Search: “together-ai--x”

Congrats to the @cursor_ai team on Composer 2.5 — a huge milestone for agentic coding models. Together AI, the AI Native Cloud, is proud to partner on this launch. Composer 2.5 is pushing the frontier

Gemma-4-31B-it-Pearl supports 32K context, configurable thinking, function calling, and JSON mode. This is Together AI’s first Pearl-powered endpoint. Eventually, we plan to expand our Pearl powered p

Heading to hashtag#MLSys2026? Come unwind with the Together AI team at Inference After Dark. Drinks, bites, shuffleboard, and a room full of researchers and AI-native builders. 🟠 Tuesday, May 19 🟠 7:3

A milestone for Pearl Research Labs: our first major enterprise partnership is live with Together AI. @togethercompute’s inference platform is an ideal demonstration of @prlnet's value proposition — O

As inference workloads dominate, what if these matmuls could also perform useful work and generate beneficial byproducts!? Similar to all the byproducts we get from crude oil distillation that are not

Gemma-4-31B-it-Pearl supports 256K context, configurable thinking, function calling, and JSON mode. This is Together AI’s first Pearl-powered endpoint. Eventually, we plan to expand our Pearl powered

Gemma-4-31B-it-Pearl supports text and image input, 256K context, configurable thinking, function calling, and JSON mode. This is Together AI’s first Pearl-powered endpoint. Eventually, we plan to exp

Inference is becoming the largest compute market and energy consumer in AI. Pearl turns inference CapEx of hyperscalers into a profit center: every LLM token produced by GPUs can simultaneously genera

Introducing Gemma-4-31B-it-Pearl on Together AI, Pearl Research Labs’ instruction-tuned checkpoint of Gemma 4 31B powered by @prlnet Proof of Useful Work protocol. AI natives can now use this Pearl mo

Pearl generates proofs from matrix multiplications that already happen during training and inference computations. Those proofs help secure Pearl Network, and the future value of Pearl emissions helps

Introducing Rime Mist v3 on Together AI, a production TTS family built for deterministic pronunciation and controllable voice output. AI natives can now deploy @rimelabs Mist v3 on Together AI dedicat

🌟Introducing🎻Violin — an Open-source Video Translation Skill. 📹Video is the dominant medium on the internet, yet most high-quality content (lecture, talk, podcast) is locked behind a single language,

Mist v3 is available across two endpoints: 👉 rime-labs/rime-mist-v3 for English TTS 👉 rime-labs/rime-mist-v3-omni for multilingual TTS across English, Spanish, French, and German 👉 Custom pronunciatio

Together AI will present seven research papers at MLSys 2026, showcasing projects that demonstrate the transition from research to production applications. The announcement highlights the company's co

Together AI STT models now hold the top two spots for transcription speed on the @ArtificialAnlys Speech to Text leaderboard. NVIDIA Parakeet TDT 0.6B V3 on Together AI ranks #1, transcribing 303 seco

Try Rime Mist v3 in voice finder directly: https://findtherightvoice.com/rime-labs--rime-mist-v3 Model pages: http://www.together.ai/models/rime-mist-v3 http://www.together.ai/models/rime-mist-v3-omni

AutoScientist is an AI system developed by Together AI that automates the complete research workflow to enable model training and scientific discovery outside of well-resourced frontier laboratories.

💡 Why did @togethercompute choose NVIDIA Blackwell to serve DeepSeek-V4? Because NVIDIA Blackwell is built for the bottlenecks that matter most in long-context inference: → KV-cache pressure during de

Highlights: 👉 600+ voices across MiniMax, Cartesia, Deepgram, Rime, and more 👉 Search by prompt or audio sample 👉 Filter by 15+ attributes, including pitch, accent, language, age, emotion, and speakin

Introducing voice finder from Together AI, a new tool to search, filter, and audition 600+ voices across leading TTS models. AI natives can now find the right voice for their app faster by describing

Voice selection significantly impacts user perception and experience across various applications, including financial services support, healthcare intake processes, and entertainment platforms. Togeth

DeepSeek V4 Pro brings long-context reasoning and SOTA coding performance to Together AI serverless. The next layer is serving it efficiently: KV cache, prefix reuse, hybrid attention, batching, kerne

For browser-use AI agents, every task is dozens of model calls in a tight loop. The inference layer isn’t background infrastructure. It’s what the product runs on. @yutori_ai runs Scouts, Delegate, an

