The inaugural PyTorch Meetup Singapore brought together engineers, researchers, and community builders to talk about everything from vLLM project updates to the broader question of sovereign intelligence. Read the full technical recap and find presentation slides in our latest blog: bit.ly/4vdcPJU

🎉 Congrats to @MiniMax_AI on releasing MiniMax M3! Frontier coding and agentic capabilities, native image and video input, computer use, and a 1M-token context window, all in a single open model. At the heart of M3 is MSA, a new sparse attention architecture: instead of attending densely over the full KV cache, each query scores 128-token KV blocks and runs attention only over the top blocks. That is what makes 1M-token context practical to serve. M3 runs in vLLM with day-0 support, verified on NVIDIA and AMD hardware: ✨ MSA sparse attention with dedicated prefill and decode kernels ✨ 1M-token context serving with prefix caching and chunked prefill ✨ BF16 and MXFP8 checkpoints, with MoE backends for both Hopper and Blackwell ✨ Native multimodal input (image video) ✨ Tool calling, reasoning parsing, and thinking-mode control for agent workloads Day-0 support like this is a true team effort. Grateful to the teams at @MiniMax_AI, @NVIDIAAI, @AIatAMD, and @inferact, and to the vLLM community for making it happen. 🙏 Deep dive into the implementation, kernel work, and deployment recipes: 🔗 vllm.ai/blog/2026-06-12-mini…

MiniMax M3, Open-Weight, Now On Hugging Face , with only ~428B parameters and ~23B activated parameters Weights: huggingface.co/MiniMaxAI/Min… MiniMax Sparse Attention: huggingface.co/papers/2606.1…

Today we're excited to introduce vime — a simple, stable, and efficient RL framework for LLM post-training in the vLLM ecosystem. Built on slime's proven training design and powered by vLLM inference, vime brings another strong option to the growing vLLM post-training ecosystem. Our goal isn't a one-size-fits-all framework. We want users with different needs to find the right vLLM-ecosystem choice for their workflows—whether that's vime, NeMo RL, OpenRLHF, verl, or others. More choice. More interoperability. More innovation. Learn more: vllm.ai/blog/2026-06-09-anno… #LLM #RLHF #PostTraining #vLLM

Meet DiffusionGemma! An experimental open model that explores a fast approach to text generation, released under an Apache 2.0 license. Moving beyond sequential, token-by-token processes to generate entire blocks of text simultaneously. Here’s what’s new with DiffusionGemma: 👇

New short course: Fast & Efficient LLM Inference with vLLM, built in partnership with @RedHat and taught by @cedricclyburn. Learn to quantize an open-source LLM, serve it with vLLM, and benchmark your deployment across speed, cost, and accuracy. Free to enroll: hubs.la/Q04jXfpR0

Excited to share some of our work on improving vLLM for RL! A number of RL frameworks, including SkyRL, use vLLM for inference, and we’ve noticed some common problems: 1. Weight syncing between training and inference is implemented in an ad-hoc fashion and duplicated across frameworks. 2. Asynchronous RL is prone to break at scale, especially in P/D and DPEP deployments. We’ve been working on improving both!

🦀 The Rust frontend is officially merged into vLLM! As GPUs get faster, the frontend has become a real share of CPU time. The new Rust frontend is a drop-in alternative to the Python API server — same engine, same ZMQ boundary. Opt in with VLLM_USE_RUST_FRONTEND=1. Early numbers: on a preprocess-heavy workload, ~837 req/s vs ~162 req/s for default Python — ~5x in a single process. A few design choices we're excited about: • Layered crates with clear boundaries • Stream-native pipeline — non-streaming for free • Builds on stable Rust Huge thanks to @BugenZhao from @inferact for introducing the work at @PyTorch Meetup Singapore. github.com/vllm-project/vllm…

Introducing: Cohere Command A We’ve created our most powerful LLM yet, optimized it to run on as little hardware as possible, and released it open-source for all.

The shock came when on Day 0 DeepSeekv4 launch, since the community vLLM/SGLang maintainers only had access to NVIDIA GPUs, they were only able to add Day 0 NVIDIA GPU support. Since then, AMD has finally priotitzed with actions and just not words by contributing an 2.5million dollar 9 node Mi355X cluster with 8x400G Pollara NICs towards open source @vllm_project maintainers including (@EmbeddedLLM maintainers, @simon_mo_ and others) & an 1.1 million dollar dev 4 node MI355 dev cluster. 2\4

TurboQuant has drawn a lot of attention recently, but the accompanying evals didn't tell the full story. So we ran what I believe is the first comprehensive study of TurboQuant: where it helps, where it falls short, and how it impacts accuracy, latency, and throughput. Findings: