SGLang v0.4
❖ We’re excited to announce the release of SGLang v0.4, featuring significant performance improvements and new features: Zero-overhead batch scheduler: 1.1x increase in throughput. Cache-aware load balancer: up to 1.9x increase in throughput with 3.8x higher cache hit rate. Data parallelism attention for DeepSeek models: up to 1.9x decoding throughput improvement. Fast structured outputs with xgrammar: up to 10x faster.
Speculative Decoding in vLLM
❖ Speculative decoding in vLLM is a powerful technique that accelerates token generation by leveraging both small and large models in tandem. In this blog, we’ll break down speculative decoding in vLLM, how it works, and the performance improvements it brings. This content is based on a session from our bi-weekly vLLM Office Hours, where we discuss techniques and updates to optimize vLLM performance.
vLLM v0.6
❖ vLLM achieves 2.7x higher throughput and 5x faster TPOT (time per output token) on Llama 8B model, and 1.8x higher throughput and 2x less TPOT on Llama 70B model. A month ago, we released our performance roadmap committing to performance as our top priority. We will start by diagnosing the performance bottleneck in vLLM previously. Then we will describe the solution we implemented and landed in the past month. Finally, we will showcase the benchmarks of the latest vLLM release v0.6.0 other inference engines.
SGLang v0.3
❖ We’re excited to announce the release of SGLang v0.3, which brings significant performance enhancements and expanded support for novel model architectures. Here are the key updates: Up to 7x higher throughput for DeepSeek Multi-head Latent Attention (MLA). Up to 1.5x lower latency with torch.compile on small batch sizes. Support for interleaved text and multi-image/video in LLaVA-OneVision. Support for interleaved window attention and 2x longer context length in Gemma-2.
FlashAttention-3
❖ Attention, as a core layer of the ubiquitous Transformer architecture, is a bottleneck for large language models and long-context applications. FlashAttention (and FlashAttention-2) pioneered an approach to speed up attention on GPUs by minimizing memory reads/writes, and is now used by most libraries to accelerate Transformer training and inference. This has contributed to a massive increase in LLM context length in the last two years, from 2-4K (GPT-3, OPT) to 128K (GPT-4), or even 1M (Llama 3).
SGLang
❖ Large Language Models (LLMs) are increasingly utilized for complex tasks that require multiple chained generation calls, advanced prompting techniques, control flow, and interaction with external environments. However, there is a notable deficiency in efficient systems for programming and executing these applications. To address this gap, we introduce SGLang, a Structured Generation Language for LLMs. SGLang enhances interactions with LLMs, making them faster and more controllable by co-designing the backend runtime system and the frontend languages.
DeepSpeed-FastGen
❖ Large language models (LLMs) like GPT-4 and LLaMA have emerged as a dominant workload in serving a wide range of applications infused with AI at every level. While frameworks like DeepSpeed, PyTorch, and several others can regularly achieve good hardware utilization during LLM training, the interactive nature of these applications and the poor arithmetic intensity of tasks like open-ended text generation have become the bottleneck for inference throughput in existing systems.
vLLM
❖ LLMs promise to fundamentally change how we use AI across all industries. However, actually serving these models is challenging and can be surprisingly slow even on expensive hardware. We are excited to introduce vLLM, an open-source library for fast LLM inference and serving. vLLM utilizes PagedAttention, our new attention algorithm that effectively manages attention keys and values. vLLM redefines the new state of the art in LLM serving: it delivers up to 24x higher throughput than HuggingFace Transformers, without requiring any model architecture changes.