vLLM with Ray Serve: Production Deployment Pattern
vLLM with Ray Serve: Production Deployment Pattern The most surprising thing about deploying large language models LLMs in production is how much of the.
vLLM Articles
50 articles
vLLM Request Priority Scheduling: Preempt Low-Priority Jobs
vLLM is doing something pretty wild with your LLM requests, and it's not just a simple queue. It's actively kicking lower-priority requests out of the w.
vLLM Speculative Decoding: Draft Model for Faster Generation
Speculative decoding lets a small, fast model generate tokens and then have a larger, more accurate model verify them, dramatically speeding up inferenc.
vLLM Tensor Parallelism: Multi-GPU Setup
Tensor parallelism splits a single large model layer across multiple GPUs, allowing you to run models that wouldn't fit on a single GPU or to speed up i.
vLLM Throughput and Latency Benchmarking: Measure Your Setup
vLLM is designed for high-throughput, low-latency LLM inference, but achieving optimal performance requires understanding and tuning your specific setup.
vLLM Tool Calling: Function Calling API Support
The most surprising thing about vLLM's tool calling support is that it's not a separate feature you enable, but rather a core capability that emerges fr.
vLLM Vision Language Models: Serve LLaVA and Qwen-VL
vLLM Vision Language Models: Serve LLaVA and Qwen-VL — practical guide covering vllm setup, configuration, and troubleshooting with real-world examples.
vLLM vs TensorRT-LLM: Compare Inference Frameworks
vLLM and TensorRT-LLM are both high-performance inference frameworks for large language models LLMs, but they approach optimization with different philo.
vLLM vs Triton: Choose the Right Serving Framework
vLLM and Triton are both powerful tools for serving large language models LLMs, but they target different needs and excel in different areas.
Fix vLLM CUDA Out of Memory During KV Cache Allocation
The vLLM OutOfMemoryError during KV cache allocation means the GPU ran out of VRAM to store the attention key-value states for the sequences being proce.
vLLM A100 vs H100 Performance: Throughput and Latency
vLLM A100 vs H100 Performance: Throughput and Latency — practical guide covering vllm setup, configuration, and troubleshooting with real-world examples.
vLLM API Server Authentication and Security
The vLLM API server, by default, doesn't enforce any authentication, meaning anyone who can reach its network endpoint can send requests and potentially.
vLLM Async Engine: Production Server Setup
The vLLM async engine lets you serve large language models with impressive throughput, but getting it into production feels like navigating a minefield .
vLLM Auto-Scaling on Kubernetes: HPA Configuration
The Horizontal Pod Autoscaler HPA in Kubernetes, when configured for vLLM, doesn't actually measure vLLM's inference throughput directly; it relies on u.
vLLM Batch Inference: Offline Processing at Scale
vLLM can process requests much faster than traditional methods because it runs inference in batches, which is essentially a way of grouping multiple req.
vLLM Best-of-N Sampling and Beam Search Config
vLLM's Best-of-N sampling and beam search aren't just about picking the "best" next token; they're a sophisticated dance of exploration and exploitation.
vLLM Chat Completions API: OpenAI-Compatible Integration
The most surprising thing about vLLM's OpenAI-compatible API is that it's not just compatible, it often outperforms OpenAI's own models in terms of raw .
vLLM Chunked Prefill: Tune for Throughput and TTFT
vLLM Chunked Prefill: Tune for Throughput and TTFT — practical guide covering vllm setup, configuration, and troubleshooting with real-world examples.
vLLM Continuous Batching: Maximize GPU Throughput
Continuous batching allows vLLM to process requests much faster by not waiting for all requests in a batch to finish before starting new ones.
vLLM Cost Per Token Optimization: Reduce Inference Cost
vLLM's memory management is the hidden driver of its cost-efficiency, specifically its PagedAttention mechanism, which treats GPU memory like virtual me.
vLLM CPU Offloading: Run Large Models with Less GPU RAM
vLLM's CPU offloading lets you run models that are too big for your GPU by cleverly moving parts of the model's weights to CPU RAM.
vLLM CUDA Graph Optimization: Reduce Kernel Launch Overhead
vLLM's CUDA graph optimization is a technique to significantly reduce the overhead associated with launching kernels on the GPU, particularly for repeti.
vLLM Custom Model Integration: Add Your Own Architecture
vLLM Custom Model Integration: Add Your Own Architecture — practical guide covering vllm setup, configuration, and troubleshooting with real-world examp...
vLLM Custom Sampling Parameters: Temperature, Top-P, Top-K
The most surprising thing about vLLM's custom sampling parameters is that topp and topk aren't just random filters, but rather a pair of mechanisms that.
