Running Llama 3.1 405B on a Single 8xA100 Node with Hot-Loaded LoRA Adapters

A user demonstrates successfully running the Llama 3.1 405B model quantized to AWQ-INT4 on a single node equipped with eight A100 80GB GPUs, enabling up to 30 fine-tuned specialists to be loaded and switched in under 200ms.

• Base model: Llama 3.1 405B (AWQ-INT4, 202GB) with 150GB VRAM remaining after loading adapters and KV cache.
• Adapter switching latency is sub-200ms via vLLM's enable_lora feature, allowing for rapid context changes.
• The system has maintained over 60 days of uptime with zero service restarts in a production environment.
• Performance metrics include a time to first token of 63-66ms, single adapter throughput of 18.7-19.2 tok/sec (sustained), and 7 concurrent adapters achieving 82.9 tok/sec combined.
• The setup supports approximately 30 adapters sized between 2-5GB each, trained as NF4 adapters served on the AWQ-INT4 base without retraining.

This configuration addresses high-stakes domains like healthcare and legal by providing the reasoning depth of a large model while reducing hallucination risks through fine-tuning and distillation, offering a cost-effective alternative to H100 clusters for self-hosted applications.