readygpu

Magistral Small 2509

24B parameters · 128k context · Apache License 2.0 · released 2025-09-12

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In short

Magistral Small 2509 is an open model from Mistral AI with 24B parameters. At Q4_K_M, Magistral Small 2509 needs about 15 GB of memory at 8k context and 15 GB at 8k, counting the weights, the KV cache and runtime buffers. The cheapest hardware in the US that runs Magistral Small 2509 fully in memory at 8k context is the NVIDIA GeForce RTX 5060 Ti 16GB, $618 at eBay, at about 18 tok/s (estimated).

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

What to run it on in the US

Fully in memory at 8k 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 P40Q5_K_Mfits12 tok/s$34534.2
NVIDIA GeForce RTX 5060 Ti 16GBQ4_K_Mfits18 tok/s$61828.6
Intel Arc Pro B60Q5_K_Mfits13 tok/s$65019.4
NVIDIA GeForce RTX 4060 Ti 16GBQ4_K_Mfits11 tok/s$77014.8
AMD Radeon RX 7900 XTQ5_K_Mfits24 tok/s$80030.4
NVIDIA GeForce RTX 4070 Ti SuperQ4_K_Mfits27 tok/s$98427.0
NVIDIA GeForce RTX 5070 TiQ4_K_Mfits35 tok/s$99535.6
NVIDIA GeForce RTX 4080 SuperQ4_K_Mfits29 tok/s$1,19924.3
Mac mini M4 32GBQ5_K_Mfits4 tok/s$1,4203.0
AMD Radeon RX 7900 XTXQ5_K_Mfits29 tok/s$1,53319.0
NVIDIA GeForce RTX 3090Q5_K_Mfits32 tok/s$1,60020.1
NVIDIA GeForce RTX 5080Q4_K_Mfits38 tok/s$1,60023.8
AMD Radeon AI PRO R9700Q8_0fits13 tok/s$1,7007.8
NVIDIA GeForce RTX 3090 TiQ5_K_Mfits35 tok/s$2,06016.7
NVIDIA GeForce RTX 4090Q5_K_Mfits35 tok/s$2,07316.6
Mac mini M4 Pro 48GBQ8_0fits7 tok/s$2,1493.1
Mac mini M4 Pro 64GBQ8_0fits7 tok/s$2,9002.3
Mac Studio M4 Max 64GBQ8_0fits13 tok/s$3,4003.9
Framework Desktop Ryzen AI Max+ 395 128GBQ8_0fits5 tok/s$3,4491.5
GMKtec EVO-X2 Ryzen AI Max+ 395 128GBQ8_0fits5 tok/s$3,6501.5
Mac Studio M3 Ultra 96GBQ8_0fits20 tok/s$4,3994.6
NVIDIA GeForce RTX 5090Q8_0fits42 tok/s$4,9218.6
Mac Studio M4 Max 128GBQ8_0fits13 tok/s$4,9992.7
ASUS Ascent GX10 128GBQ8_0fits6 tok/s$6,1001.0
NVIDIA DGX Spark 128GBQ8_0fits6 tok/s$7,3990.9
NVIDIA RTX PRO 6000 BlackwellQ8_0fits42 tok/s$7,9975.3
Mac Studio M3 Ultra 256GBQ8_0fits20 tok/s$8,4992.4
Mac Studio M3 Ultra 512GBQ8_0fits20 tok/s$23,0000.9
Framework Desktop Ryzen AI Max+ 395 64GBQ8_0fits5 tok/s––
Intel Arc B580Q4_K_Moffload8 tok/s$34621.7
NVIDIA GeForce RTX 3060 12GBQ4_K_Moffload8 tok/s$36021.4
Intel Arc A770 16GBQ4_K_Moffload17 tok/s$40041.8
NVIDIA GeForce RTX 3080 10GBQ4_K_Moffload7 tok/s$46915.3
AMD Radeon RX 7600 XTQ4_K_Moffload10 tok/s$48020.4
AMD Radeon RX 9060 XT 16GBQ4_K_Moffload11 tok/s$52020.8
AMD Radeon RX 7800 XTQ4_K_Moffload20 tok/s$55036.5
NVIDIA GeForce RTX 4070 SuperQ4_K_Moffload9 tok/s$69912.4
NVIDIA GeForce RTX 5070Q4_K_Moffload9 tok/s$75012.5
AMD Radeon RX 9070 XTQ4_K_Moffload21 tok/s$81525.2

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_Mgguf13.3 GB15.3 GB19.2 GB24.4 GB34.8 GB
Q5_K_Mgguf15.6 GB17.6 GB21.5 GB26.7 GB37.1 GB
Q8_0gguf23.3 GB25.3 GB29.2 GB34.4 GB44.8 GB

Run it

Copy a command for the runtime you use.

  • Q4_K_M
    • Ollamaollama run hf.co/mistralai/Magistral-Small-2509-GGUF:Q4_K_M
    • LM Studiolms get mistralai/Magistral-Small-2509-GGUF@q4_k_m
    • llama.cppllama-server -hf mistralai/Magistral-Small-2509-GGUF:Q4_K_M
  • Q5_K_M
    • Ollamaollama run hf.co/mistralai/Magistral-Small-2509-GGUF:Q5_K_M
    • LM Studiolms get mistralai/Magistral-Small-2509-GGUF@q5_k_m
    • llama.cppllama-server -hf mistralai/Magistral-Small-2509-GGUF:Q5_K_M
  • Q8_0
    • Ollamaollama run hf.co/mistralai/Magistral-Small-2509-GGUF:Q8_0
    • LM Studiolms get mistralai/Magistral-Small-2509-GGUF@q8_0
    • llama.cppllama-server -hf mistralai/Magistral-Small-2509-GGUF:Q8_0

Notes

The card says performance might degrade past 40k tokens of context, even though the window is 128k. It recommends vLLM with mistral_common >= 1.8.5, and [THINK]/[/THINK] must be encoded as special tokens.

Questions

How much VRAM does Magistral Small 2509 need?
At Q4_K_M, Magistral Small 2509 needs about 15 GB of memory at 8k context and 15 GB at 8k, counting the weights, the KV cache and runtime buffers.
What is the best GPU for running Magistral Small 2509 locally?
The cheapest hardware in the US that runs Magistral Small 2509 fully in memory at 8k context is the NVIDIA GeForce RTX 5060 Ti 16GB, $618 at eBay, at about 18 tok/s (estimated). The best value is the NVIDIA GeForce RTX 5070 Ti (about 35 tok/s for $995). The fastest we price is the NVIDIA GeForce RTX 5090, at about 42 tok/s.
Can I run Magistral Small 2509 on a 16 GB GPU?
Yes: at Q4_K_M it fits fully in 16 GB at 8k context. On the NVIDIA GeForce RTX 5080 it runs at about 38 tok/s (estimated).
Can I run Magistral Small 2509 on a 24 GB GPU?
Yes: at Q5_K_M it fits fully in 24 GB at 8k context. On the NVIDIA GeForce RTX 3090 Ti it runs at about 35 tok/s (estimated).
How do I run Magistral Small 2509 locally?
With Ollama: ollama run hf.co/mistralai/Magistral-Small-2509-GGUF:Q4_K_M (the Q4_K_M build). LM Studio and llama.cpp commands are on this page.
What licence is Magistral Small 2509 released under?
Apache License 2.0, which allows commercial use.