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MiniMax M3

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Kimi K3

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Qwen3.8-2.4T-A95B

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NVIDIA Nemotron 3.5 Lightning

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Muse Glimmer 30B

Video

ByteDance Seedance 2.5

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DeepSeek V4 Pro 0813

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GLM-5.3

new

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GLM-5.3-Flash

new

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DeepSeek V4.1 Flash

new

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Tev1 4B Experimental

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Gemma 4 31B

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DeepSeek V4 Pro

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NVIDIA Nemotron 3 Ultra

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Qwen3.7-Plus

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GLM-5.2

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GPT Image 2

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ByteDance Seedance 2.0

Free

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Ternary Bonsai 27B

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The Together AI Platform

Powering every step of the AI development journey —from experimentation to massive scale.

Inference
Compute
Model shaping

- Serverless Inference
  The fastest way to run open-source models on demand. Powered by cutting-edge inference research. No infrastructure to manage, no long-term commitments.
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- Batch Inference
  Cost-effectively process massive workloads asynchronously. Scale to 30 billion tokens per model with any serverless model or private deployment.
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- Provisioned Throughput
  Committed inference capacity with token-based pricing, reserved throughput, and a 99% uptime SLA. Drop-in API compatibility for production workloads with no infrastructure to manage.
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- Dedicated Model Inference
  Deploy models on dedicated infrastructure. Purpose-built for teams who need speed, control, and the best economics in the market.
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- Dedicated Container Inference
  GPU infrastructure purpose-built for generative media workloads. Deploy video, audio, and image models with performance acceleration powered by Together Research.
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- Accelerated Compute
  Scale from self-serve instant clusters to thousands of GPUs, all optimized for better performance with Together Kernel Collection.
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- Sandbox
  Use fast, secure code sandboxes at scale to set up full-scale development environments for AI apps and agents.
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- Managed Storage
  High-performance managed storage for AI-native workloads. Object storage and parallel filesystems optimized for AI, with zero egress fees.
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import { CodeSandbox } from "@codesandbox/sdk" const sdk = new CodeSandbox() const sandbox = await sdk.sandboxes.create({ id: "node-template" }) const client = await sandbox.connect() await client.commands.run("npm install && npm run build") const previewUrl = client.hosts.getUrl(3000) await sdk.sandboxes.hibernate(sandbox.id) // Snapshot saved — resume anytime in <1s
- Fine-Tuning
  Fine-tune open-source models for production workloads, using the latest research techniques. Improve accuracy, reduce hallucinations, and control behavior — without managing training infrastructure.
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Grounded in cutting-edge research

Foundational systems research for production AI.

Kernels

To Infinity and Beyond: ThunderKittens Now on NVIDIA Vera Rubin NVL72!

Dylan Lim, Xinyi Li, Sonny Li, Peter Wu, Dan Fu, Simran Arora

Agents

ThunderAgent: 2x Faster Agentic Inference for Synthetic Data Generation at Scale

Hao Kang, Ziyang Li, Weili Xu, Xinyu Yang, Yinfang Chen, Junxiong Wang, Beidi Chen, Tushar Krishna, Chenfeng Xu, Simran Arora.

Kernels

Together AI at ICML 2026: frontier research across the full stack

Together Research

Kernels

ParallelKernelBench: Frontier LLMs can't write fast multi-GPU kernels (yet)

Willy Chan, Nathan Paek, Simon Guo, Simran Arora, Daniel Y. Fu

Agents

Violin: An open-source video translation skill that breaks language barriers

Shang Zhu, Kevin Qinghong Lin (Oxford), James Zou

Inference

Accelerate RL rollouts by up to 50% with distribution-aware speculative decoding

Zelei Shao, Vikranth Srivatsa, Sanjana Srivastava, Qingyang Wu, Alpay Ariyak, Xiaoxia Wu, Ameen Patel, Jue Wang, Percy Liang, Tri Dao, Ce Zhang, Yiying Zhang, Ben Athiwaratkun, Chenfeng Xu, Junxiong Wang

Architecture

Parcae: Doing more with fewer parameters using stable looped models

Hayden Prairie, Zachary Novack, Taylor Berg-Kirkpatrick, Dan Fu

Agents

EinsteinArena: Harnessing the collective intelligence of agents in the wild to advance science

