# vllm | vLLM | Knowledge Base | Instagit

A high-throughput and memory-efficient inference and serving engine for LLMs

GitHub Stars: 71.8k

Repository: https://github.com/vllm-project/vllm

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## Articles

### [How to Implement Custom Logit Processors and Sampling Parameters in vLLM](/vllm-project/vllm/how-to-implement-custom-logit-processors-and-sampling-parameters-in-vllm)

Learn to implement custom logit processors and sampling parameters in vLLM. Extend vLLM's capabilities by subclassing LogitsProcessor and registering your custom logic for advanced text generation control.

- Tags: how-to-guide
- Published: 2026-03-03

### [How to Configure Compilation Modes (Eager, CUDAGraph, Inductor) in vLLM](/vllm-project/vllm/how-to-configure-compilation-modes-eager-cudagraph-inductor-in-vllm)

Master vLLM compilation modes eager CUDAGraph and Inductor. Learn to configure settings via CLI or Python API for optimized performance.

- Tags: deep-dive
- Published: 2026-03-03

### [How to Debug Memory Issues and OOM Errors in vLLM: A Complete Guide](/vllm-project/vllm/how-to-debug-memory-issues-and-oom-errors-in-vllm)

Debug vLLM memory issues and OOM errors with our guide. Isolate problems in workspace buffers, KV-cache, or activations using memory profiling tools.

- Tags: how-to-guide
- Published: 2026-03-03

### [How to Use the gRPC API for High-Performance Serving in vLLM](/vllm-project/vllm/how-to-use-the-grpc-api-for-high-performance-serving-in-vllm)

Learn to use the vLLM gRPC API for high-performance serving. Stream tokens efficiently with unlimited message sizes and structured output support for powerful language model inference.

- Tags: how-to-guide
- Published: 2026-03-03

### [How to Configure KV Cache Allocation and Management in vLLM](/vllm-project/vllm/how-to-configure-kv-cache-allocation-and-management-in-vllm)

Master KV cache allocation and management in vLLM. Learn to set CacheConfig parameters like block_size and gpu_memory_utilization for optimized tensor allocation via CLI or Python.

- Tags: how-to-guide
- Published: 2026-03-03

### [How to Use Multi-Modal Models (LLaVA) with vLLM: A Complete Guide](/vllm-project/vllm/how-to-use-multi-modal-models-llava-with-vllm)

Integrate LLaVA multi-modal models seamlessly with vLLM. Our guide simplifies image preprocessing and vision encoder inference for efficient LLM deployment.

- Tags: how-to-guide
- Published: 2026-03-03

### [vLLM v1 Engine vs Legacy Engine: Key Architectural Differences Explained](/vllm-project/vllm/how-does-the-v1-engine-differ-from-the-legacy-engine-in-vllm)

Explore the vLLM v1 engine vs legacy engine architectural differences. Discover its unified scheduler, `EngineCore` loop, and removal of legacy features for enhanced performance.

- Tags: architecture
- Published: 2026-03-03

### [How to Configure Attention Backends (FlashAttention and FlashInfer) in vLLM](/vllm-project/vllm/how-to-configure-attention-backends-flashattention-flashinfer-in-vllm)

Learn to configure attention backends like FlashAttention and FlashInfer in vLLM using CLI flags API or config files for faster inference. Optimize your LLM performance effortlessly.

- Tags: how-to-guide
- Published: 2026-03-03

### [How to Use Embedding Models and Pooling Parameters in vLLM: Complete Technical Guide](/vllm-project/vllm/how-to-use-embedding-models-and-pooling-parameters-in-vllm)

Learn to use embedding models and pooling parameters in vLLM. This guide details how vLLM's pooling runner generates text embeddings efficiently by routing hidden states through the model's pooler.

- Tags: how-to-guide
- Published: 2026-03-03

### [How Expert Parallelism Works for Mixture-of-Experts Models in vLLM](/vllm-project/vllm/how-does-expert-parallelism-work-for-mixture-of-expert-models-in-vllm)

Discover how expert parallelism in vLLM shards expert weights across GPUs for scalable distributed inference of Mixture-of-Experts models. Optimize your MoE inference.