vLLM Disaggregated Prefill and Decode: Separate Phases
The most surprising thing about vLLM's disaggregated prefill and decode is that they are fundamentally different computational problems, and treating th.
vLLM Docker and Kubernetes Production Deployment
vLLM, the lightning-fast inference engine, can be a bit prickly when you try to wrangle it into production with Docker and Kubernetes.
vLLM Embedding Models: Serve and Query Embeddings
You can serve and query embedding models with vLLM, and the most surprising thing is how little you need to change from serving a text-generation model .
vLLM Fine-Tuned Adapter Serving: LoRA and Full Finetunes
The most surprising thing about serving fine-tuned LLMs with vLLM is that sometimes, the smaller adapter weights like LoRA can actually be slower to loa.
vLLM FP8 Quantization: H100 Native FP8 Inference
The H100's FP8 tensor cores unlock a new level of inference throughput, but getting them to hum involves understanding a few key constraints.
vLLM GGUF Loading: Serve Quantized GGUF Models
Quantized GGUF models can actually load and run faster than their unquantized counterparts, even though they use less memory.
vLLM GPU Memory Utilization: Configure KV Cache Size
vLLM GPU Memory Utilization: Configure KV Cache Size — practical guide covering vllm setup, configuration, and troubleshooting with real-world examples.
vLLM gRPC Server: Low-Latency Inference Endpoint
vLLM's gRPC server doesn't just serve model outputs; it's a highly optimized pipeline designed to minimize the wall-clock time between a request arrivin.
vLLM Guided Decoding: Constrain Output to JSON Schema
Guided decoding in vLLM lets you force an LLM to produce output that strictly conforms to a predefined structure, like JSON.
vLLM Health Checks and Prometheus Metrics
vLLM is a surprisingly efficient inference engine, but its internal workings can feel like a black box, especially when it comes to monitoring.
vLLM Installation: GPU Quickstart Guide
vLLM can actually run faster than Hugging Face Transformers on a single GPU, even for tasks you might think would be CPU-bound.
vLLM Llama, Mistral, Mixtral: Model Serving Guide
Serving large language models efficiently is a surprisingly complex dance between hardware, software, and the model's own architecture.
vLLM Load Balancing: Route Across Multiple Replicas
The most surprising thing about vLLM load balancing is that it's not just about spreading requests evenly; it's about predicting which replica can finis.
vLLM LoRA Serving: Dynamically Swap Multiple Adapters
vLLM can dynamically swap multiple LoRA adapters on a single model instance, letting you serve many fine-tuned variations without spinning up new GPUs.
vLLM Marlin and AWQ: Fastest INT4 Quantization
Marlin and AWQ are two cutting-edge techniques that let you run large language models LLMs on less hardware by quantizing their weights to INT4, but the.
vLLM max_model_len: Configure Context Length
vLLM's maxmodellen isn't just about how much text a model can process, it's about how much context it can hold across multiple turns.
vLLM Mixtral MoE: Expert Parallelism Setup
Expert parallelism in vLLM for Mixtral MoE models isn't just about distributing experts; it's about orchestrating a symphony of specialized computation .
vLLM Model Warm-Up: Eliminate Cold Start Latency
The primary reason vLLM feels sluggish on its first request is that it's not just loading weights; it's meticulously preparing its internal memory struc.
vLLM Monitoring: P99 Latency Dashboards
vLLM's P99 latency dashboards are often misunderstood because they don't just measure request processing time; they also include the time spent waiting .
vLLM Multi-Node Distributed Serving: Scale Across Hosts
vLLM's multi-node serving is how you stop thinking about fitting your massive LLM into a single GPU's memory and start thinking about fitting it across .
vLLM OpenAI-Compatible Server: Drop-In API Replacement
The vLLM OpenAI-compatible server is so good at being a drop-in replacement for OpenAI's API that you can often forget it's not actually OpenAI.
vLLM PagedAttention: Memory Management Explained
PagedAttention is a memory management system for large language models that achieves near-optimal memory utilization by treating GPU memory like virtual.
vLLM Pipeline Parallelism: Serve Models Too Big for One GPU
Pipeline parallelism in vLLM is a technique that lets you serve large language models that wouldn't fit into a single GPU's memory by splitting the mode.
vLLM Prefix Caching: Reuse KV Cache for Common Prefixes
The most surprising thing about vLLM's prefix caching is that it doesn't just save computation, it fundamentally changes the cost structure of serving l.
vLLM Production Checklist: Config, Security, Monitoring
A vLLM deployment isn't just about loading a model; it's a distributed system where the inference server, the model weights, and the client requests are.
vLLM Quantization: AWQ, GPTQ, and INT4 Inference
Quantization isn't just about making models smaller; it's a sophisticated form of lossy compression that fundamentally alters the model's weights to all.