Run MaziyarPanahi/Llama-3.3-70B-Instruct-GGUF locally
MaziyarPanahi/Llama-3.3-70B-Instruct-GGUF is a very large instruction-tuned chat model with 70.55 billion parameters, built on the llama architecture. It has been downloaded 163,570 times.
To run MaziyarPanahi/Llama-3.3-70B-Instruct-GGUF locally at a 4,096-token context, its quantized versions need between 26.61 GB (Q2_K, lowest quality) and 133.48 GB (GGUF, highest quality) of memory, weights plus KV cache and a system margin included.
For most users the best balance is Q5_K_M, needing about 48.57 GB. That means MaziyarPanahi/Llama-3.3-70B-Instruct-GGUF fits entirely in the VRAM of a 32 GB GPU or larger, running fully on the GPU.
All quantizations
| Quant. | Bits | Quality | Weights | KV | Total | Speed~ | Verdict |
|---|---|---|---|---|---|---|---|
| Q2_K | 2.99 | Low | 24.56 GB | 1.25 GB | 26.61 GB | 2.0 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 |
| 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 | — | Insufficient |
| Q8_0 | 8.5 | Excellent | 69.83 GB | 1.25 GB | 71.88 GB | — | Insufficient |
| GGUF | 16.0 | Excellent | 131.43 GB | 1.25 GB | 133.48 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/Llama-3.3-70B-Instruct-GGUF?
You need about 30.84 GB of VRAM to run MaziyarPanahi/Llama-3.3-70B-Instruct-GGUF entirely on the GPU using the Q3_K_S quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run MaziyarPanahi/Llama-3.3-70B-Instruct-GGUF on an 8 GB GPU?
No. MaziyarPanahi/Llama-3.3-70B-Instruct-GGUF does not fit on an 8 GB GPU, even with the smallest quantization and system RAM offloading.
Can I run MaziyarPanahi/Llama-3.3-70B-Instruct-GGUF on a 16 GB GPU?
Partially. MaziyarPanahi/Llama-3.3-70B-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/Llama-3.3-70B-Instruct-GGUF on a 24 GB GPU?
Partially. MaziyarPanahi/Llama-3.3-70B-Instruct-GGUF only fits on a 24 GB GPU by offloading part of it to system RAM (with Q8_0), which runs but is slower.
What is the best quantization for MaziyarPanahi/Llama-3.3-70B-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.