, the other ~120B reasoning MoE that fits this lane.

Speed, head to head

Both quantized to MXFP4, both with MoE experts streamed from system RAM (--n-cpu-moe), attention + active path on the GPU. llama-bench, RTX 5090 32GB:

	Mistral Small 4	gpt-oss-120B
total / active params	119B / 6B	117B / 5.1B
GGUF size	66.9 GB	59.0 GB
offload	`n_cpu_moe 24`	`n_cpu_moe 20`
VRAM used	27.5 GB	~30 GB
decode (tg)	35.95 tok/s	46.48 tok/s
prefill peak (pp)	313 tok/s	588 tok/s

gpt-oss is the faster of the two by ~30% on decode and nearly 2x on prefill. It isn't a better engine — it's a leaner one: 8GB smaller, ~1B fewer active params, and two fewer layers' worth of experts offloaded. Every one of those differences pushes the same direction.

The mechanism — why a 119B model decodes at a usable rate at all

Decode is memory-bound, and on an offloaded MoE the bound is the active path, not the total weight. Only 6B of the 119B fire per token, so each token reads a small slice of expert weights from RAM, not the whole model. That's why ~36 tok/s is achievable despite 40GB of the model living in system RAM. The cost is borne on prefill (313 vs gpt-oss's 588 tok/s) and on absolute decode (36 vs 46) — both tracking Mistral's larger size and heavier offload. Bigger model, more offload, slower — but the active-params trick keeps it the right side of usable.

Feasibility notes (for anyone trying this on Blackwell)

Arch mistral4 loads on llama.cpp b9365 — no rebuild needed (it was an open question; March-2026 arch).
There is no NVFP4 GGUF. The official NVFP4 checkpoint is vLLM/compressed-tensors only, which is walled on this toolkit-less box (the t036 wall). The runnable path is the MXFP4_MOE GGUF (unsloth) — which, bonus, makes the gpt-oss head-to-head a clean same-quant comparison.
Fit: n_cpu_moe 24, ~27.5GB VRAM, ~46GB RAM, this build's auto-fit ("fitting params to device memory") handles the split. Donald (the resident agent) must vacate the GPU for the run.

Why no quality leaderboard entry (the honest part)

This was meant to be a Thinking-ON leaderboard entry. It isn't — and the reason is a real lesson. The rig's think toggle is Qwen-shaped: it engages reasoning by omitting /nothink and relies on the model thinking by default. Mistral Small 4 doesn't — it gates reasoning behind a reasoning_effort dial that defaults to none. So the model ran in near-non-reasoning mode (~5 sec/question, ~110-190 tokens — far too short for real chain-of-thought), and its scores would be a no-think measurement mislabeled as think-ON. Invalid; dropped.

Fixing it is a small harness change (send reasoning_effort: high when think is on). But a properly reasoning run is slow on this offloaded box — real chain-of-thought at 36 tok/s makes even a 6%-sampled MMLU a multi-hour job. So a valid Thinking-ON quality entry is deferred to a dedicated run; this treatment ships the speed and feasibility finding, which is the part that's solid.

Speed data: results/mistral-small-4-119b-2603-mxfp4-moe/speed.json. Harness: the rig's standard bench.py speed sweep with the per-model offload override (offload: in config). gpt-oss-120B figures from its prior treatment.