What is vLLM?
vLLM is high-throughput inference engine for LLMs using PagedAttention and continuous batching to maximize GPU utilization. vLLM achieves industry-leading throughput for LLM serving.
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.
vLLM reduces LLM inference infrastructure costs by 60-80% compared to naive deployment approaches, making self-hosted AI economically viable for mid-size organizations. The open-source model eliminates vendor dependency while providing inference performance competitive with proprietary solutions from NVIDIA and commercial providers. Southeast Asian companies deploying multilingual customer service AI benefit from vLLM's efficient memory management serving diverse language workloads on constrained GPU budgets. Organizations processing sensitive data in regulated industries gain compliance advantages through self-hosted deployment avoiding third-party data processing agreements required by API-based inference providers.
- PagedAttention for efficient memory management.
- Continuous batching for high throughput.
- 10-20x higher throughput than naive PyTorch.
- Supports diverse models and quantization.
- OpenAI-compatible API.
- Standard for high-throughput LLM serving.
- PagedAttention memory management enables serving 3-5x more concurrent users per GPU compared to standard HuggingFace inference implementations.
- Continuous batching automatically groups incoming requests to maximize GPU utilization without requiring manual batch size configuration or scheduling logic.
- Open-source licensing eliminates per-query licensing costs that proprietary inference solutions impose at production scale exceeding 50,000 daily requests.
- Model compatibility covers major architectures including Llama, Mistral, and Qwen families with new model support typically added within 2-4 weeks of release.
- Distributed inference across multiple GPUs for models exceeding single-card memory requires tensor parallelism configuration adding deployment complexity.
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.
Need help implementing vLLM?
Pertama Partners helps businesses across Southeast Asia adopt AI strategically. Let's discuss how vllm fits into your AI roadmap.