Accelerate inference, model shaping, and pre-training on a research-optimized platform.
San Francisco, CA
Joined November 2022
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Together AI retweeted
If you want your apps to be more unique, generate your own fonts!
These only cost $0.02 each with DeepSeek V4.1 Flash 1017 on @togethercompute.
Open sourcing all 36 fonts on Monday!
agentic rl means training across long, multi-turn interactions with tools
together post-training supports tool calls, async generation + the environment you already use
you define the environment and rewards. we run the training + generation
together.ai/post-training
🇺🇸 Introducing geo-specific serverless inference endpoints, starting with seven leading open models processed 100% in the U.S.
Switch your base URL to us-api.together.ai/v1
Same API key, same model IDs. 1.25x standard rates.
Learn more: togetherai.link/ZA0OzLW
jack & jill used together post-training to train a model that predicts which candidates a hiring manager will want to meet
in offline evals, they matched a closed-source model’s quality on an open-source model that’s significantly cheaper to serve
together.ai/post-training
Speech-to-speech models have traditionally sacrificed intelligence. Phonic-1 from @phonic_co is the most advanced speech-to-speech model for agentic tasks, running highly challenging calls across six continents.
Trained and served on Together AI. Congrats to the Phonic team!
Introducing Phonic-1, the most advanced speech-to-speech model for agentic tasks — from @phonic_co, the speech-to-speech model company.
We’ve had a breakout year and raised a $27.5M Series A led by @firstround with participation from @Lux_Capital, @felicis, and Definition.
Together AI retweeted
Rain couldn't stop the builders.
Great conversations at our Step 5 Preview Launch Night with @togethercompute!
From long-horizon agents and tool-calling reliability to inference costs and what AI breakthroughs mean for the next wave of startups.
Huge thanks to our panelist Dan Fu (@realDanFu · @togethercompute), Mar Hershenson (@MarHershenson · @pearvc), Saoud Rizwan (@sdrzn · @cline), and Joey Wang (@harvey), and everyone who joined us!
More to come.
long agent rollouts can get expensive fast
together post-training uses per-gpu pricing, so running more rollouts in parallel brings the cost of each one down
together.ai/post-training
together gpu clusters can now take a node from gpu fault to healthy again automatically
health checks identify the failure, the platform chooses the lightest repair, and you decide what can run unattended, down to individual xid codes
here’s how it works
together.ai/blog/auto-repair…
Two companies, one goal: open models that work for builders at any scale.
@jmorgan of @ollama and @vipulved of Together AI on where open models win, how to spend tokens wisely, and what developers want next.
📽️: youtube.com/watch?v=ZAGVED3p…
the open model stack is becoming composable
model → inference → gateway/router → harness → tools
each layer can evolve independently, so you can swap in better models, providers + tools as the ecosystem moves
we broke down the full stack ↓
together.ai/blog/the-open-so…
minimax m3.1 flash beats opus 5.5 on agentic automation at ~27x lower cost
the model has a 1m context window + vision support, and is built for coding, computer use + cybersecurity
We are LIVE with @AIatMeta to dive into Muse Spark Open!
Everything you need to know about Muse Spark Open: A conversation with Meta and Together AI nitter.cf/i/broadcasts/1MJgNbkXm…
Everything you need to know about Muse Spark Open: A conversation with Meta and Together AI nitter.cf/i/broadcasts/1MJgNbkXm…
post-training gets better the faster you can iterate
multiple lora experiments can share the same gpu capacity on together post-training, so you can test more ideas without spinning up new infrastructure for every run
together.ai/post-training
minimax m3.1 flash is now available for post-training on together ai
run sft or rl with your own data, rewards + environments, while keeping the training loop in your own python code
take m3.1 flash and shape it for the agentic workload you need
together.ai/post-training
Open models in production. Inference at real scale. Cost per token that makes finance smile.
That was the energy at GTC Berlin.
Customer and partner sessions, Kaffee Inferenz, Open Haus Berlin, and a night at Untergrund.
Thanks to every builder who came through.
Together AI retweeted
Great to see Factory using Reflection’s Beam model through Together AI.
A win-win for all three teams, and a great example of the open AI ecosystem working together.
factory got early access to beam through together before launch
they used that window to test @reflection_ai's model on real coding + agentic workflows, including tool-error recovery, conflict handling + final repo verification
by day 0, @FactoryAI already knew how beam behaved on the work that matters to their customers
factory got early access to beam through together before launch
they used that window to test @reflection_ai's model on real coding + agentic workflows, including tool-error recovery, conflict handling + final repo verification
by day 0, @FactoryAI already knew how beam behaved on the work that matters to their customers
Together AI retweeted
Everything you need to know about MiniMax M3.1 Flash → minimaxflash.com.
Now live on @togethercompute!
MiniMax M3.1 Flash weights are now public. 428B parameters total, 23B activated per token.
Agentic performance: A generational leap in agentic tasks, coding, and cybersecurity — 76.2 on BankerToolBench, 73.7% on DeepSWE v1.1, 61.7 on OSWorld V2, 29.6 on Agents' Last Exam, and 82.2 on CyberGym.
Efficient by design: MiniMax Sparse Attention and DSpark speculative decoding improve inference efficiency.
Hugging Face:
huggingface.co/MiniMaxAI/Min…
ModelScope:
modelscope.cn/models/MiniMax…
API: platform.minimax.io
M Plan: minimax.io/m-planMiniMax
MCode for desktop: agent.minimax.io/download
