Devstral Small 2507
23.6B parameters · 128k context · Apache 2.0 · released 2025-07-04
codingagentic
In short
Devstral Small 2507 is an open model from Mistral AI with 23.6B parameters. At Q4_K_M, Devstral Small 2507 needs about 15 GB of memory at 8k context and 24 GB at 64k, counting the weights, the KV cache and runtime buffers. The cheapest hardware in Germany that runs Devstral Small 2507 fully in memory at 64k context is the NVIDIA GeForce RTX 5090, 6.028 € at Amazon, at about 40 tok/s (estimated).
Updated . Speeds are estimates; how they are worked out.
What to run it on in Germany
Fully in memory at 64k context, priced at retailers today.
Cheapest
NVIDIA GeForce RTX 509040tok/sest.Q5_K_M
6.028 €
Best value
NVIDIA GeForce RTX 509040tok/sest.Q5_K_M
6.028 €
Fastest
NVIDIA GeForce RTX 509040tok/sest.Q5_K_M
6.028 €
All hardware that runs it
Everything we price, best value first. Two cards split the model by layers.
| Hardware | Quant | Fits | Decode | Price | tok/s per 1,000 |
|---|---|---|---|---|---|
| AMD Radeon AI PRO R9700 | Q5_K_M | fits | 13 tok/s | 1.867 € | 6.9 |
| Mac mini M4 Pro 48GB | Q8_0 | fits | 5 tok/s | 2.699 € | 1.9 |
| Mac mini M4 Pro 64GB | Q8_0 | fits | 5 tok/s | 2.799 € | 1.8 |
| GMKtec EVO-X2 Ryzen AI Max+ 395 128GB | Q8_0 | fits | 4 tok/s | 3.700 € | 1.1 |
| Mac Studio M4 Max 64GB | Q8_0 | fits | 10 tok/s | 4.183 € | 2.4 |
| Mac Studio M4 Max 128GB | Q8_0 | fits | 10 tok/s | 5.800 € | 1.7 |
| Mac Studio M3 Ultra 96GB | Q8_0 | fits | 15 tok/s | 5.999 € | 2.5 |
| NVIDIA GeForce RTX 5090 | Q5_K_M | fits | 40 tok/s | 6.028 € | 6.7 |
| NVIDIA DGX Spark 128GB | Q8_0 | fits | 5 tok/s | 8.313 € | 0.6 |
| ASUS Ascent GX10 128GB | Q8_0 | fits | 5 tok/s | 8.623 € | 0.5 |
| Mac Studio M3 Ultra 256GB | Q8_0 | fits | 15 tok/s | 15.555 € | 1.0 |
| Mac Studio M3 Ultra 512GB | Q8_0 | fits | 15 tok/s | 18.995 € | 0.8 |
| NVIDIA RTX PRO 6000 Blackwell | Q8_0 | fits | 31 tok/s | 19.076 € | 1.6 |
| Framework Desktop Ryzen AI Max+ 395 128GB | Q8_0 | fits | 4 tok/s | – | – |
| Framework Desktop Ryzen AI Max+ 395 64GB | Q8_0 | fits | 4 tok/s | – | – |
| NVIDIA GeForce RTX 3060 12GB | Q4_K_M | offload | 3 tok/s | 309 € | 10.4 |
| Intel Arc B580 | Q4_K_M | offload | 3 tok/s | 360 € | 8.9 |
| NVIDIA Tesla P40 | Q4_K_M | offload | 8 tok/s | 369 € | 20.6 |
| NVIDIA GeForce RTX 3080 10GB | Q4_K_M | offload | 3 tok/s | 500 € | 6.2 |
| AMD Radeon RX 9060 XT 16GB | Q4_K_M | offload | 4 tok/s | 550 € | 6.7 |
| Intel Arc A770 16GB | Q4_K_M | offload | 4 tok/s | 646 € | 6.5 |
| NVIDIA GeForce RTX 4060 Ti 16GB | Q4_K_M | offload | 4 tok/s | 650 € | 5.8 |
| NVIDIA GeForce RTX 5070 | Q4_K_M | offload | 4 tok/s | 699 € | 5.0 |
| NVIDIA GeForce RTX 4070 Super | Q4_K_M | offload | 3 tok/s | 707 € | 4.8 |
| NVIDIA GeForce RTX 5060 Ti 16GB | Q4_K_M | offload | 4 tok/s | 739 € | 5.7 |
| AMD Radeon RX 9070 XT | Q4_K_M | offload | 4 tok/s | 778 € | 5.7 |
| AMD Radeon RX 7600 XT | Q4_K_M | offload | 4 tok/s | 813 € | 4.4 |
| AMD Radeon RX 7900 XT | Q4_K_M | offload | 7 tok/s | 855 € | 7.8 |
| AMD Radeon RX 7800 XT | Q4_K_M | offload | 4 tok/s | 1.002 € | 4.3 |
