Loading model data...
Loading model data...
Quick Answer: meta-llama/Llama-2-13b-chat-hf requires a minimum of 7GB VRAM for Q4 quantization. Compatible with 5 GPUs including NVIDIA L40. Expected speed: ~76 tokens/sec on NVIDIA L40. Plan for 32GB system RAM and 100GB of fast storage for smooth local inference.
Llama 3 13B hits the sweet spot between small-model cost and 70B accuracy. Give it a 16GB GPU and it becomes a capable coding and reasoning partner.
Start with at least 7GB of VRAM for Q4 inference. Scale to higher quantizations as your hardware grows, and pick a build below that fits your budget and throughput goals.
| Component | Minimum | Recommended | Optimal |
|---|---|---|---|
| VRAM | 7GB (Q4) | 13GB (Q8) | 26GB (FP16) |
| RAM | 16GB | 32GB | 64GB |
| Disk | 50GB | 100GB | - |
| Model size | 7GB (Q4) | 13GB (Q8) | 26GB (FP16) |
| CPU | Modern CPU (Ryzen 5/Intel i5 or better) | Modern CPU (Ryzen 5/Intel i5 or better) | Modern CPU (Ryzen 5/Intel i5 or better) |
See compatible GPUs →
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
Common questions about running meta-llama/Llama-2-13b-chat-hf locally
Use runtimes like llama.cpp, text-generation-webui, or vLLM. Download the quantized weights from Hugging Face, ensure you have enough VRAM for your target quantization, and launch with GPU acceleration (CUDA/ROCm/Metal).
Start with Q4 for wide GPU compatibility. Upgrade to Q8 if you have spare VRAM and want extra quality. FP16 delivers the highest fidelity but demands workstation or multi-GPU setups.
Official weights are available via Hugging Face. Quantized builds (Q4, Q8) can be loaded into runtimes like llama.cpp, text-generation-webui, or vLLM. Always verify the publisher before downloading.