From diffusion language models that break free from token-by-token generation to async batching that reclaims 25% of wasted GPU time, AI inference infrastructure is undergoing a fundamental transformation in 2026.
Agentic AI is no longer a research curiosity. It is a production reality, and the infrastructure underneath it is evolving faster than most teams can track.…
The AI revolution is shifting from training to inference. Explore how vLLM, TensorRT-LLM, and MLOps practices are reshaping computing infrastructure for the inference era.
A comprehensive comparison of vLLM, TensorRT-LLM, TGI, and SGLang—the four inference engines dominating AI infrastructure in 2026. Plus the MLOps tools and hardware trends shaping the serving landscape.
From 30x throughput gains with NVIDIA Dynamo to trillion-parameter Llama 4 models running on single GPUs, discover the infrastructure innovations defining AI production in 2025.
The AI infrastructure landscape has undergone a seismic shift in 2026. From vLLM and TGI to NVIDIA Blackwell B200 and agentic systems, explore the technologies defining production-ready AI at scale.
The AI infrastructure landscape of 2026: vLLM dominates inference, AMD and TPUs challenge NVIDIA, vector databases mature for RAG, and AI observability becomes essential for production ML systems.
How vLLM's PagedAttention innovation, multi-hardware support, and distributed parallelism strategies made it the dominant open-source LLM inference engine in 2026, delivering 2-4x throughput improvements.
When adding GPUs doesn't reduce latency, the problem isn't capacity—it's routing. Discover how llm-d's cache-aware scheduling delivers 57x faster TTFT and 2x throughput on the same hardware.
The vLLM Korea Meetup 2026, held in Seoul on April 2nd, delivered more than just technical presentations—it offered a window into how AI inference infrastructure is…
vLLM v0.19.0 brings full Google Gemma 4 architecture support, speculative decoding with zero-bubble async scheduling, and significant Model Runner V2 maturation for improved throughput and efficiency.
The latest vLLM release adds Google Gemma 4 architecture support with MoE, multimodal, and tool-use capabilities, plus breakthrough performance improvements through zero-bubble async scheduling.
The vLLM project releases v0.19.0 featuring Gemma 4 architecture support, zero-bubble async scheduling with speculative decoding, Model Runner V2 enhancements, and ViT full CUDA graph capture for improved inference performance.
vLLM v0.19.0 ships with Google Gemma 4 support, zero-bubble async scheduling with speculative decoding, Model Runner V2 improvements, and contributions from 197 developers.
vLLM v0.18.0 introduces production-ready gRPC serving and GPU-less preprocessing for multimodal workloads.
The vLLM project has released version 0.18.0, a substantial update featuring 445 commits from 213 contributors including 61 new contributors. This release significantly expands deployment flexibility…
vLLM 0.17 brings PyTorch 2.10, FlashAttention 4 support, and the new Nemotron 3 Super model, delivering next-generation attention performance for LLM inference.
vLLM 0.17.1 adds Nemotron 3 Super and, more importantly, patches several MoE and TRT-LLM edge cases. That is the real story: production LLM serving is still a game of backend-specific correctness, especially once MoE, FP8, and mixed execution paths enter the room.
vLLM 0.16.0 lands with async scheduling and full pipeline parallelism support, plus speculative decoding improvements. Here’s how to think about throughput, tail latency, and operational rollout.
vLLM v0.16.0 ships with a large set of changes and a fast-moving contributor base. To adopt it safely, treat it like an API platform: validate OpenAI-compat endpoints, scheduling behavior, and observability before a fleet-wide cutover.
