The Inference Cost Problem
As AI applications scale to millions of users, inference costs can grow exponentially. A naive deployment of a 70B model might cost $2-5 per million tokens. With the right optimizations, you can bring that below $0.20 per million tokens without measurable quality regression.
- INT4/INT8 quantization — reduces model memory footprint by 4-8x, enabling larger batch sizes or fitting more models per GPU.
- Speculative decoding — a small draft model proposes tokens that the large model verifies in parallel, achieving 2-4x throughput gains.
- Continuous batching — dynamically groups requests to maximize GPU utilization, eliminating idle time between completions.
- KV cache optimization — prefix caching and paged attention (vLLM) reduce memory overhead for shared prompt prefixes.