Federico Bianchi,* Yongchan Kwon,* James Zou

Agents

AI for Systems: Using LLMs to Optimize Database Query Execution

Mehmet Hamza Erol, Xiangpeng Hao, Federico Bianchi, Ciro Greco, Jacopo Tagliabue, James Zou

Kernels

Inside the Together AI kernels team

Will Van Eaton

Inference

Aurora

Junxiong Wang, Fengxiang Bie, Jisen Li, Zhongzhu Zhou, Zelei Shao, Yubo Wang, Yinghui Liu, Qingyang Wu, Avner May, Sri Yanamandra, Ce Zhang, Tri Dao, Percy Liang, Shuaiwen Leon Song, Ben Athiwaratkun, Chenfeng Xu, Xiaoxia Wu

Agents

Plan, divide, and conquer: How weak models excel at long context tasks

Zhen Xu, Shang Zhu, Jue Wang, Junlin Wang, Ben Athiwaratkun, Chi Wang, James Zou, Ce Zhang

Architecture

Mamba-3

Aakash Lahoti* (CMU), Kevin Y. Li* (CMU), Berlin Chen* (Princeton), Caitlin Wang* (Princeton), Aviv Bick (CMU), J. Zico Kolter (CMU), Tri Dao (Princeton, Together AI), Albert Gu (CMU, Cartesia A)

Kernels

Key research and product announcements at the AI Native Conf

Kernels

FlashAttention-4: Algorithm and Kernel Pipelining Co-Design for Asymmetric Hardware Scaling

Ted Zadouri (Princeton University, Together AI), Markus Hoehnerbach (Meta), Jay Shah (Colfax Research), Timmy Liu (NVIDIA), Vijay Thakkar (Meta, Georgia Tech), Tri Dao (Princeton University, Together AI)

Inference

Cache-aware prefill–decode disaggregation (CPD) for up to 40% faster long-context LLM serving

Jiejing Zhang, Yubo Wang, Yinghui Liu, Mourya Vangala Srinivasa, Chenxi Li, Jue Wang, Yineng Zhang, Shuaiwen Leon Song, Ce Zhang

Agents

CoderForge-Preview: SOTA open dataset for training efficient coding agents

By Alpay Ariyak*, Junda Zhang, Junxiong Wang, Shang Zhu, Federico Bianchi, Sanjana Srivastava, Ashwinee Panda, Siddhant Bharti, Chenfeng Xu, John Heo, Xiaoxia Shirley Wu, James Zou, Percy Liang, Leon Song, Ce Zhang, Ben Athiwaratkun, Zhongzhu Zhou*, Qingyang Wu* *Project Core Leads

Agents

How speech models fail where it matters the most and what to do about it

Kaitlyn Zhou, Martijn Bartelds, Federico Bianchi, James Zou

Inference

Consistency diffusion language models: Up to 14x faster inference without sacrificing quality

Minseo Kim, Chenfeng Xu, Coleman Richard Charles Hooper, Harman Singh, Ben Athiwaratkun, Ce Zhang, Kurt Keutzer, Amir Gholami | Seoul National University, University of California, Berkeley, Together AI

Agents

What do LLMs think when you don't tell them what to think about?

Yongchan Kwon and James Zou

Agents

DSGym: A holistic framework for evaluating and training data science agents

Fan Nie, Junlin Wang, Harper Hua, Federico Bianchi, Yongchan Kwon, Zhenting Qi, Owen Queen, Shang Zhu, James Zou

Kernels

Research POV: Yes, AGI Can Happen – A Computational Perspective

Together AI

Model Shaping

How to run TorchForge reinforcement learning pipelines in the Together AI Native Cloud

Together AI Training and Research, The PyTorch team at Meta

Model Shaping

Introducing AutoJudge: Streamlined inference acceleration via automated dataset curation

Roman Garipov, Fedor Velikonivtsev, Ivan Ermakov, Ruslan Svirschevski, Vage Egiazarian, Max Ryabinin

Agents

Large Reasoning Models Fail to Follow Instructions During Reasoning: A Benchmark Study

