Ministral 3 8B Instruct
8BMistral Ministral 3
Mistral ships the GGUF itself, in 3B / 8B / 14B and Instruct or Reasoning variants — the 8B is the one that fits an 8GB card at Q4 with room for real context. Plain full attention on all 34 layers, so the memory estimates below behave the way the calculator assumes. Quality loss per level has not been published.
262K
Max Context
3
Quant Variants
GGUF Q8_0
Best Quality
—
Accuracy Retained
Quantization Variants
Per-quant VRAM, quality loss, and inference speed on RTX 4090
Measured = run on this site · Estimated = not run here: calculated, or a figure this site has not verified · Community = public reports
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Running Ministral 3 8B Instruct locally
At Q4_K_M and 4K of context, Ministral 3 8B Instruct needs about 6.0 GB — 4.9 GB of weights, 0.53 GB of KV cache and a 0.6 GB activation buffer. The smallest cards in this index that clear that comfortably are 8 GB ones — 9 of them, from the RTX 4060 up — and 76 of the 78 cards here do. These are calculated figures, not measurements: the estimate stops counting a card as comfortable at 88% of its VRAM, which is roughly the room a desktop session needs.
What longer context costs
Going from 4K to 32K adds about 3.72 GB, taking the total to 10.1 GB. Weights do not move with context — only the KV cache does, and it grows linearly, so this is the number to watch when planning for long documents. The model's native window is 256K; holding all of it at Q4_K_M would need about 43 GB.
Which build to download
This index only tracks Ministral 3 8B Instruct in GGUF, across 3 levels (Q4_K_M, Q5_K_M, Q8_0). No per-level perplexity sweep has been published for this model, so the quality column is empty rather than estimated — within one model, more bits per weight is the only ordering the data supports. No throughput has been measured for this model on this site's hardware. GGUF runs on llama.cpp and Ollama across NVIDIA, AMD and Apple silicon; AWQ and GPTQ target vLLM on CUDA and ROCm; EXL2 is ExLlamaV2 — CUDA only, and now archived, so local use rather than serving.
Common questions
- How much VRAM does Ministral 3 8B Instruct need?
- About 6.0 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 10.1 GB at 32K. That figure is weights plus KV cache plus a 10% activation buffer, calculated from the model's architecture rather than measured on a card.
- Will Ministral 3 8B Instruct run on a 8GB GPU?
- Yes — at Q4_K_M and 4K context it needs about 6.0 GB, which leaves 2.0 GB spare on a RTX 4060, or on any of the other 8 GB cards here (9 in all). That is the smallest size in this index that clears it comfortably; 76 of 78 do.
- Which quantization of Ministral 3 8B Instruct should I use?
- No published quality comparison exists for this model, so pick by footprint: Q4_K_M is the level most people run, and a higher bits-per-weight level is more faithful. All 3 levels in the index are Q4_K_M, Q5_K_M, Q8_0.
Where this model fits
Sized at Q4_K_M with a 4K context window, smallest card first. Comfortable means the estimate uses at most 88% of the memory.
- Runs comfortably on
- RTX 40608GB · +2.0RTX 4060 Ti 8G8GB · +2.0Radeon RX 9060 XT 8G8GB · +2.0RTX 5060 Ti 8G8GB · +2.0RTX 50608GB · +2.0
- Tight but possible
- Mac M1 8G8GBMac M3 8G8GB
- Ships in
- GGUF
- Try it yourself
- Size it in the calculatorGenerate the run command
How to actually run this
Deployment guides for this model and this class of hardware.
This page's figures change when the model or the runtime does.Last updated 2026-10-20 RSS → /feed.xml