PROTOCOL.READ / 11 min read
Optimizing LLM Inference Costs: Reducing Compute Spend by 65% with vLLM & AWQ
How to deploy custom open-weights models on private GPU clusters, cutting API costs while improving throughput by 4x.
Beyond Commercial API Lock-In
Relying exclusively on proprietary SaaS API endpoints for high-throughput AI agent workloads creates unpredictable recurring costs and latency bottlenecks.
Key Optimization Techniques
- PagedAttention Engine: Implementing vLLM’s memory management to maximize batch sizes on NVIDIA A100/H100 GPUs.
- AWQ 4-bit Quantization: Reducing VRAM footprint by 75% with negligible accuracy drop.
- Speculative Decoding: Using a 1B draft model to accelerate a 70B target model’s output generation.