Run bartowski/Meta-Llama-3.1-8B-Instruct-GGUF locally
Meta-Llama-3.1-8B-Instruct is an AI language model based on the Llama family, designed for instruction-following and natural language processing tasks. With 8.03 billion parameters, it operates under the llama3.1 license and is optimized for general-purpose use cases requiring structured responses and conversational capabilities.
To run bartowski/Meta-Llama-3.1-8B-Instruct-GGUF locally at a 4,096-token context, its quantized versions need between 4.05 GB (IQ2_M, lowest quality) and 31.22 GB (F32, highest quality) of memory, weights plus KV cache and a system margin included.
For most users the best balance is Q8_0, needing about 9.25 GB. That means bartowski/Meta-Llama-3.1-8B-Instruct-GGUF fits entirely in the VRAM of a 6 GB GPU or larger, running fully on the GPU.
All quantizations
| Quant. | Bits | Quality | Weights | KV | Total | Speed~ | Verdict |
|---|---|---|---|---|---|---|---|
| IQ2_M | 2.94 | Low | 2.75 GB | 0.5 GB | 4.05 GB | 145.7 t/s | Fits in VRAM |
| Q2_K | 3.17 | Low | 2.96 GB | 0.5 GB | 4.26 GB | 135.1 t/s | Fits in VRAM |
| IQ3_XS | 3.51 | Fair | 3.28 GB | 0.5 GB | 4.58 GB | 122.1 t/s | Fits in VRAM |
| Q3_K_S | 3.65 | Fair | 3.41 GB | 0.5 GB | 4.71 GB | 117.2 t/s | Fits in VRAM |
| Q2_K_L | 3.68 | Fair | 3.44 GB | 0.5 GB | 4.74 GB | 116.3 t/s | Fits in VRAM |
| IQ3_M | 3.77 | Fair | 3.52 GB | 0.5 GB | 4.82 GB | 113.5 t/s | Fits in VRAM |
| Q3_K_M | 4.0 | Fair | 3.74 GB | 0.5 GB | 5.04 GB | 106.9 t/s | Fits in VRAM |
| Q3_K_L | 4.31 | Good | 4.03 GB | 0.5 GB | 5.33 GB | 99.4 t/s | Fits in VRAM |
| IQ4_XS | 4.43 | Good | 4.14 GB | 0.5 GB | 5.44 GB | 96.6 t/s | Fits in VRAM |
| Q4_0_4_4 | 4.64 | Good | 4.34 GB | 0.5 GB | 5.64 GB | 92.1 t/s | Fits in VRAM |
| Q4_0_4_8 | 4.64 | Good | 4.34 GB | 0.5 GB | 5.64 GB | 92.1 t/s | Fits in VRAM |
| Q4_0_8_8 | 4.64 | Good | 4.34 GB | 0.5 GB | 5.64 GB | 92.1 t/s | Fits in VRAM |
| IQ4_NL | 4.66 | Good | 4.36 GB | 0.5 GB | 5.66 GB | 91.8 t/s | Fits in VRAM |
| Q4_K_S | 4.67 | Good | 4.37 GB | 0.5 GB | 5.67 GB | 91.5 t/s | Fits in VRAM |
| Q3_K_XL | 4.76 | Good | 4.45 GB | 0.5 GB | 5.75 GB | 89.8 t/s | Fits in VRAM |
| Q4_K_M | 4.9 | Good | 4.58 GB | 0.5 GB | 5.88 GB | 87.3 t/s | Fits in VRAM |
| Q4_K_L | 5.29 | Very good | 4.95 GB | 0.5 GB | 6.25 GB | 80.9 t/s | Fits in VRAM |
| Q5_K_S | 5.58 | Very good | 5.21 GB | 0.5 GB | 6.51 GB | 76.7 t/s | Fits in VRAM |
| Q5_K_M | 5.71 | Very good | 5.34 GB | 0.5 GB | 6.64 GB | 74.9 t/s | Fits in VRAM |
| Q5_K_L | 6.03 | Very good | 5.64 GB | 0.5 GB | 6.94 GB | 70.9 t/s | Fits in VRAM |
| Q6_K | 6.57 | Excellent | 6.14 GB | 0.5 GB | 7.44 GB | 65.1 t/s | Fits in VRAM |
| Q6_K_L | 6.82 | Excellent | 6.38 GB | 0.5 GB | 7.68 GB | 62.7 t/s | Fits in VRAM |
| Q8_0 | 8.51 | Excellent | 7.95 GB | 0.5 GB | 9.25 GB | 50.3 t/s | Fits in VRAM |
| F32 | 32.01 | Excellent | 29.92 GB | 0.5 GB | 31.22 GB | 1.7 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 bartowski/Meta-Llama-3.1-8B-Instruct-GGUF?
You need about 5.88 GB of VRAM to run bartowski/Meta-Llama-3.1-8B-Instruct-GGUF entirely on the GPU using the Q4_K_M quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run bartowski/Meta-Llama-3.1-8B-Instruct-GGUF on an 8 GB GPU?
Yes. With 8 GB of VRAM you can run bartowski/Meta-Llama-3.1-8B-Instruct-GGUF fully on the GPU using Q6_K_L (about 7.68 GB).
Can I run bartowski/Meta-Llama-3.1-8B-Instruct-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run bartowski/Meta-Llama-3.1-8B-Instruct-GGUF fully on the GPU using Q8_0 (about 9.25 GB).
Can I run bartowski/Meta-Llama-3.1-8B-Instruct-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run bartowski/Meta-Llama-3.1-8B-Instruct-GGUF fully on the GPU using Q8_0 (about 9.25 GB).
What is the best quantization for bartowski/Meta-Llama-3.1-8B-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.