What is PagedAttention (vLLM)?
PagedAttention manages KV cache in non-contiguous memory pages like virtual memory, eliminating fragmentation and enabling efficient memory usage. PagedAttention is core innovation enabling vLLM's high throughput.
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.
Paged attention in vLLM increases serving throughput 2-4x on identical hardware by eliminating memory waste, directly halving per-request inference costs for high-volume applications. Companies migrating from naive serving implementations to vLLM defer GPU capacity expansions by 6-12 months, preserving USD 20K-100K in hardware spending. For startups and mid-market companies self-hosting open-weight models, vLLM's memory efficiency determines whether deployment economics compete with managed API pricing at their actual request volumes.
- KV cache in paged memory (vs. contiguous).
- Eliminates memory fragmentation.
- Near-zero memory waste from padding.
- Enables sharing KV cache across sequences (prefix sharing).
- Core to vLLM performance advantages.
- Inspired by operating system virtual memory.
- Deploy vLLM for production LLM serving when handling variable-length concurrent requests because paged attention eliminates memory fragmentation that wastes 60-80% of GPU memory in naive implementations.
- Configure page sizes and memory allocation policies based on your expected request length distribution since optimal settings vary between conversational and document processing workloads.
- Benchmark vLLM throughput against TGI and Triton on your target model because framework performance advantages differ across architectures, quantization levels, and hardware configurations.
- Monitor GPU memory utilization dashboards to verify paged attention is achieving expected efficiency gains since misconfiguration can silently negate theoretical memory savings.
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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