Run MaziyarPanahi/Mistral-Small-Instruct-2409-GGUF locally
MaziyarPanahi/Mistral-Small-Instruct-2409-GGUF is a large instruction-tuned chat model with 22.25 billion parameters, built on the llama architecture. It has been downloaded 103,571 times.
To run MaziyarPanahi/Mistral-Small-Instruct-2409-GGUF locally at a 4,096-token context, its quantized versions need between 6.17 GB (IQ1_S, lowest quality) and 43.12 GB (GGUF, highest quality) of memory, weights plus KV cache and a system margin included.
For most users the best balance is IQ2_XS, needing about 7.86 GB. That means MaziyarPanahi/Mistral-Small-Instruct-2409-GGUF fits entirely in the VRAM of an 8 GB GPU or larger, running fully on the GPU.
All quantizations
| Quant. | Bits | Quality | Weights | KV | Total | Speed~ | Verdict |
|---|---|---|---|---|---|---|---|
| IQ1_S | 1.74 | Very low | 4.5 GB | 0.88 GB | 6.17 GB | 88.9 t/s | Fits in VRAM |
| IQ1_M | 1.89 | Very low | 4.91 GB | 0.88 GB | 6.58 GB | 81.5 t/s | Fits in VRAM |
| IQ2_XS | 2.39 | Very low | 6.19 GB | 0.88 GB | 7.86 GB | 64.6 t/s | Fits in VRAM |
| Q2_K | 2.97 | Low | 7.7 GB | 0.88 GB | 9.38 GB | 6.5 t/s | Offload |
| IQ3_XS | 3.3 | Low | 8.55 GB | 0.88 GB | 10.22 GB | 5.9 t/s | Offload |
| Q3_K_S | 3.47 | Fair | 8.98 GB | 0.88 GB | 10.65 GB | 5.6 t/s | Offload |
| Q3_K_M | 3.87 | Fair | 10.02 GB | 0.88 GB | 11.69 GB | 5.0 t/s | Offload |
| Q3_K_L | 4.22 | Good | 10.92 GB | 0.88 GB | 12.6 GB | 4.6 t/s | Offload |
| IQ4_XS | 4.29 | Good | 11.12 GB | 0.88 GB | 12.79 GB | 4.5 t/s | Offload |
| Q4_K_S | 4.55 | Good | 11.79 GB | 0.88 GB | 13.47 GB | 4.2 t/s | Offload |
| Q4_K_M | 4.8 | Good | 12.42 GB | 0.88 GB | 14.1 GB | 4.0 t/s | Offload |
| Q5_K_S | 5.51 | Very good | 14.27 GB | 0.88 GB | 15.95 GB | 3.5 t/s | Offload |
| Q5_K_M | 5.65 | Very good | 14.64 GB | 0.88 GB | 16.32 GB | 3.4 t/s | Offload |
| Q6_K | 6.56 | Excellent | 17.0 GB | 0.88 GB | 18.67 GB | 2.9 t/s | Offload |
| Q8_0 | 8.5 | Excellent | 22.02 GB | 0.88 GB | 23.69 GB | 2.3 t/s | Offload |
| GGUF | 16.0 | Excellent | 41.44 GB | 0.88 GB | 43.12 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-Instruct-2409-GGUF?
You need about 7.86 GB of VRAM to run MaziyarPanahi/Mistral-Small-Instruct-2409-GGUF entirely on the GPU using the IQ2_XS quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run MaziyarPanahi/Mistral-Small-Instruct-2409-GGUF on an 8 GB GPU?
Yes. With 8 GB of VRAM you can run MaziyarPanahi/Mistral-Small-Instruct-2409-GGUF fully on the GPU using IQ2_XS (about 7.86 GB).
Can I run MaziyarPanahi/Mistral-Small-Instruct-2409-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run MaziyarPanahi/Mistral-Small-Instruct-2409-GGUF fully on the GPU using Q5_K_S (about 15.95 GB).
Can I run MaziyarPanahi/Mistral-Small-Instruct-2409-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run MaziyarPanahi/Mistral-Small-Instruct-2409-GGUF fully on the GPU using Q8_0 (about 23.69 GB).
What is the best quantization for MaziyarPanahi/Mistral-Small-Instruct-2409-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.