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.

📄️ 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.