Yongchan Kwon, Shang Zhu, Federico Bianchi, Kaitlyn Zhou, James Zou

Inference

AdapTive-LeArning Speculator System (ATLAS): A New Paradigm in LLM Inference via Runtime-Learning Accelerators

Junxiong Wang, Shirley Wu, Zelei Shao, Vikranth Srivatsa, Jue Wang, Roy Yuan, Qingyang Wu, Alpay Ariyak, Rupert Wu, Wai Tong Chung, Chenfeng Xu, Yonatan Oren, Pragaash Ponnusamy, Yineng Zhang, Avner May, Leon Song, Tri Dao, Percy Liang, Ce Zhang, Ben Athiwaratkun

Agents

How Together AI Uses AI Agents to Automate Complex Engineering Tasks: Lessons from Developing Efficient LLM Inference Systems

Shang Zhu, Federico Bianchi, Wai Tong Chung, Zain Hasan, Rupert Wu, Ce Zhang, James Zou, Ben Athiwaratkun

Agents

Back to The Future: Evaluating AI Agents on Predicting Future Events

Federico Bianchi, Junlin Wang, Zain Hasan, Shang Zhu, Roy Yuan, Clémentine Fourrier, James Zou

Inference

DeepSWE: Training a Fully Open-sourced, State-of-the-Art Coding Agent by Scaling RL

Michael Luo*, Naman Jain*, Jaskirat Singh*, Sijun Tan*, Ameen Patel*, Qingyang Wu*, Alpay Ariyak*, Colin Cai*, Tarun Venkat, Shang Zhu, Ben Athiwaratkun, Manan Roongta, Ce Zhang, Li Erran Li, Raluca Ada Popa, Koushik Sen, Ion Stoica

Agents

From Zero to One: Building An Autonomous and Open Data Scientist Agent from Scratch

Federico Bianchi, Shang Zhu, Zain Hasan, Ben Athiwaratkun and James Zou

Inference

Model-Preserving Adaptive Rounding with YAQA

Albert Tseng, Zhaofeng Sun, and Chris De Sa

Agents

Mixture-of-Agents Alignment: Harnessing the Collective Intelligence of Open-Source LLMs to Improve Post-Training

Junlin Wang, Roy Xie, Shang Zhu, Jue Wang, Ben Athiwaratkun, Bhuwan Dhingra, Shuaiwen Leon Song, Ce Zhang, James Zou

Inference

Boosting DeepSeek-R1’s Speed with Customized Speculative Decoding

Wai Tong Chung, Dan Waters, Avner May, Ben Athiwaratkun

Kernels

Chipmunk: Training-Free Acceleration of Diffusion Transformers with Dynamic Column-Sparse Deltas

Austin Silveria, Soham Govande, Dan Fu

Model Shaping

Direct Preference Optimization: A Technical Deep Dive

Ivan Provilkov, Zain Hasan, Max Ryabinin

Model Shaping

Continued Fine-tuning of LLMs: A Technical Deep Dive

Artem Chumachenko, Zain Hasan, Max Ryabinin

Agents

Open Deep Research

Together AI

Inference

DeepCoder: A Fully Open-Source 14B Coder at O3-mini Level

Michael Luo*, Sijun Tan*, Roy Huang*, Ameen Patel*, Alpay Ariyak*, Qingyang Wu*, Xiaoxiang Shi, Rachel Xin, Colin Cai, Maurice Weber, Ce Zhang, Li Erran Li, Raluca Ada Popa, Ion Stoica

Kernels

ThunderKittens Now Optimized for NVIDIA Blackwell GPUs

Benjamin Spector, Aaryan Singhal, Dan Fu, Chris Ré

Inference

Minions: embracing small LMs, shifting compute on-device, and cutting cloud costs in the process

Avanika Narayan*, Dan Biderman*, Sabri Eyuboglu*, Avner May, Scott Linderman, James Zou, Christopher Ré

Model Shaping

Long Context Fine-Tuning: A Technical Deep Dive

George Grigorev, Zain Hasan, Max Ryabinin

Model Shaping

Fine-Tuning LLMs for Multi-Turn Conversations: A Technical Deep Dive

Artem Chumachenko, Zain Hasan, Max Ryabinin

Inference

Even Better, Even Faster Quantized LLMs with QTIP

Albert Tseng, Qingyao Sun, David Hou, Chris De Sa

Architecture

Linearizing LLMs with LoLCATs

Michael Zhang, Simran Arora, Rahul Chalamala, Alan Wu, Benjamin Spector, Aaryan Singhal, Krithik Ramesh, Christopher Ré

