Inference optimization

Running an LLM is just the starting point. Making it fast, efficient, and scalable is where inference optimization comes into play. Whether you're building a chatbot, an agent, or any LLM-powered tool, inference performance directly impacts both user experience and operational cost.

If you're using a serverless endpoint (e.g., OpenAI API), much of this work is abstracted away. But if you're self-hosting open-source or custom models, applying the right optimization techniques lets you adapt to different use cases. This is how you can build faster, smarter, and more cost-effective AI applications than your competitors.

📄️ Key metrics for LLM inference

Measure key metrics like latency and throughput to optimize LLM inference performance.

📄️ 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.

📄️ Prefix-aware routing

Challenges in applying prefix caching

📄️ KV cache utilization-aware load balancing

Route LLM requests based on KV cache usage for faster, smarter inference.

📄️ 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.