Hosting Open Source LLMs on Serverless GPU Providers: The 2025 Guide
Deploying open-source Large Language Models (LLMs) on serverless GPU infrastructure eliminates hardware management while enabling dynamic scaling. This guide explores an provider-agnostic framework optimized for cost, security, and real-time performance.
Deployment Architectures for LLMs
Containerization Strategy: Package LLMs (e.g., Llama 2, Mistral) in GPU-optimized containers with quantized weights for faster cold starts.
Provider-Agnostic Triggers: Use HTTP endpoints or event queues (e.g., SQS/PubSub) to invoke models. Maintain compatibility across AWS Lambda GPU, Lambda Labs, and RunPod.
Performance Optimization
- Cold Start Mitigation: Pre-warm instances using scheduled keep-alive pings
- Quantization: 4-bit GGML/GPTQ models reduce memory footprint by 60%+
- Batching: Parallelize requests during peak loads
Security Protocols
Implement zero-trust architecture:
- API gateway authentication with JWT/OAuth
- Model weights encryption at rest (AES-256)
- Input/output validation against prompt injection
Auto-Scaling Patterns
Configure scaling policies based on:
- Concurrent request queue depth
- GPU memory utilization (alert at >85%)
- Cost-per-request thresholds
Cost Optimization Framework
| Model Size | Avg. Runtime | Cost/1M Tokens |
|---|---|---|
| 7B params | 350ms | $0.27 |
| 13B params | 620ms | $0.83 |
| 70B params | 2100ms | $4.10 |
Savings Tactics: Spot instance bidding, request coalescing, and model pruning.
“Serverless GPUs democratize LLM hosting but require rigorous monitoring. Track GPU memory leakage and cold start frequency – they’re the silent cost killers.”
Deep Dives
Practical Guides
