Yes — with 39.7 GB to spare

Ministral 3 8B at Q4_K_M fits your RTX 6000 Ada entirely on the GPU at 8K context, at an estimated 116 tokens per second. There is room for its full 256K window.

Fully on GPU 8K context Q4_K_M · 5.0 GB Apache 2.0 Released Dec 2025 Vision

Mistral's 8B with images in. Plain GQA, so budget more KV cache than Qwen3.5 9B at the same context.

What hardware do I need for Ministral 3 8B? →

The VRAM budget

weights 5.0 GB

Weights 5.0 GB KV cache @ 8K 1.06 GB Runtime overhead 0.6 GB Free 39.7 GB of 46.4 GB

Quantisation ladder

Quant	Weights	Total @ 8K	Max context	Tok/s	Quality	Fit
F16	16.6 GB	18.3 GB	219K	35	Reference	Long context
Q8_0	8.8 GB	10.5 GB	256K	66	−0.1% ppl	Long context
Q6_K	6.8 GB	8.5 GB	256K	85	−0.4% ppl	Long context
Q5_K_M	5.9 GB	7.6 GB	256K	99	−0.8% ppl	Long context
Q4_K_M	5.0 GB	6.7 GB	256K	116	−1.9% ppl	Recommended
Q3_K_M	4.1 GB	5.7 GB	256K	143	−5.4% ppl	Long context
Q2_K	3.5 GB	5.1 GB	256K	167	−15% ppl	Long context

Quality is the published perplexity delta against f16 weights. Max context assumes an f16 KV cache; q8_0 roughly doubles it.

How to run it

Ollama llama.cpp LM Studio vLLM

terminal

$ ollama pull ministral-3:8b
$ OLLAMA_CONTEXT_LENGTH=8192 \
    ollama run ministral-3:8b

The default. One binary, a model registry, an OpenAI-compatible port. More on Ollama.

01Download is 5.0 GB. Keep it on an SSD — a first load off a spinning disk takes minutes.

02Close anything else holding VRAM. A browser with hardware acceleration can sit on 1–2 GB.

03There is room to go to the model's full 256K context on this card.

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