Read the full blog: https://www.together.ai/blog/serving-deepseek-v4-why-million-token-context-is-an-inference-systems-problem# Watch the full webinar on DeepSeek v4: https://www.youtube.com/watch?v=D

Excited to speak at PyCon US @pycon about production LLM inference systems, runtime optimization, and next-gen engine design — including some of the new ideas behind the recently open-sourced project

The gap between 'this looks cool' and 'I'm actually running it' used to be a day or two of setup. Not anymore. Deploy any Hugging Face model on the AI Native Cloud in a single session 👇🏼 https://www.t

Together AI has long been a proud supporter of open-source innovation in inference. We're excited for the new TokenSpeed inference engine, available in preview today with MIT licence, and can't wait t

Deepgram STT is now natively available on @togethercompute. One platform, full voice-agent stack: Deepgram transcription, Together-hosted LLMs, Aura-2 TTS. Sub-3-second round trip on reference builds.

Inference is 80-90% of the lifetime cost of a production AI system. Most AI-native teams are leaving performance and margin on the table. Here’s how Together AI, the AI Native Cloud, fixes that on @nv

On 5/5 @realDanFu and team will discuss DSV4’s hybrid attention and KV cache efficiency, should be a great session! Join us Tue 5/5: #DeepSeek-V4's hybrid attention + sparse MoE reduces KV cache up to

@profdanklein spent 20 years studying how language forms intelligence. When LLMs exploded, he saw something everyone missed. These systems were fluent, confident, and wrong. And no one could tell the

Together AI reported significant improvements after switching from AWS, achieving zero training-blocking failures, approximately 50% cost savings, and rapid issue resolution within hours through share

The AI industry is racing toward superintelligence. Dan and Scaled Cognition built something more : Super-Reliable Intelligence. 'The smartest model in the world is useless if it can’t get the answer

Adaptive Data is a Together AI tool designed to help teams improve the quality of datasets before fine-tuning language models by enabling analysis, adaptation, and refinement of training data. The pla

Join us Tue 5/5: #DeepSeek-V4's hybrid attention + sparse MoE reduces KV cache up to 90%, enabling 1M-token context. We'll cover why that makes it great for agentic workflows, what it took to serve at

Most model trainings outside of frontier labs fail. 📈 Because of bad or insufficient data. 🚮 Or just data for what you want + not general capabilities. 🎯 Most builders give up + become elevated prompt

We believe that intelligence should not arrive preconfigured. @togethercompute is now available directly inside the Adaption platform, connecting Adaptive Data with large-scale training in a single wo

We’re excited to partner with @adaption_ai to make Together Fine-Tuning natively available in Adaptive Data. AI natives can now move from optimized training data in Adaptive Data to fine-tuned open mo

With this integration, teams can optimize data, launch fine-tuning, evaluate results, and deploy on Together AI inference through a tighter workflow. Learn more: http://www.together.ai/blog/announcing

$3/million output tokens. Qwen 3.5 Plus is basically a frontier model. Let that sink in. Introducing Qwen3.6-Plus from @Alibaba_Qwen, a 1M-context model built for real-world agents, agentic coding, an

Highlights: 👉 1M context for long-horizon agentic workflows 👉 Stronger agentic coding across frontend, repo-level, and terminal-based tasks 👉 Multimodal reasoning across text, image, and video inputs

Introducing Qwen3.6-Plus from @Alibaba_Qwen, a 1M-context model built for real-world agents, agentic coding, and multimodal reasoning. AI natives can now use Qwen3.6-Plus on Together AI and benefit fr

Last week we announced DeepSeek-V4. Today we’re sharing a closer look at DeepSeek-V4 Pro on Together AI: 512K context, controllable reasoning modes, and cached-input pricing for long-context workloads

Qwen3.6-Plus, a large language model, is now available for use through Together AI's platform. Together AI has announced the availability of this model and is inviting users to try it via their models

DeepSeek V4 Pro is a language model available through Together AI's platform, with official quickstart documentation provided to help users get started with the model. The quickstart guide likely cove

@NVIDIA Nemotron 3 Nano Omni is now on Together AI. Enterprise multimodal AI — video, audio, image, documents & text — optimized for speed and scale. ✅ ~3B active params, 9x higher throughput ✅ Fully