Inference optimization

Running an LLM is just the starting point. Making it fast, efficient, and scalable is where inference optimization comes into play.

Why do you need to optimize inference?

A setup that works in a demo can fail under real traffic. Latency can grow as queues form, throughput can plateau below the hardware's capacity, and cost can scale faster than expected. In production, optimization helps you meet latency and throughput goals at a cost the product can sustain.

Optimization also gives you more control over trade-offs. Some workloads need low latency for interactive users. Others need maximum throughput for batch jobs. Long-context applications need careful KV cache management. Multi-tenant systems need routing and scheduling policies that prevent one workload from hurting another.

If you're using a serverless endpoint (e.g., OpenAI API), much of this work is abstracted away, but the trade-offs still affect price, rate limits, and response time. If you're self-hosting open-source or custom models, applying the right optimization techniques is what lets you adapt the serving stack to your actual workload instead of accepting whatever the default runtime gives you.

📄️ LLM performance benchmarks

LLM performance benchmarks are standardized tests that measure how LLMs perform under specific conditions. Unlike leaderboards that rank the best LLMs based on accuracy or reasoning ability, performance benchmarks focus on practical LLM performance metrics such as throughput, latency, cost efficiency, and resource utilization. Learn how to run and interpret LLM performance benchmarks.

📄️ Static, dynamic and continuous batching

Optimize LLM inference with static, dynamic, and continuous batching for better GPU utilization.

📄️ PagedAttention

Improve LLM memory usage with block-based KV cache storage via PagedAttention.

📄️ Speculative decoding

Speculative decoding accelerates LLM inference with draft model predictions verified by the target model.

📄️ Prefill-decode disaggregation

Disaggregate prefill and decode for better parallel execution, resource allocation, and scaling.

📄️ Prefix caching

Prefix caching speeds up LLM inference by reusing shared prompt KV cache across requests.

📄️ Inference routing

Route LLM requests using cache locality, queue depth, KV cache pressure, and worker state for lower latency and better utilization.

📄️ KV cache offloading

Learn how KV cache offloading improves LLM inference by reducing GPU memory usage, lowering latency, and cutting compute costs.

📄️ Data, tensor, pipeline, expert and hybrid parallelisms

Understand the differences between data, tensor, pipeline, expert and hybrid parallelisms.

📄️ Offline batch inference

Run predictions at scale with offline batch inference for efficient, non-real-time processing.

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