What is Throughput vs. Latency Optimization?
Throughput vs. Latency Optimization balances requests per second (throughput) against time per request (latency) through batching and scheduling strategies. Different applications require different optimization targets.
This model optimization and inference term is currently being developed. Detailed content covering implementation approaches, performance tradeoffs, best practices, and deployment considerations will be added soon. For immediate guidance on model optimization strategies, contact Pertama Partners for advisory services.
Throughput-latency optimization directly determines AI infrastructure cost efficiency, with well-tuned systems serving 2-3x more requests per GPU dollar than naive deployment configurations. Interactive applications like chatbots and real-time translation require sub-200ms latency constraints that batch processing approaches cannot satisfy without architectural compromise. Organizations serving both real-time and batch workloads should deploy separate inference pipelines optimized for each pattern rather than forcing single-configuration compromises. Southeast Asian AI service providers competing on price margins benefit disproportionately from throughput optimization since infrastructure costs represent 40-60% of per-request service delivery expenses.
- Throughput: requests per second (batch processing).
- Latency: time to first/last token (interactive).
- Batching increases throughput but raises latency.
- Continuous batching balances both.
- Choose target based on application (chatbot vs. batch processing).
- Infrastructure costs scale with throughput, UX with latency.
- Batch size selection creates fundamental tradeoffs where larger batches improve throughput by 2-4x but increase individual request latency proportionally affecting user experience.
- Continuous batching techniques dynamically group requests based on arrival patterns, achieving 80-90% of maximum throughput without fixed batch size latency penalties.
- SLA definitions should specify P95 and P99 latency requirements rather than averages since tail latencies disproportionately affect user satisfaction and system reliability perceptions.
- Queue management strategies including priority routing ensure high-value requests receive preferential processing when system load forces tradeoff decisions between competing objectives.
- Infrastructure right-sizing based on throughput-latency curves prevents over-provisioning that wastes 30-50% of GPU investment when workload patterns permit higher batch utilization.
Common Questions
When should we quantize models?
Quantize for deployment when inference cost or latency is concern and minor quality degradation is acceptable. Test quantized models thoroughly on your use cases. 8-bit quantization typically has minimal impact, 4-bit requires more careful evaluation.
How do we choose inference framework?
Consider model format compatibility, hardware support, performance requirements, and operational preferences. vLLM excels for high-throughput serving, TensorRT-LLM for low latency, Ollama for local deployment simplicity.
More Questions
Batching increases throughput but raises per-request latency. Optimize for throughput in offline batch processing, latency for interactive applications. Continuous batching balances both for variable workloads.
References
- NIST Artificial Intelligence Risk Management Framework (AI RMF 1.0). National Institute of Standards and Technology (NIST) (2023). View source
- Stanford HAI AI Index Report 2025. Stanford Institute for Human-Centered AI (2025). View source
Inference in AI is the process of running a trained model to generate outputs -- such as predictions, text responses, image classifications, or recommendations -- from new input data. It is the production phase of AI where the model delivers value to end users, as opposed to the training phase where the model learns.
Inference is the process of using a trained AI model to make predictions or decisions on new, unseen data in real time, representing the production phase where AI delivers actual business value by processing customer requests, analysing images, generating text, or making recommendations.
Repetition Penalty reduces probability of previously generated tokens to discourage repetitive text, improving output diversity. Repetition penalties are essential for coherent long-form generation.
Stop Sequences are tokens or strings that trigger generation termination when encountered, enabling control over output length and format. Stop sequences are critical for structured generation and chat applications.
Structured Generation constrains model outputs to match specified formats (JSON, XML, grammars) through constrained decoding. Structured generation ensures parseable, valid outputs for integration with systems.
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