Applications

Multimodal Document RAG with Llama 3.2 Vision and ColQwen2

Zain Hasan

Architecture

The Mamba in the Llama: Distilling and Accelerating Hybrid Models

Junxiong Wang, Daniele Paliotta, Avner May, Alexander M. Rush, Tri Dao

Inference

Speculative decoding for high-throughput long-context inference

Jian Chen, Vashisth Tiwari, Ranajoy Sadhukhan, Yunho Jin, Zhuoming Chen, Jinyuan Shi, Ian En-Hsu Yen, Avner May, Beidi Chen

Inference

TEAL: Training-Free Activation Sparsity in Large Language Models

James Liu, Pragaash Ponnusamy, Tianle Cai, Han Guo, Yoon Kim, Ben Athiwaratkun

Kernels

FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision

Jay Shah (Colfax Research), Ganesh Bikshandi (Colfax Research), Ying Zhang (Meta), Vijay Thakkar (NVIDIA), Pradeep Ramani (NVIDIA), Tri Dao (Princeton University, Together AI)

Applications

Building a personalized code assistant with open-source LLMs using RAG Fine-tuning

Kezhen Chen, Linda He, Ben Athiwaratkun, Jue Wang, Maurice Weber, Heejin Jeong, Yonatan Oren, Michael Poli

Inference

SpecExec: Massively Parallel Speculative Decoding for Interactive LLM Inference on Consumer Devices

Ruslan Svirschevski, Avner May, Zhuoming Chen, Beidi Chen, Zhihao Jia, Max Ryabinin

Agents

Together MoA — collective intelligence of open-source models pushing the frontier of LLM capabilities

Junlin Wang, Jue Wang, Ben Athiwaratkun, Ce Zhang, James Zou

Architecture

Dragonfly: A large vision-language model with multi-resolution zoom

Kezhen Chen, Rahul Thapa, Rahul Chalamala, Ben Athiwaratkun, Shuaiwen Leon Song, James Zou

Kernels

ThunderKittens: A Simple Embedded DSL for AI kernels

Benjamin Spector, Aaryan Singhal, Simran Arora, Chris Re

Inference

FAQ: Building LLMs with RedPajama-v2, a 30 trillion token web dataset

Together AI

Inference

Sequoia: Scalable, Robust, and Hardware-aware Speculative Decoding

Zhuoming Chen, Avner May, Ruslan Svirschevski, Yuhsun Huang, Max Ryabinin, Zhihao Jia, Beidi Chen

Architecture

BASED: Simple linear attention language models balance the recall-throughput tradeoff

Simran Arora, Sabri Eyuboglu, Michael Zhang, Aman Timalsina, Silas Alberti, Dylan Zinsley, James Zou, Atri Rudra, Christopher Ré

Architecture

Evo: Long-context modeling from molecular to genome scale

Eric Nguyen, Michael Poli, Matthew Durrant, Patrick Hsu, Brian Hie

Inference

BitDelta: Your Fine-Tune May Only Be Worth One Bit

James Liu, Guangxuan Xiao, Kai Li, Jason D. Lee, Song Han, Tri Dao, Tianle Cai

Inference

Long context retrieval models with Monarch Mixer

Jon Saad-Falcon, Dan Fu, Simran Arora

Architecture

Mamba-3B-SlimPJ: State-space models rivaling the best Transformer architecture

Tri Dao, Albert Gu

Architecture

Paving the way to efficient architectures: StripedHyena-7B, open source models offering a glimpse into a world beyond Transformers

Together

Kernels

FlashFFTConv: Efficient Convolutions for Long Sequences with Tensor Cores

Dan Fu, Hermann Kumbong, Eric Nguyen, Chris Ré

Inference

RedPajama-Data-v2: An open dataset with 30 trillion tokens for training large language models

Together

Kernels

Flash-Decoding for long-context inference

Tri Dao, Daniel Haziza, Francisco Massa, Grigory Sizov

Inference

Medusa: Simple framework for accelerating LLM generation with multiple decoding heads

