Day-0 support for @MiniMax_AI M3.1 Flash is live in TokenSpeed. DSpark speculative decoding and MXFP8 + NVFP4 mixed precision on Blackwell. TokenSpeed is featured in the official deployment guide. 📖 huggingface.co/MiniMaxAI/Min… 👉 github.com/lightseekorg/toke…

minimax m3.1 flash is now live on together ai we’re excited to be a day-0 launch partner for the model, aka space bunny m3.1 flash is built for agentic tasks, coding, computer use + cybersecurity, with a 1m context window and vision support try it now together.ai/models/minimax-m…

Today, we are announcing a strategic partnership with @MiniMax_AI to advance frontier production inference for open-source models. The Inferact Platform has served MiniMax M3.1 Flash in production since the day it launched. Requests through the MiniMax platform are routed to our stack alongside MiniMax’s own capacity, after we met MiniMax’s vendor verification requirements for accuracy and reliability. The Inferact Platform serves this agentic workload with guaranteed SLAs and auto-tunes the endpoint against live traffic, while MiniMax keeps control of how its model is served. Inferact also leads day-0 @vllm_project support for each MiniMax release, so the model serves at production quality the day the weights ship. The engineering from both efforts is upstreamed to vLLM. Thanks to the MiniMax team for the partnership. Read the full announcement: inferact.ai/news/minimax-par…

Space Bunny is here! 🐰 MiniMax M3.1 Flash hits >1K tok/s, bs1 decode with SGLang, production-ready from Day 0. This integration is a joint effort by @MiniMax_AI, @NVIDIAAI, @AMD, and @radixark, and you can also run RL with Miles! -Broad hardware support: NVIDIA Blackwell and Hopper (GB300/B300/B200/H200), plus AMD Instinct MI350X and MI355X. -Agentic serving at scale: ~100K total tok/s per GPU on the AgentX coding-agent trace with 96 concurrent sessions. -Speculative decoding: Supports MiniMax's DSpark draft and a DFlash2 draft trained by @radixark; supports HiCache and multimodal input. -Ultra-low-latency inference: MegaKernels deliver 2.15× faster single-request decode.

Space Bunny is here! 🐰 MiniMax M3.1 Flash hits >1K tok/s, bs1 decode with SGLang, production-ready from Day 0. This integration is a joint effort by @MiniMax_AI, @NVIDIAAI, @AMD, and @radixark, and you can also run RL with Miles! -Broad hardware support: NVIDIA Blackwell and Hopper (GB300/B300/B200/H200), plus AMD Instinct MI350X and MI355X. -Agentic serving at scale: ~100K total tok/s per GPU on the AgentX coding-agent trace with 96 concurrent sessions. -Speculative decoding: Supports MiniMax's DSpark draft and a DFlash2 draft trained by @radixark; supports HiCache and multimodal input. -Ultra-low-latency inference: MegaKernels deliver 2.15× faster single-request decode.

You’ve probably already used it: Space Bunny pushed 200T+ tokens before anyone knew it was MiniMax-M3.1-Flash. Now it’s open weights 🔥 Thank you @MiniMax_AI for open sourcing it! Day-0 fine-tuning in NVIDIA NeMo-AutoModel ✅ FSDP2, expert, pipeline, and context parallelism, ✅scaling up to 128 GPUs, and the NVFP4 checkpoint loads directly. github.com/NVIDIA-NeMo/Autom…

Today, we are announcing a strategic partnership with @MiniMax_AI to advance frontier production inference for open-source models. The Inferact Platform has served MiniMax M3.1 Flash in production since the day it launched. Requests through the MiniMax platform are routed to our stack alongside MiniMax’s own capacity, after we met MiniMax’s vendor verification requirements for accuracy and reliability. The Inferact Platform serves this agentic workload with guaranteed SLAs and auto-tunes the endpoint against live traffic, while MiniMax keeps control of how its model is served. Inferact also leads day-0 @vllm_project support for each MiniMax release, so the model serves at production quality the day the weights ship. The engineering from both efforts is upstreamed to vLLM. Thanks to the MiniMax team for the partnership. Read the full announcement: inferact.ai/news/minimax-par…

MiniMax M3.1 Flash: Space Performance + Bunny Speed 🚀🐰 Yes, Space Bunny - which has topped global leaderboards with 200T+ tokens - was an earlier version of M3.1 Flash. 💰Redefining the performance economics of agentic AI. M3.1 Flash achieves frontier-level intelligence across key tasks such as Cowork (Automation-Bench: 62%; ALE: 30%), Coding (DeepSWE: 74%) and Visual Reasoning (BabyVision: 91%), at 1/3 the cost of M3 and 3x the speed. API pricing (flat rate): $0.15 / $0.60 per 1M input/output tokens, with cache reads at $0.015 / 1M tokens. Model weights will arrive on Oct 23. API: platform.minimax.io M Plan: minimax.io/m-plan MiniMax Code for desktop: agent.minimax.io/download

Inferact is an API launch partner for MiniMax M3.1 Flash! 🎉 We're working with @MiniMax_AI on production inference for its latest model, built for multi-step automation, real-codebase engineering and visual reasoning. M3.1 Flash scored 29.6 on Agents’ Last Exam (ALE), alongside 72.5% on AutomationBench-AA and 73.67% on DeepSWE—capabilities that matter when agents need to plan work, use tools and carry tasks through. Our contribution is the serving stack: bringing our vLLM engineering and the Inferact Platform to the collaboration. Thanks to the MiniMax team for the partnership. Explore the model and API integration: platform.minimax.io/docs/gui…

Today we launch SGLang-TorchTPU, built with @Google TPU team and Meta @PyTorch team. We’re excited to see it powering production inference at Meta! SGLang-TorchTPU is a TPU plugin for SGLang that brings: - Top open models on TPU: Muse Spark Open, GLM-5.3 Flash, Kimi K3, Ling 3.1 Flash, MiniMax H3 - SGLang’s production features for large agentic workloads - Custom MegaKernel: Muse Spark Open hits 770 tok/s decode at bs=1 on InferenceX 8K/1K SGLang-TorchTPU is available now: github.com/sgl-project/sglan…

We just tried @MiniMax_AI's M3.1 Flash in our SGLang Omni cascaded voice interaction system, and the experience was genuinely impressive! 🐰🚀 Our original pipeline was Qwen3-ASR → Qwen3 LLM → Qwen3-TTS. We replaced the locally hosted LLM with the MiniMax M3.1 Flash API while keeping the rest of the pipeline unchanged, and recorded a demo to see how it performs in real conversations. The results were excellent. M3.1 Flash responded incredibly fast, understood our questions well, and consistently delivered sharp, concise, and direct answers. The conversation felt remarkably smooth and natural. One thing I increasingly appreciate about voice agents is that intelligence alone isn't enough. Unlike text, users can't easily skim an audio response. A model that understands what you need and gets straight to the point can make a huge difference to the overall interaction experience. This is also what makes the modular architecture of SGLang Omni so exciting to me. We can swap individual components, connect local speech models with frontier LLM APIs, and immediately explore how different models change the end-to-end interaction experience. Huge congrats to the MiniMax team! Really impressive work, and we're excited to explore more together. 🔥