Search: “together-ai--x”

We’re back 🔥. Thrilled to be named once again to the @Forbes AI 50. The AI Native Cloud, built for the full AI lifecycle: fast inference, open models, fine-tuning at scale. https://www.forbes.com/list

@lmsysorg @sgl_project @vllm_project @CoreWeave @nebiusai @nscale @togethercompute @Togethercompute enables AI labs and enterprises to run frontier models and agents on the NVIDIA Blackwell platform—g

The edge inference implication: memory, not compute, is the binding constraint. Parcae opens a new axis. Scale quality by looping deeper, not by adding parameters. Bigger-model quality at smaller-mode

Training code and models are live on Hugging Face. Dan Fu (Together AI's VP of Kernels) led the work. Together AI provided compute. Blog: https://www.together.ai/blog/parcae Paper: https://arxiv.org/a

What if you could get 1.3B Transformer quality from a 770M model? That's not a compression result. It's a different architecture. Parcae, from @realDanFu (Together AI's VP of Kernels) and his lab at U

EinsteinArena is a platform where AI agents collaborate on open science problems — submitting solutions, posting in discussion threads, building on each other's constructions in real time. Agents just

EinsteinArena is an open-source benchmarking platform developed by Together AI designed to evaluate and rank AI models, with a live public leaderboard tracking model performance. The project welcomes

This isn't the only result. As of April 11, agents have set 11 new SOTA results on open problems including: → Erdős minimum overlap problem → Second autocorrelation inequality → Tammes problem (n=50)

MiniMax M2.7 is now available on Together AI, offering a powerful open-source model trained using a self-directed reinforcement learning loop. This training methodology enabled the model to achieve th

On software engineering: → 56.22% SWE-Pro, matching GPT-5.3-Codex → 55.6% VIBE-Pro — full-stack project delivery → SWE Multilingual: 76.5 For agentic work: → Multi-agent collaboration built into the m

MiniMax-M2 is a large-scale mixture-of-experts (MoE) language model available for inference on Together AI's platform, accessible via both serverless and dedicated infrastructure options. The model ca

Together AI maintains a public events page at [together.ai/events](https://www.together.ai/events) listing its past and upcoming appearances, spanning major industry conferences such as NVIDIA GTC,...

Together Computer (Together AI) participated in HumanX 2026, a major AI conference held April 6–9 in San Francisco, where they hosted activations including custom comics, branded hats, and a happy ...

Google's Gemma 4 31B is a dense multimodal model from Google DeepMind now available on Together AI's serverless infrastructure via the endpoint `google/gemma-4-31B-it`. It features a 256K context ...

Highlights: 👉 Configurable thinking mode for step-by-step reasoning 👉 Multimodal understanding with text and image input, including document parsing and OCR 👉 Native function calling with structured t

Introducing Gemma 4 31B from @GoogleDeepMind on Together AI. AI natives can now use Gemma 4 31B on Together and benefit from reliable inference for multimodal reasoning, tool use, and agentic workflow

GLM-5.1 is Z.ai's post-training upgrade to GLM-5, now available on Together AI, delivering a 28% coding performance improvement through a refined reinforcement learning pipeline while retaining the...

Highlights: 👉 28% coding improvement over GLM-5 with refined RL post-training 👉 Better long-horizon execution across hundreds of rounds and thousands of tool calls 👉 Thinking mode, tool calling, and s

Introducing GLM-5.1 from @Zai_org on Together AI. AI natives can now use GLM-5.1 on Together and benefit from reliable inference for production-scale agentic engineering and long-horizon coding workfl