Ministral 3 8B — VRAM, Speed & Local Setup

作者： Jakub Rusinowski · 最后更新： 2026年9月6日

Model library → Ministral 3 → Ministral 3 8B

The balanced middle of the Ministral 3 range at 5.6 GB Q4_K_M — comfortable on an 8 GB card and the natural replacement for a Llama 3.1 8B install. Vision-capable, 128K context, Apache 2.0.

Ministral 3 8B needs about 6 GB of VRAM at Q4_K_M — quantized weights plus framework overhead, before any KV cache. On Apple Silicon that figure comes out of unified memory.

Specifications

Parameters8 Billion
Context window131,072
ArchitectureDense Transformer (vision)
ProviderMistral AI
LicenceApache 2.0
Specified atQ4_K_M
System RAM16 GB
Record updated2026-09-06

Licence

Apache-2.0 — commercial use permitted. Commercial use permitted. No usage restrictions beyond attribution.

VRAM and Speed by Quantization

Modelled on a reference NVIDIA RTX 4090 (24 GB), with no KV cache (this record has no published architecture). Speed figures are ESTIMATES from the memory-bandwidth roofline described on the methodology page, not benchmarks we ran — rows marked measured come from published or reader-submitted runs. VRAM here includes the KV cache, so it reads higher than the headline figure above, which does not.

QuantWeightsVRAM neededEst. speedFit on 24 GB
Q2_K2.6 GB3.4 GB~154 tok/s (est.)Fits comfortably
Q3_K_M3.4 GB4.2 GB~133 tok/s (est.)Fits comfortably
Q4_K_M4.8 GB5.6 GB~107 tok/s (est.)Fits comfortably
Q5_K_M5.7 GB6.5 GB~95 tok/s (est.)Fits comfortably
Q6_K6.6 GB7.4 GB~86 tok/s (est.)Fits comfortably
Q8_08.5 GB9.3 GB~70 tok/s (est.)Fits comfortably
F1616.0 GB16.8 GB~41 tok/s (est.)Fits comfortably

Want the memory numbers alone, at every quantization level and your own context length? Use the Ministral 3 8B VRAM calculator.

Buy This HardwareIntel Arc B570 10GB — 10 GB VRAM · 150 W board powerDeploy in the Cloud NowRTX 4090 on RunPod — from $0.34/hr · rate checked 2026-07

or compare on Vast.ai from $0.35/hr (typical low · varies)

As an Amazon Associate we earn from qualifying purchases. Cloud GPU links are referral links — we may earn a commission at no extra cost to you.

Recommended GPU

The cheapest catalogued GPU that runs Ministral 3 8B is the Intel Arc B570 (10 GB).

联盟营销声明: 本页部分链接为联盟推广链接——如果你通过它们购买，LLM Configurator 可能会获得佣金，而你无需支付任何额外费用。作为亚马逊联盟成员（Amazon Associate），LLM Configurator 会从符合条件的购买中获得收益。
Intel Arc B570 10GB
10 GB VRAM · 150 W board power
2026年价格波动较大——请以当前商品页价格为准。
在亚马逊查看价格

How to Run Ministral 3 8B

Install Ollama, then run:

ollama run ministral-3:8b

Weights on Hugging Face: mistralai/Ministral-3-8B-Instruct.

Best for: general purpose, edge devices, multimodal, chat.

Can I Run Ministral 3 8B on My GPU?

Other Ministral 3 Sizes

Ministral 3 8B — Frequently Asked Questions

How much VRAM does Ministral 3 8B need?
About 6 GB at Q4_K_M — quantized weights plus framework overhead, before any KV cache. The cache grows with context length and is added on top; the table above folds it in. Apple Silicon counts unified memory toward the same figure.
Does Ministral 3 8B run on an RTX 4090 (24 GB)?
Yes. Ministral 3 8B needs about 6 GB at Q4_K_M, inside a 24 GB card, at an estimated 107 tokens/sec.
How do I run Ministral 3 8B locally?
Install Ollama and run `ollama run ministral-3:8b`. That pulls the weights and starts a local OpenAI-compatible endpoint; after the download nothing leaves the machine.
What other sizes does Ministral 3 come in?
Ministral 3 3B (3 GB), Ministral 3 8B (6 GB), Ministral 3 14B (9 GB). Every size shares the family's training and licence; the larger ones score higher and need proportionally more memory.

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