vLLM

vLLM is an open-source, high-throughput, and memory-efficient inference and serving engine designed for Large Language Models (LLMs). It optimizes the deployment of LLMs by addressing the primary bottleneck in LLM serving: the inefficient management of the KV (Key-Value) cache.

Key Technical Features

1. High Throughput and Low Latency

Through the reduction of memory waste, vLLM can accommodate much larger batch sizes within the same GPU memory constraints. This increased concurrency directly translates to higher throughput (tokens per second) across the system, even while maintaining acceptable latency for individual requests.

2. Efficient Memory Management

By utilizing a paged approach, vLLM achieves near-zero memory waste. The engine manages the lifecycle of KV cache blocks, allocating them as tokens are generated and deallocating them as sequences complete, ensuring optimal utilization of available VRAM.

3. Continuous Batching

Unlike traditional static batching, which waits for all requests in a batch to complete before starting a new batch, vLLM employs continuous batching (also known as iteration-level scheduling). This allows the engine to insert new requests into the batch as soon as existing requests complete an iteration, significantly reducing the "waiting" time for new sequences and increasing overall GPU utilization.

4. Broad Model Compatibility

vLLM supports a wide array of modern LLM architectures, including but not limited to:

5. Distributed Inference

vLLM supports distributed inference via Tensor Parallelism (TP). This allows a single model to be partitioned across multiple GPUs, enabling the deployment of much larger models that exceed the memory capacity of a single accelerator.

Deployment and Integration

vLLM is designed for production environments and provides several interfaces for integration: