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Ministral 3 8B Reasoning 2512

8.9B parameters · 256k context · Apache 2.0 · released 2025-10-31

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In short

Ministral 3 8B Reasoning 2512 is an open model from Mistral AI with 8.9B parameters. At Q4_K_M, Ministral 3 8B Reasoning 2512 needs about 7 GB of memory at 8k context and 14 GB at 64k, counting the weights, the KV cache and runtime buffers. The cheapest hardware in the UK that runs Ministral 3 8B Reasoning 2512 fully in memory at 64k context is the NVIDIA GeForce RTX 5060 Ti 16GB, £550 at eBay, at about 18 tok/s (estimated).

Updated . Speeds are estimates; how they are worked out.

What to run it on in the UK

Fully in memory at 64k context, priced at retailers today.

All hardware that runs it

Everything we price, best value first. Two cards split the model by layers.

HardwareQuantFitsDecodePricetok/s per 1,000
NVIDIA Tesla P40Q8_0fits12 tok/s£26145.3
AMD Radeon RX 7600 XTQ4_K_Mfits11 tok/s£34831.9
AMD Radeon RX 9060 XT 16GBQ4_K_Mfits12 tok/s£49924.7
NVIDIA GeForce RTX 5060 Ti 16GBQ5_K_Mfits18 tok/s£55033.3
AMD Radeon RX 7800 XTQ4_K_Mfits24 tok/s£56842.3
Intel Arc A770 16GBQ4_K_Mfits20 tok/s£58833.2
AMD Radeon RX 9070 XTQ4_K_Mfits25 tok/s£67036.7
Intel Arc Pro B60Q8_0fits13 tok/s£74217.0
AMD Radeon RX 7900 XTQ8_0fits24 tok/s£78930.7
NVIDIA GeForce RTX 4070 Ti SuperQ5_K_Mfits27 tok/s£95028.8
NVIDIA GeForce RTX 5070 TiQ5_K_Mfits37 tok/s£97537.5
NVIDIA GeForce RTX 4060 Ti 16GBQ5_K_Mfits12 tok/s£97712.1
AMD Radeon RX 7900 XTXQ8_0fits29 tok/s£99929.1
NVIDIA GeForce RTX 5080Q5_K_Mfits39 tok/s£1,19932.7
Mac mini M4 32GBQ8_0fits4 tok/s£1,2003.6
NVIDIA GeForce RTX 4080 SuperQ5_K_Mfits30 tok/s£1,42921.0
AMD Radeon AI PRO R9700Q8_0fits19 tok/s£1,67411.6
NVIDIA GeForce RTX 3090Q8_0fits32 tok/s£1,70718.7
NVIDIA GeForce RTX 3090 TiQ8_0fits34 tok/s£2,19915.6
Mac mini M4 Pro 48GBQ8_0fits10 tok/s£3,0003.3
GMKtec EVO-X2 Ryzen AI Max+ 395 128GBQ8_0fits8 tok/s£3,3202.3
Mac Studio M4 Max 64GBQ8_0fits20 tok/s£3,3995.7
NVIDIA GeForce RTX 5090Q8_0fits61 tok/s£5,00012.2
Mac Studio M3 Ultra 96GBQ8_0fits29 tok/s£5,4995.3
NVIDIA GeForce RTX 4090Q8_0fits34 tok/s£6,0155.7
Mac Studio M3 Ultra 256GBQ8_0fits29 tok/s£8,4993.4
Mac Studio M3 Ultra 512GBQ8_0fits29 tok/s£17,4951.7
NVIDIA RTX PRO 6000 BlackwellQ8_0fits61 tok/s£29,5042.1
Mac Studio M4 Max 128GBQ8_0fits20 tok/s––
Mac mini M4 Pro 64GBQ8_0fits10 tok/s––
Framework Desktop Ryzen AI Max+ 395 128GBQ8_0fits8 tok/s––
NVIDIA DGX Spark 128GBQ8_0fits9 tok/s––
ASUS Ascent GX10 128GBQ8_0fits9 tok/s––
Framework Desktop Ryzen AI Max+ 395 64GBQ8_0fits8 tok/s––
Intel Arc B580Q4_K_Moffload9 tok/s£34925.2
NVIDIA GeForce RTX 3060 12GBQ4_K_Moffload9 tok/s£39023.3
NVIDIA GeForce RTX 3080 10GBQ4_K_Moffload8 tok/s£46618.0
NVIDIA GeForce RTX 5070Q4_K_Moffload12 tok/s£69516.5
NVIDIA GeForce RTX 4070 SuperQ4_K_Moffload10 tok/s£87411.9

1 other card or machine we price cannot hold it at this context.

Memory needed

Weights, KV cache and runtime buffers at each context length (KV cache at fp16).

QuantFile@8k@32k@64k@128k
Q4_K_Mgguf4.8 GB6.7 GB10.0 GB14.4 GB23.3 GB
Q5_K_Mgguf5.6 GB7.5 GB10.8 GB15.2 GB24.1 GB
Q8_0gguf8.4 GB10.2 GB13.5 GB18.0 GB26.9 GB

Run it

Copy a command for the runtime you use.

  • Q4_K_M
    • Ollamaollama run hf.co/mistralai/Ministral-3-8B-Reasoning-2512-GGUF:Q4_K_M
    • LM Studiolms get mistralai/Ministral-3-8B-Reasoning-2512-GGUF@q4_k_m
    • llama.cppllama-server -hf mistralai/Ministral-3-8B-Reasoning-2512-GGUF:Q4_K_M
  • Q5_K_M
    • Ollamaollama run hf.co/mistralai/Ministral-3-8B-Reasoning-2512-GGUF:Q5_K_M
    • LM Studiolms get mistralai/Ministral-3-8B-Reasoning-2512-GGUF@q5_k_m
    • llama.cppllama-server -hf mistralai/Ministral-3-8B-Reasoning-2512-GGUF:Q5_K_M
  • Q8_0
    • Ollamaollama run hf.co/ggml-org/Ministral-3-8B-Reasoning-2512-GGUF:Q8_0
    • LM Studiolms get ggml-org/Ministral-3-8B-Reasoning-2512-GGUF@q8_0
    • llama.cppllama-server -hf ggml-org/Ministral-3-8B-Reasoning-2512-GGUF:Q8_0

Notes

Needs vllm >= 0.12.0 (with mistral_common >= 1.8.6) or Transformers 5.0.0rc0. Fits in 24GB of VRAM in BF16, or under 12GB when quantized.

Questions

How much VRAM does Ministral 3 8B Reasoning 2512 need?
At Q4_K_M, Ministral 3 8B Reasoning 2512 needs about 7 GB of memory at 8k context and 14 GB at 64k, counting the weights, the KV cache and runtime buffers.
What is the best GPU for running Ministral 3 8B Reasoning 2512 locally?
The cheapest hardware in the UK that runs Ministral 3 8B Reasoning 2512 fully in memory at 64k context is the NVIDIA GeForce RTX 5060 Ti 16GB, £550 at eBay, at about 18 tok/s (estimated). The best value is the AMD Radeon RX 7800 XT (about 24 tok/s for £568). The fastest we price is the NVIDIA GeForce RTX 5090, at about 61 tok/s.
Can I run Ministral 3 8B Reasoning 2512 on a 16 GB GPU?
Yes: at Q5_K_M it fits fully in 16 GB at 64k context. On the NVIDIA GeForce RTX 5080 it runs at about 39 tok/s (estimated).
Can I run Ministral 3 8B Reasoning 2512 on a 24 GB GPU?
Yes: at Q8_0 it fits fully in 24 GB at 64k context. On the NVIDIA GeForce RTX 3090 Ti it runs at about 34 tok/s (estimated).
How do I run Ministral 3 8B Reasoning 2512 locally?
With Ollama: ollama run hf.co/mistralai/Ministral-3-8B-Reasoning-2512-GGUF:Q4_K_M (the Q4_K_M build). LM Studio and llama.cpp commands are on this page.
What licence is Ministral 3 8B Reasoning 2512 released under?
Apache 2.0, which allows commercial use.