Top 10 techniques to reduce LLM inference costs

Running LLMs in production can get expensive quickly, but a lot of the cost comes from inefficient inference setups rather than the models themselves.

Here are 10 techniques teams commonly use to reduce inference costs:

1. Batching requests – Combine multiple prompts into one GPU execution to increase utilization.

2. Quantization – Use 4-bit or 8-bit weights to reduce memory and improve throughput.

3. Speculative decoding – Use a smaller model to draft tokens and a larger model to verify.

4. Model routing – Send simple queries to cheaper models and complex ones to larger models.

5. Prompt compression – Reduce unnecessary tokens in prompts and system instructions.

6. KV cache reuse – Reuse context where possible to avoid recomputation.

7. Streaming responses – Reduces perceived latency and can reduce compute usage in some flows.

8. Autoscaling inference servers – Scale GPUs only when traffic spikes.

9. Distillation – Train smaller models that mimic larger models.

10. Efficient inference stacks – Use optimized runtimes like vLLM, TensorRT-LLM, or TGI.

Many teams reduce 30–70% of inference cost just by improving utilization and architecture.

Curious what others are doing:

What techniques have made the biggest difference in your inference costs?