Run lmg-anon/vntl-llama3-8b-v2-gguf locally
lmg-anon/vntl-llama3-8b-v2-gguf is a large language model with 8.03 billion parameters, built on the llama architecture. It is released under the llama3 license and has been downloaded 756,073 times.
To run lmg-anon/vntl-llama3-8b-v2-gguf locally at a 4,096-token context, its quantized versions need between 6.64 GB (Q5_K_M, lowest quality) and 9.25 GB (Q8_0, 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 6.64 GB. That means lmg-anon/vntl-llama3-8b-v2-gguf fits entirely in the VRAM of an 8 GB GPU or larger, running fully on the GPU.
All quantizations
| Quant. | Bits | Quality | Weights | KV | Total | Speed~ | Verdict |
|---|---|---|---|---|---|---|---|
| Q5_K_M | 5.71 | Very good | 5.34 GB | 0.5 GB | 6.64 GB | 74.9 t/s | Fits in VRAM |
| Q8_0 | 8.51 | Excellent | 7.95 GB | 0.5 GB | 9.25 GB | 6.3 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 lmg-anon/vntl-llama3-8b-v2-gguf?
You need about 6.64 GB of VRAM to run lmg-anon/vntl-llama3-8b-v2-gguf entirely on the GPU using the Q5_K_M quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run lmg-anon/vntl-llama3-8b-v2-gguf on an 8 GB GPU?
Yes. With 8 GB of VRAM you can run lmg-anon/vntl-llama3-8b-v2-gguf fully on the GPU using Q5_K_M (about 6.64 GB).
Can I run lmg-anon/vntl-llama3-8b-v2-gguf on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run lmg-anon/vntl-llama3-8b-v2-gguf fully on the GPU using Q8_0 (about 9.25 GB).
Can I run lmg-anon/vntl-llama3-8b-v2-gguf on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run lmg-anon/vntl-llama3-8b-v2-gguf fully on the GPU using Q8_0 (about 9.25 GB).
What is the best quantization for lmg-anon/vntl-llama3-8b-v2-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.