vLLM

Recent developments

• 2026-09-09 — vLLM 0.29.0 defaults to Model Runner V2 for all models
• 2026-09-07 — vLLM bug allows incompatible LoRA adapters to load silently
• 2026-09-04 — vLLM boosts LLM inference speed with virtual memory paging
• 2026-09-01 — SGLang inference engine boosts LLM performance with token-level KV cache
• 2026-08-28 — Self-host LLMs with vLLM for 45% cost savings on cloud GPUs
• 2026-08-06 — New GRIT format aims to prevent silent bugs in quantized AI models

Frequently asked

How does vLLM achieve its high inference performance?

vLLM primarily achieves high performance through its PagedAttention algorithm, which optimizes GPU memory usage for KV caches. This technique, inspired by virtual memory paging, dynamically allocates memory blocks, significantly reducing waste and increasing throughput by 2x to 4x. The recent vLLM 0.29.0 update now defaults to Model Runner V2, further streamlining execution and memory handling. Additionally, continuous batching and chunked prefill contribute to its efficiency, making it ideal for high-throughput production environments.

What are the cost benefits of using vLLM for LLM serving?

Using vLLM for self-hosting LLMs on cloud GPUs can lead to substantial cost savings and enhanced data privacy. It provides an OpenAI-compatible API, native prefix caching, and structured output capabilities, making it a robust alternative to managed services. For instance, it enables efficient deployment of models like Gemma 4-E2B on single Google Cloud TPU v5e chips, demonstrating its capability for cost-effective, scalable AI agent systems while maintaining data sovereignty and predictability.

How does vLLM compare to other LLM serving engines like SGLang?

vLLM excels in high-throughput production environments, consistently delivering superior performance for concurrent users due to its advanced batching and memory management. However, SGLang, with its RadixAttention and token-level KV cache, demonstrates notable speedups (up to 5x) for specific workloads involving high prefix reuse, such as agent loops and RAG. While vLLM remains a strong general-purpose solution, SGLang offers specialized advantages for certain context-heavy applications, pushing vLLM to continuously innovate.

What recent developments in vLLM improve reliability and deployment flexibility?

Recent vLLM updates significantly improve reliability and deployment flexibility. The introduction of the GRIT format is crucial for preventing silent bugs in quantized models by explicitly defining numerical contracts. Version 0.29.0 defaults to Model Runner V2, streamlining execution and memory handling. Furthermore, integrations with Kubernetes via llm-d enhance autoscaling based on queue depth, and a new tuning guide helps users optimize vLLM's settings for specific workloads, ensuring robust and efficient deployments.

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