Run MaziyarPanahi/Mistral-Small-24B-Instruct-2501-GGUF locally
MaziyarPanahi/Mistral-Small-24B-Instruct-2501-GGUF is a large instruction-tuned chat model with 23.57 billion parameters, built on the llama architecture. It is released under the apache-2.0 license and has been downloaded 106,263 times.
To run MaziyarPanahi/Mistral-Small-24B-Instruct-2501-GGUF locally at a 4,096-token context, its quantized versions need between 9.86 GB (Q2_K, lowest quality) and 45.5 GB (GGUF, highest quality) of memory, weights plus KV cache and a system margin included.
For most users the best balance is Q6_K, needing about 19.6 GB. That means MaziyarPanahi/Mistral-Small-24B-Instruct-2501-GGUF fits entirely in the VRAM of a 10 GB GPU or larger, running fully on the GPU.
All quantizations
| Quant. | Bits | Quality | Weights | KV | Total | Speed~ | Verdict |
|---|---|---|---|---|---|---|---|
| Q2_K | 3.02 | Low | 8.28 GB | 0.78 GB | 9.86 GB | 6.0 t/s | Offload |
| Q3_K_S | 3.53 | Fair | 9.69 GB | 0.78 GB | 11.27 GB | 5.2 t/s | Offload |
| Q3_K_M | 3.89 | Fair | 10.69 GB | 0.78 GB | 12.27 GB | 4.7 t/s | Offload |
| Q3_K_L | 4.21 | Good | 11.55 GB | 0.78 GB | 13.13 GB | 4.3 t/s | Offload |
| Q4_K_S | 4.6 | Good | 12.62 GB | 0.78 GB | 14.2 GB | 4.0 t/s | Offload |
| Q4_K_M | 4.86 | Good | 13.35 GB | 0.78 GB | 14.93 GB | 3.7 t/s | Offload |
| Q5_K_S | 5.53 | Very good | 15.18 GB | 0.78 GB | 16.77 GB | 3.3 t/s | Offload |
| Q5_K_M | 5.69 | Very good | 15.61 GB | 0.78 GB | 17.19 GB | 3.2 t/s | Offload |
| Q6_K | 6.57 | Excellent | 18.02 GB | 0.78 GB | 19.6 GB | 2.8 t/s | Offload |
| Q8_0 | 8.5 | Excellent | 23.33 GB | 0.78 GB | 24.92 GB | — | Insufficient |
| GGUF | 16.0 | Excellent | 43.92 GB | 0.78 GB | 45.5 GB | — | Insufficient |
KV cache computed from the model's exact architecture. Speed is a rough estimate bounded by memory bandwidth.
Frequently asked questions
How much VRAM do you need to run MaziyarPanahi/Mistral-Small-24B-Instruct-2501-GGUF?
You need about 9.86 GB of VRAM to run MaziyarPanahi/Mistral-Small-24B-Instruct-2501-GGUF entirely on the GPU using the Q2_K quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run MaziyarPanahi/Mistral-Small-24B-Instruct-2501-GGUF on an 8 GB GPU?
Partially. MaziyarPanahi/Mistral-Small-24B-Instruct-2501-GGUF only fits on an 8 GB GPU by offloading part of it to system RAM (with Q6_K), which runs but is slower.
Can I run MaziyarPanahi/Mistral-Small-24B-Instruct-2501-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run MaziyarPanahi/Mistral-Small-24B-Instruct-2501-GGUF fully on the GPU using Q4_K_M (about 14.93 GB).
Can I run MaziyarPanahi/Mistral-Small-24B-Instruct-2501-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run MaziyarPanahi/Mistral-Small-24B-Instruct-2501-GGUF fully on the GPU using Q6_K (about 19.6 GB).
What is the best quantization for MaziyarPanahi/Mistral-Small-24B-Instruct-2501-GGUF?
If memory allows, higher bits-per-weight means better quality. A common sweet spot is a Q4_K_M or Q5_K_M quantization, which keeps most of the quality while roughly halving the memory versus 8-bit. Pick the highest quantization that still fits in your VRAM.