Performance: Master LLM Latency & Throughput

Performance: The Complete Guide to LLM Speed

Every millisecond matters. Users abandon slow AI features. This track gives you the techniques to achieve sub-second responses while maximizing throughput.

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🎯 What You’ll Master

TTFT Optimization

Understand Time to First Token and how to minimize perceived latency for users.

Throughput Scaling

Achieve 23x throughput improvements with continuous batching and infrastructure optimization.

RAG Performance

Eliminate retrieval bottlenecks with vector search optimization and hybrid strategies.

Infrastructure Selection

Choose the right GPUs, frameworks, and deployment patterns for your latency requirements.

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⚡ Latency & TTFT Optimization

Time to First Token (TTFT) The most important LLM metric

Latency Debugging Find and fix bottlenecks

GPU Hardware Selection A100 vs H100 vs L4 comparison

Continuous Batching 23x throughput improvement

KV Cache Optimization Memory for low-latency inference

Speculative Decoding 2-4x speedups with draft models

Model Serving Infrastructure vLLM vs TorchServe vs Ray Serve

Quantization for Latency 4-bit, 8-bit speed gains

Prompt Optimization for Latency Shorter prompts = faster responses

Batching at Scale High-volume throughput optimization

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🔍 RAG & Retrieval Performance

Vector Database Latency The overlooked RAG bottleneck

Vector Search Optimization ANN algorithms & indexing

Hybrid Search Dense + sparse search strategies

Context Window Management What to retrieve, what to skip

Embedding Model Selection Fast vs accurate tradeoffs

Multi-Stage Retrieval Fast retrieval → accurate reranking

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🔬 Specialized Topics

Long-Context Processing When larger windows help

Streaming Responses Perceived latency reduction

Multi-Token Prediction Speculative execution speedups

Request Batching Architecture Latency + throughput optimization

Multi-Level Caching Prompt, embedding, response cache

Distributed Inference Tensor & pipeline parallelism

Load Balancing Strategies for LLM endpoints

Latency Monitoring SLA enforcement & alerting

Cost vs Latency Tradeoff The deployment decision matrix

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🚀 Quick Wins

1. Enable streaming → Reduce perceived latency by 70%+
2. Implement continuous batching → 10-23x throughput improvement
3. Optimize KV cache → 20-30% memory savings
4. Use speculative decoding → 2-4x generation speedup

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Coming Soon: Interactive Latency Heatmap

Our latency benchmarking tool with model, hardware, and batch size comparisons is under development.