LLM Inference Optimization: Cutting Latency Without Sacrificing Quality
LLM Inference Optimization
When you move a language model from a notebook into production, latency becomes the metric everyone notices. Users expect sub-second responses, but a 7B parameter model running in FP16 on a CPU can easily take ten seconds for a short completion.
The good news: you rarely need a bigger model to fix latency. You need a better inference stack.
Start with profiling
Before changing anything, measure where time goes. A typical inference request breaks down into:
- Tokenization — usually negligible
- Prefill — processing the prompt (compute-bound)
- Decode — generating each new token (memory-bandwidth-bound)
Most latency complaints come from decode. If your prefill dominates, your prompts are too long or your batch size is wrong.
Quantization
Post-training quantization (INT8, INT4, GPTQ, AWQ) can cut memory usage by 2–4× and often improves throughput with minimal quality loss on well-calibrated models.
| Method | Memory savings | Quality impact |
|---|---|---|
| FP16 | baseline | best |
| INT8 | ~2× | minimal |
| INT4 | ~4× | task-dependent |
For chat applications, INT4 with a good calibration set is often indistinguishable from FP16 for end users.
Batching strategies
- Static batching — simple, good for uniform workloads
- Continuous batching — vLLM-style iteration-level scheduling; best for variable-length requests
- Speculative decoding — draft model proposes tokens, target model verifies; great for latency-sensitive UIs
KV-cache management
The key-value cache grows linearly with sequence length. Techniques like PagedAttention, sliding window attention, and cache quantization keep memory predictable as conversations get longer.
Hardware selection
GPUs still win for throughput, but modern CPUs with AMX/AVX-512 and NPUs on edge devices are viable for smaller models. Match hardware to your SLA, not the benchmark leaderboard.
Takeaway
Optimize the path from prompt to first token, then optimize tokens per second. Most production wins come from quantization + continuous batching + right-sizing the model — not from swapping to a larger one.
