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 405.85 billion parameters, built on the llama architecture. It is released under the llama3.1 license and has been downloaded 173,018 times.
Under the hood it uses 126 transformer layers, a hidden size of 16,384, 128 attention heads. It uses grouped-query attention (128 query heads sharing 16 key/value heads), which already trims KV-cache memory compared with full multi-head attention.
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. Context length drives that memory directly: at 4,096 tokens the KV cache for Meta-Llama-3.1-405B-Instruct-GGUF is about 3.94 GB, rising to roughly 126.0 GB at its full 131,072-token context. Shorter prompts free up memory for a higher-quality quantization.
Available GGUF quantizations for MaziyarPanahi/Meta-Llama-3.1-405B-Instruct-GGUF include Q2_K, Q3_K_S. 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 | 2.98 | Low | 140.82 GB | 3.94 GB | 145.56 GB | — | Insufficient |
| Q3_K_S | 3.49 | 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
What kind of model is MaziyarPanahi/Meta-Llama-3.1-405B-Instruct-GGUF?
MaziyarPanahi/Meta-Llama-3.1-405B-Instruct-GGUF is an instruction-tuned chat model with 405.85 billion parameters, based on the llama architecture. It is released under the llama3.1 license and distributed as GGUF files for local inference.
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 context length does MaziyarPanahi/Meta-Llama-3.1-405B-Instruct-GGUF support?
MaziyarPanahi/Meta-Llama-3.1-405B-Instruct-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/Meta-Llama-3.1-405B-Instruct-GGUF?
For MaziyarPanahi/Meta-Llama-3.1-405B-Instruct-GGUF, 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.