Mistral-Large-Instruct-2411 GGUF size and VRAM requirements
MaziyarPanahi/Mistral-Large-Instruct-2411-GGUF is a very large instruction-tuned chat model with 122.61 billion parameters, built on the llama architecture. It has been downloaded 173,295 times.
Under the hood it uses 88 transformer layers, a hidden size of 12,288, 96 attention heads. It uses grouped-query attention (96 query heads sharing 8 key/value heads), which already trims KV-cache memory compared with full multi-head attention.
To run MaziyarPanahi/Mistral-Large-Instruct-2411-GGUF locally at a 4,096-token context, its quantized versions need between 44.27 GB (Q2_K, lowest quality) and 82.72 GB (Q5_K_M, highest quality) of memory, weights plus KV cache and a system margin included. Context length drives that memory directly: at 4,096 tokens the KV cache for Mistral-Large-Instruct-2411-GGUF is about 1.38 GB, rising to roughly 44.0 GB at its full 131,072-token context. Shorter prompts free up memory for a higher-quality quantization.
Available GGUF quantizations for MaziyarPanahi/Mistral-Large-Instruct-2411-GGUF include Q2_K, Q3_K_S, Q3_K_M, Q3_K_L, Q4_K_S, Q4_K_M, Q5_K_S, Q5_K_M. The model supports a native context length of up to 131,072 tokens; a longer context grows the KV cache and the memory needed.
GGUF file size and memory by quantization
Compare real GGUF weight sizes, estimated KV cache and total memory for Q4, Q5, Q8 and every quantization published in this repository.
| Quant. | Bits | Quality | Weights | KV | Total | Speed~ | Verdict |
|---|---|---|---|---|---|---|---|
| Q2_K | 2.95 | Low | 42.09 GB | 1.38 GB | 44.27 GB | — | Insufficient |
| Q3_K_S | 3.45 | Fair | 49.22 GB | 1.38 GB | 51.4 GB | — | Insufficient |
| Q3_K_M | 3.86 | Fair | 55.04 GB | 1.38 GB | 57.22 GB | — | Insufficient |
| Q3_K_L | 4.21 | Good | 60.12 GB | 1.38 GB | 62.3 GB | — | Insufficient |
| Q4_K_S | 4.54 | Good | 64.79 GB | 1.38 GB | 66.97 GB | — | Insufficient |
| Q4_K_M | 4.78 | Good | 68.19 GB | 1.38 GB | 70.37 GB | — | Insufficient |
| Q5_K_S | 5.5 | Very good | 78.56 GB | 1.38 GB | 80.74 GB | — | Insufficient |
| Q5_K_M | 5.64 | Very good | 80.55 GB | 1.38 GB | 82.72 GB | — | Insufficient |
KV cache computed from the model's exact architecture. Speed is a rough estimate bounded by memory bandwidth.
Frequently asked questions
What kind of model is MaziyarPanahi/Mistral-Large-Instruct-2411-GGUF?
MaziyarPanahi/Mistral-Large-Instruct-2411-GGUF is an instruction-tuned chat model with 122.61 billion parameters, based on the llama architecture. It is distributed as GGUF files for local inference.
How much VRAM do you need to run MaziyarPanahi/Mistral-Large-Instruct-2411-GGUF?
You need about 44.27 GB of VRAM to run MaziyarPanahi/Mistral-Large-Instruct-2411-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-Large-Instruct-2411-GGUF on an 8 GB GPU?
No. MaziyarPanahi/Mistral-Large-Instruct-2411-GGUF does not fit on an 8 GB GPU, even with the smallest quantization and system RAM offloading.
Can I run MaziyarPanahi/Mistral-Large-Instruct-2411-GGUF on a 16 GB GPU?
Partially. MaziyarPanahi/Mistral-Large-Instruct-2411-GGUF only fits on a 16 GB GPU by offloading part of it to system RAM (with Q2_K), which runs but is slower.
Can I run MaziyarPanahi/Mistral-Large-Instruct-2411-GGUF on a 24 GB GPU?
Partially. MaziyarPanahi/Mistral-Large-Instruct-2411-GGUF only fits on a 24 GB GPU by offloading part of it to system RAM (with Q4_K_M), which runs but is slower.
What context length does MaziyarPanahi/Mistral-Large-Instruct-2411-GGUF support?
MaziyarPanahi/Mistral-Large-Instruct-2411-GGUF supports a native context length of up to 131,072 tokens. A longer context grows the KV cache, so it increases the memory needed to run the model.
What is the best quantization for MaziyarPanahi/Mistral-Large-Instruct-2411-GGUF?
For MaziyarPanahi/Mistral-Large-Instruct-2411-GGUF, a strong default is Q4_K_M, which needs about 70.37 GB and keeps most of the quality while roughly halving the memory versus 8-bit. With VRAM to spare, Q5_K_M or Q6_K add a little more quality; if you are tight on memory, a smaller quantization still runs. Pick the highest quantization that fits your VRAM.