Latency Optimization
Techniques and engineering practices that reduce the response time of AI systems from input to output for better user experience and throughput.
In Depth
Latency optimization encompasses the techniques, infrastructure decisions, and engineering practices aimed at reducing the time between submitting a request to an AI system and receiving the complete response. For interactive AI applications, latency directly impacts user experience, adoption, and the range of viable use cases, making it a critical performance dimension alongside accuracy and cost.
LLM inference latency has two key components: time-to-first-token (TTFT), which measures the delay before the model begins generating output, and inter-token latency (ITL), which measures the time between consecutive generated tokens. TTFT is dominated by the processing of the input prompt through the model (prefill phase), while ITL is determined by the speed of individual autoregressive decode steps. Different applications prioritize these differently: chatbots need low TTFT for responsiveness, while batch processing cares more about overall throughput.
Optimization techniques span multiple levels. Model-level optimizations include quantization (reducing weight precision), pruning (removing unimportant connections), distillation (training smaller models), and speculative decoding (using a fast draft model to propose tokens). Serving-level optimizations include continuous batching (dynamically grouping requests), paged attention (efficient KV-cache memory management), tensor parallelism (distributing across GPUs), and prefix caching (reusing computation for common prompt prefixes). Infrastructure-level optimizations include GPU selection, memory bandwidth optimization, and network topology design.
Production latency optimization requires measurement-driven engineering. Profiling tools identify bottlenecks across the inference pipeline. Latency budgets allocate time across system components (preprocessing, retrieval, inference, post-processing). SLO (Service Level Objective) definitions set concrete targets. A/B testing validates that optimizations improve real-world user experience. The optimization process is iterative, as changes to models, traffic patterns, and requirements continuously shift the performance landscape.
Related Terms
Inference
The process of running a trained AI model to generate predictions or outputs from new input data, as opposed to the training phase.
Quantization
The process of reducing AI model weight precision from higher-bit formats to lower-bit representations to decrease memory usage and increase inference speed.
Model Serving
The infrastructure and systems that host trained AI models and handle incoming prediction requests in production environments.
GPU Computing
The use of graphics processing units for general-purpose parallel computation, providing the massive throughput needed for training and running AI models.
Edge Inference
Running AI model inference directly on local devices or edge hardware near the data source, rather than sending data to cloud servers for processing.
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Refactoring AWS, Azure, GCP, and Oracle workloads into production-grade AI stacks. Multi-cloud RAG pipelines, observability, guardrails, and MLOps that slot into existing engineering rhythms.
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Planning and operating GPU fleets across factories, research hubs, and remote sites. Jetson, Fleet Command, and bare metal roll-outs with zero-trust networking and remote lifecycle management.
NVIDIA Blueprint Launch Kits
In-a-box deployments for Enterprise Research copilots, Enterprise RAG pipelines, and Video Search & Summarisation agents with interactive Q&A. Blueprints tuned for your data, infra, and compliance profile.
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