- Tags: deep-dive
- Published: 2026-03-03

### [How to Optimize GPU Memory Utilization with gpu_memory_utilization in vLLM](/vllm-project/vllm/how-to-optimize-gpu-memory-utilization-with-gpu_memory_utilization-in-vllm)

Optimize GPU memory utilization in vLLM by setting the gpu_memory_utilization parameter. Balance throughput and OOM risk by adjusting this value. Learn how now.

- Tags: how-to-guide
- Published: 2026-03-03

### [How to Implement Beam Search and Parallel Sampling in vLLM](/vllm-project/vllm/how-to-implement-beam-search-and-parallel-sampling-in-vllm)

Learn to implement beam search and parallel sampling in vLLM. Optimize your LLM inference with efficient batching and GPU acceleration for faster requests.

- Tags: how-to-guide
- Published: 2026-03-03

### [How CUDA Graph Optimization Improves Inference Performance in vLLM](/vllm-project/vllm/how-does-cuda-graph-optimization-improve-inference-performance-in-vllm)

Discover how CUDA graph optimization in vLLM dramatically boosts inference speed. Experience 2x-5x faster decode and 1.5x-3x faster prefill by eliminating kernel launch overhead.

- Tags: performance
- Published: 2026-03-03

### [How Chunked Prefill Works in vLLM: Implementation Guide and Configuration Best Practices](/vllm-project/vllm/how-does-chunked-prefill-work-and-when-to-enable-it-in-vllm)

Learn how chunked prefill works in vLLM to process long prompts efficiently by splitting them into smaller chunks. Discover implementation details and configuration best practices for optimal performance.

- Tags: deep-dive
- Published: 2026-03-03

### [How to Use the OpenAI-Compatible API Server in vLLM: Complete Setup Guide](/vllm-project/vllm/how-to-use-the-openai-compatible-api-server-in-vllm)

Learn to set up vLLM's OpenAI-compatible API server for high-throughput LLM inference. Replace OpenAI's API seamlessly with vLLM's powerful backend.

- Tags: how-to-guide
- Published: 2026-03-03

### [How Prefix Caching Works in vLLM: Configuration and Implementation Guide](/vllm-project/vllm/how-does-prefix-caching-work-and-how-to-configure-it-in-vllm)

Learn how prefix caching in vLLM optimizes LLM inference by sharing KV-cache blocks. Discover configuration options for reduced compute overhead in prefill.

- Tags: how-to-guide
- Published: 2026-03-03

### [How the vLLM Scheduler Handles Request Prioritization and Preemption](/vllm-project/vllm/how-does-the-scheduler-handle-request-prioritization-and-preemption-in-vllm)

Discover how the vLLM scheduler prioritizes requests and preempts lower priority tasks when KV cache allocation fails. Learn about FCFS and priority scheduling policies.

- Tags: how-to-guide
- Published: 2026-03-03

### [How to Use Structured Outputs (JSON Schema, Regex, Grammar) in vLLM](/vllm-project/vllm/how-to-use-structured-outputs-json-schema-regex-grammar-in-vllm)

Learn how vLLM enforces structured outputs using JSON schema, regex, or grammars with token-level bitmasks for precise text generation.

- Tags: how-to-guide
- Published: 2026-03-03

### [How to Use LoRA Adapters and Multi-LoRA in vLLM for Efficient Fine-Tuning](/vllm-project/vllm/how-to-use-lora-adapters-and-multi-lora-in-vllm-for-efficient-fine-tuning)

Discover how to efficiently fine-tune models with LoRA adapters and Multi-LoRA in vLLM. Load adapters at runtime without reloading weights for faster deployment.

- Tags: how-to-guide
- Published: 2026-03-03

### [How Speculative Decoding in vLLM Accelerates Token Generation](/vllm-project/vllm/how-does-speculative-decoding-work-in-vllm-for-faster-token-generation)

Discover how speculative decoding in vLLM accelerates token generation. Learn how a draft model and rejection sampling boost speed N times with exact output distribution.

- Tags: deep-dive
- Published: 2026-03-03

### [How PagedAttention Works in vLLM to Maximize Memory Efficiency](/vllm-project/vllm/how-does-pagedattention-work-in-vllm-and-improve-memory-efficiency)

Discover how PagedAttention in vLLM optimizes GPU memory by eliminating copies and processing KV cache directly on compact buffers. Maximize your LLM's efficiency.

- Tags: internals
- Published: 2026-03-03