Run MaziyarPanahi/Meta-Llama-3.1-70B-Instruct-GGUF locally
MaziyarPanahi/Meta-Llama-3.1-70B-Instruct-GGUF is a very large instruction-tuned chat model with 70.55 billion parameters, built on the llama architecture. It has been downloaded 160,065 times.
To run MaziyarPanahi/Meta-Llama-3.1-70B-Instruct-GGUF locally at a 4,096-token context, its quantized versions need between 16.34 GB (IQ1_S, lowest quality) and 55.96 GB (Q6_K, highest quality) of memory, weights plus KV cache and a system margin included.
For most users the best balance is IQ2_XS, needing about 21.74 GB. That means MaziyarPanahi/Meta-Llama-3.1-70B-Instruct-GGUF fits entirely in the VRAM of a 24 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 | 14.29 GB | 1.25 GB | 16.34 GB | 28.0 t/s | Fits in VRAM |
| IQ1_M | 1.9 | Very low | 15.6 GB | 1.25 GB | 17.65 GB | 25.6 t/s | Fits in VRAM |
| IQ2_XS | 2.4 | Very low | 19.69 GB | 1.25 GB | 21.74 GB | 20.3 t/s | Fits in VRAM |
| Q2_K | 2.99 | Low | 24.56 GB | 1.25 GB | 26.61 GB | 2.0 t/s | Offload |
| IQ3_XS | 3.32 | Fair | 27.29 GB | 1.25 GB | 29.34 GB | 1.8 t/s | Offload |
| Q3_K_S | 3.51 | Fair | 28.79 GB | 1.25 GB | 30.84 GB | 1.7 t/s | Offload |
| Q3_K_M | 3.89 | Fair | 31.91 GB | 1.25 GB | 33.96 GB | 1.6 t/s | Offload |
| Q3_K_L | 4.21 | Good | 34.59 GB | 1.25 GB | 36.64 GB | 1.4 t/s | Offload |
| IQ4_XS | 4.3 | Good | 35.3 GB | 1.25 GB | 37.35 GB | 1.4 t/s | Offload |
| Q4_K_S | 4.57 | Good | 37.58 GB | 1.25 GB | 39.63 GB | 1.3 t/s | Offload |
| Q4_K_M | 4.82 | Good | 39.6 GB | 1.25 GB | 41.65 GB | 1.3 t/s | Offload |
| Q5_K_S | 5.52 | Very good | 45.32 GB | 1.25 GB | 47.37 GB | 1.1 t/s | Offload |
| Q5_K_M | 5.66 | Very good | 46.52 GB | 1.25 GB | 48.57 GB | 1.1 t/s | Offload |
| Q6_K | 6.56 | Excellent | 53.91 GB | 1.25 GB | 55.96 GB | 0.9 t/s | Offload |
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/Meta-Llama-3.1-70B-Instruct-GGUF?
You need about 21.74 GB of VRAM to run MaziyarPanahi/Meta-Llama-3.1-70B-Instruct-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/Meta-Llama-3.1-70B-Instruct-GGUF on an 8 GB GPU?
Partially. MaziyarPanahi/Meta-Llama-3.1-70B-Instruct-GGUF only fits on an 8 GB GPU by offloading part of it to system RAM (with IQ2_XS), which runs but is slower.
Can I run MaziyarPanahi/Meta-Llama-3.1-70B-Instruct-GGUF on a 16 GB GPU?
Partially. MaziyarPanahi/Meta-Llama-3.1-70B-Instruct-GGUF only fits on a 16 GB GPU by offloading part of it to system RAM (with Q5_K_S), which runs but is slower.
Can I run MaziyarPanahi/Meta-Llama-3.1-70B-Instruct-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run MaziyarPanahi/Meta-Llama-3.1-70B-Instruct-GGUF fully on the GPU using IQ2_XS (about 21.74 GB).
What is the best quantization for MaziyarPanahi/Meta-Llama-3.1-70B-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.