Tianle Cai*, Yuhong Li*, Zhengyang Geng, Hongwu Peng, Tri Dao (* Equal contribution)

Model Shaping

Llama-2-7B-32K-Instruct — and fine-tuning for Llama-2 models with Together API

Together

Inference

Faster inference enables up to 5x price reduction on Together API

Together

Inference

Preparing for the era of 32K context: Early learnings and explorations

Together

Architecture

Monarch Mixer: A new model architecture for increased efficiency

Dan Fu, Simran Arora, Chris Ré

Model Shaping

Fine-tuning language models over slow networks using activation compression with guarantees

Jue Wang, Binhang Yuan, Luka Rimanic, Yongjun He, Tri Dao, Beidi Chen, Christopher Re, Ce Zhang

Model Shaping

Decentralized training of foundation models in heterogeneous environments

Binhang Yuan, Yongjun He, Jared Quincy Davis, Tianyi Zhang, Tri Dao, Beidi Chen, Percy Liang, Christopher Re, Ce Zhang

Kernels

FlashAttention: Fast and memory-efficient exact attention with IO-Awareness

Tri Dao, Daniel Y. Fu, Stefano Ermon, Atri Rudra, Christopher Ré

Model Shaping

CocktailSGD: Fine-tuning foundation models over 500Mbps networks

Jue Wang, Binhang Yuan, Luka Rimanic, Yongjun He, Tri Dao, Beidi Chen, Christopher Re, Ce Zhang

Inference

FlexGen: High-throughput generative inference of large language models with a single GPU

Ying Sheng, Lianmin Zheng, Binhang Yuan, Zhuohan Li, Max Ryabinin, Daniel Y. Fu, Zhiqiang Xie, Beidi Chen, Clark Barrett, Joseph E. Gonzalez, Percy Liang, Christopher Ré, Ion Stoica, Ce Zhang

Architecture

Hyena Hierarchy: Towards larger convolutional language models

Michael Poli, Stefano Massaroli, Eric Nguyen, Daniel Y. Fu, Tri Dao, Stephen Baccus, Yoshua Bengio, Stefano Ermon, Christopher Ré

Kernels

FlashConv: Speeding up state space models

Dan Fu and Tri Dao

Architecture

Hungry Hungry Hippos: Towards language modeling with state space models

Daniel Y. Fu, Tri Dao, Khaled K. Saab, Armin W. Thomas, Atri Rudra, Christopher Ré

Model Shaping

NeurIPS 2022: Overcoming communication bottlenecks for decentralized training (2/2)

Together

Model Shaping

NeurIPS 2022: Overcoming communication bottlenecks for decentralized training (1/2)

Together

Inference

HELM: benchmarking large language models on the Together Research Computer

Together

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AI natives build on Together AI

See how Together AI powers customers building the next generation of AI products.

How Cursor partnered with Together AI to deliver real-time, low-latency inference at scale

Inference, GPU clusters, RESEARCH • Enterprise

How Decagon Engineered Sub-Second Voice AI with Together AI

Inference, GPU clusters, RESEARCH

Cost reduction per turn vs. gpt-5 mini

11x

Faster Inference

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Inference

Together Link: frontier-quality open models in the harness you already use

Together Link brings frontier open models like GLM 5.3 and Kimi K3 into the coding agent your team already uses, cutting model spend by over 50%.

Inference

The Open Source AI Stack

A deep dive into the open model AI stack — model, inference, gateways and routers, harness, and tools — and how keeping each layer independent lets you swap in a new open model in minutes instead of rebuilding your workflow.

GPU Clusters

Together AI and Y Combinator partner to launch the first dedicated GPU cluster for the YC community

No more two-year compute contracts. Together AI and YC just gave YC startups a faster way to get GPUs.

Model Library

GLM-5.3 vs. Claude Fable 5 on DeepSWE: Cost, Coding, and Routing

We ran 904 DeepSWE rollouts on GLM-5.3 and Claude Fable 5. A tie on pass@1, but GLM-5.3 wins pass@4 and costs 5.4x less: \$3.99 per rollout vs. \$21.63.

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Build what's nexton the AI Native Cloud

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Grounded in cutting-edge research

AI natives build on Together AI

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Build what's next
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