What is Continuous Batching?
Continuous Batching dynamically adds and removes requests from batches as they arrive and complete, maximizing GPU utilization for variable-length generation. Continuous batching improves throughput without sacrificing latency.
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.
Continuous batching reduces LLM serving costs by 50-70% through improved GPU utilization, directly translating to USD 5K-30K monthly savings for companies processing thousands of daily inference requests. The throughput improvements enable serving more concurrent users on identical hardware, deferring expensive GPU capacity expansions that would otherwise be triggered by growing demand. For startups and mid-market companies operating on constrained infrastructure budgets, continuous batching represents the single highest-impact optimization for making self-hosted LLM deployments economically viable.
- Dynamic batching vs. static batch processing.
- Adds requests as they arrive, removes as complete.
- Maximizes GPU utilization with variable lengths.
- Reduces wait time for short requests in batch.
- Standard in vLLM and modern serving systems.
- Critical for efficient production serving.
- Enable continuous batching on LLM serving infrastructure to achieve 2-5x throughput improvements by eliminating idle GPU cycles that occur when shorter requests finish before longer ones in static batches.
- Configure maximum batch size and prefill token limits to prevent memory exhaustion during traffic spikes when many concurrent requests with long contexts arrive simultaneously.
- Monitor per-request latency distributions alongside throughput metrics since aggressive batching configurations can increase tail latencies beyond acceptable thresholds for interactive applications.
- Evaluate serving frameworks like vLLM and TGI that implement continuous batching natively rather than building custom batching logic that requires ongoing maintenance and optimization.
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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