Efficient LLM Inference with vLLM | Generated by AI
vLLM: Efficient LLM Inference and Serving
vLLM is an open-source library designed for fast, memory-efficient inference and serving of large language models (LLMs). Developed initially at UC Berkeley’s Sky Computing Lab, it’s now a community-driven project used widely in production for deploying LLMs like Llama or GPT variants. Its core innovation is PagedAttention, a technique that treats key-value (KV) cache memory like virtual memory pages, reducing waste and enabling higher throughput by dynamically allocating non-contiguous blocks.
How It Works
- Continuous Batching: Unlike traditional systems that wait for full batches, vLLM dynamically adds/removes requests mid-execution, minimizing idle GPU time during decoding.
- Memory Management: PagedAttention avoids fragmentation in the KV cache (which grows with sequence length), supporting longer contexts without OOM errors.
- Optimized Execution: Uses CUDA/HIP graphs for faster kernel launches, integrated with FlashAttention/FlashInfer for attention computation, and supports quantization (e.g., AWQ, GPTQ, FP8) to cut memory use by up to 4x.
- Advanced Features: Includes speculative decoding (to guess tokens and verify), chunked prefill (for long inputs), multi-LoRA (adapting models on-the-fly), and distributed parallelism (tensor, pipeline, expert).
vLLM exposes an OpenAI-compatible API server, integrates seamlessly with Hugging Face models, and runs on diverse hardware (NVIDIA/AMD/Intel GPUs, TPUs, CPUs). It’s ideal for high-throughput scenarios, achieving 2-10x speedups over baselines like Hugging Face Transformers in serving benchmarks.
Key Use Cases
- Online serving for chatbots or APIs with streaming outputs.
- Offline batch inference for tasks like summarization.
- Scaling to multi-GPU clusters without custom plumbing.
Ray: Unified Framework for Scaling AI and Python Apps
Ray is an open-source distributed computing framework that makes it easy to scale Python code—especially AI/ML workloads—from a single machine to massive clusters. Created by Anyscale (with UC Berkeley roots), it abstracts away distributed systems complexities like scheduling, fault tolerance, and orchestration, letting developers focus on logic.
Main Components
- Ray Core: The foundation—Pythonic primitives for tasks (parallel functions), actors (stateful services), and objects (distributed data sharing). It handles autoscaling, retries, and resource allocation automatically.
- Ray AI Libraries: Domain-specific tools built on Core:
- Ray Data: Scalable ETL for preprocessing datasets.
- Ray Train: Distributed training with integrations (PyTorch, TensorFlow, Hugging Face).
- Ray Tune: Hyperparameter optimization at scale.
- Ray Serve: Model deployment for inference, with routing, batching, and A/B testing.
- RLlib: Reinforcement learning toolkit.
- Ray Clusters: Managed infrastructure layer for deploying on clouds (AWS, GCP), Kubernetes, or on-prem, with autoscaling based on demand.
How It Works
Ray runs as a daemon on nodes, forming a cluster with a head node for coordination. You decorate functions with @ray.remote for parallelism, and it distributes execution across CPUs/GPUs. For ML, libraries like Serve handle HTTP endpoints and load balancing, while Core ensures fault-tolerant scaling (e.g., restarting failed tasks).
Key Use Cases in ML/AI
- End-to-end pipelines: Data loading → training → tuning → serving.
- Hyperparameter sweeps on 100s of GPUs.
- Real-time inference fleets, like in recommendation systems.
- Custom distributed apps, e.g., simulations or simulations.
Ray’s strength is its unified API: Write once, scale anywhere, with low boilerplate compared to Spark or Dask.
Relation Between vLLM and Ray
In systems like SLOs-Serve (from our earlier discussion), vLLM handles the low-level LLM batching/inference, while Ray orchestrates multi-replica deployments, routing requests across nodes for burst handling and scaling.
vLLM Documentation
Ray Documentation