Meta-Llama-3.1-405B-Instruct GGUF size and VRAM requirements
MaziyarPanahi/Meta-Llama-3.1-405B-Instruct-GGUF is a very large instruction-tuned chat model with 410.08 billion parameters, built on the llama architecture. It is released under the llama3.1 license and has been downloaded 98,051 times.
To run MaziyarPanahi/Meta-Llama-3.1-405B-Instruct-GGUF locally at a 4,096-token context, its quantized versions need between 145.56 GB (Q2_K, lowest quality) and 169.63 GB (Q3_K_S, highest quality) of memory, weights plus KV cache and a system margin included.
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 | 140.82 GB | 3.94 GB | 145.56 GB | — | Insufficient |
| Q3_K_S | 3.45 | Fair | 164.89 GB | 3.94 GB | 169.63 GB | — | Insufficient |
KV cache computed from the model's exact architecture. Speed is a rough estimate bounded by memory bandwidth.
Frequently asked questions
Can I run MaziyarPanahi/Meta-Llama-3.1-405B-Instruct-GGUF on an 8 GB GPU?
No. MaziyarPanahi/Meta-Llama-3.1-405B-Instruct-GGUF does not fit on an 8 GB GPU, even with the smallest quantization and system RAM offloading.
Can I run MaziyarPanahi/Meta-Llama-3.1-405B-Instruct-GGUF on a 16 GB GPU?
No. MaziyarPanahi/Meta-Llama-3.1-405B-Instruct-GGUF does not fit on a 16 GB GPU, even with the smallest quantization and system RAM offloading.
Can I run MaziyarPanahi/Meta-Llama-3.1-405B-Instruct-GGUF on a 24 GB GPU?
No. MaziyarPanahi/Meta-Llama-3.1-405B-Instruct-GGUF does not fit on a 24 GB GPU, even with the smallest quantization and system RAM offloading.
What is the best quantization for MaziyarPanahi/Meta-Llama-3.1-405B-Instruct-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.