| Intel Arc Pro B60 | Q4_K_M | offload | 8 tok/s | 1.074 € | 7.2 |
| NVIDIA GeForce RTX 4080 Super | Q4_K_M | offload | 5 tok/s | 1.100 € | 4.3 |
| NVIDIA GeForce RTX 5070 Ti | Q4_K_M | offload | 5 tok/s | 1.149 € | 4.2 |
| NVIDIA GeForce RTX 4070 Ti Super | Q4_K_M | offload | 5 tok/s | 1.247 € | 3.7 |
| NVIDIA GeForce RTX 5080 | Q4_K_M | offload | 5 tok/s | 1.500 € | 3.3 |
| AMD Radeon RX 7900 XTX | Q4_K_M | offload | 14 tok/s | 1.555 € | 9.0 |
| NVIDIA GeForce RTX 3090 | Q4_K_M | offload | 16 tok/s | 1.850 € | 8.8 |
| NVIDIA GeForce RTX 3090 Ti | Q4_K_M | offload | 17 tok/s | 2.500 € | 6.8 |
| NVIDIA GeForce RTX 4090 | Q4_K_M | offload | 17 tok/s | 3.955 € | 4.3 |
2 other cards and machines we price cannot hold it at this context.
Memory needed
Weights, KV cache and runtime buffers at each context length (KV cache at fp16).
| Quant | File | @8k | @32k | @64k | @128k |
|---|---|---|---|---|---|
| Q4_K_Mgguf | 13.3 GB | 15.3 GB | 19.2 GB | 24.4 GB | 34.8 GB |
| Q5_K_Mgguf | 15.6 GB | 17.6 GB | 21.5 GB | 26.7 GB | 37.1 GB |
| Q8_0gguf | 23.3 GB | 25.3 GB | 29.2 GB | 34.4 GB | 44.8 GB |
Run it
Copy a command for the runtime you use.
- Q4_K_M
- Ollama
ollama run hf.co/mistralai/Devstral-Small-2507_gguf:Q4_K_M - LM Studio
lms get mistralai/Devstral-Small-2507_gguf@q4_k_m - llama.cpp
llama-server -hf mistralai/Devstral-Small-2507_gguf:Q4_K_M
- Ollama
- Q5_K_M
- Ollama
ollama run hf.co/mistralai/Devstral-Small-2507_gguf:Q5_K_M - LM Studio
lms get mistralai/Devstral-Small-2507_gguf@q5_k_m - llama.cpp
llama-server -hf mistralai/Devstral-Small-2507_gguf:Q5_K_M
- Ollama
- Q8_0
- Ollama
ollama run hf.co/mistralai/Devstral-Small-2507_gguf:Q8_0 - LM Studio
lms get mistralai/Devstral-Small-2507_gguf@q8_0 - llama.cpp
llama-server -hf mistralai/Devstral-Small-2507_gguf:Q8_0
- Ollama
Notes
Text-only 24B coding agent (vision encoder removed), said to run on a single RTX 4090 or 32GB Mac. vLLM serving requires vLLM >= 0.9.1 and mistral_common >= 1.7.0.
Questions
- How much VRAM does Devstral Small 2507 need?
- At Q4_K_M, Devstral Small 2507 needs about 15 GB of memory at 8k context and 24 GB at 64k, counting the weights, the KV cache and runtime buffers.
- What is the best GPU for running Devstral Small 2507 locally?
- The cheapest hardware in Germany that runs Devstral Small 2507 fully in memory at 64k context is the NVIDIA GeForce RTX 5090, 6.028 € at Amazon, at about 40 tok/s (estimated).
- Can I run Devstral Small 2507 on a 16 GB GPU?
- Only partly: at 64k context it does not fit in 16 GB, so part of it runs from system RAM and it slows to about 4 tok/s on the AMD Radeon RX 9060 XT 16GB (estimated).
- Can I run Devstral Small 2507 on a 24 GB GPU?
- Only partly: at 64k context it does not fit in 24 GB, so part of it runs from system RAM and it slows to about 8 tok/s on the NVIDIA Tesla P40 (estimated).
- How do I run Devstral Small 2507 locally?
- With Ollama: ollama run hf.co/mistralai/Devstral-Small-2507_gguf:Q4_K_M (the Q4_K_M build). LM Studio and llama.cpp commands are on this page.
- What licence is Devstral Small 2507 released under?
- Apache 2.0, which allows commercial use.