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Quick Answer: meta-llama/Llama-3.1-8B-Instruct requires a minimum of 5GB VRAM for Q4 quantization. Compatible with 5 GPUs including RTX 4090. Expected speed: ~72 tokens/sec on RTX 4090. Plan for 64GB system RAM and 100GB of fast storage for smooth local inference.
Llama 3 8B is the go-to lightweight assistant. It runs on almost any 12GB GPU, making it ideal for chatbots, agent prototypes, and personal copilots.
Start with at least 5GB 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 | 5GB (Q4) | 9GB (Q8) | 18GB (FP16) |
| RAM | 32GB | 64GB | 64GB |
| Disk | 50GB | 100GB | - |
| Model size | 5GB (Q4) | 9GB (Q8) | 18GB (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-3.1-8B-Instruct 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.