Knowledge catalogue
Congrats to @ollama on the fundraise and 9M+ active builders.🚀 Open models are becoming the default path for developers who want to build, run, and own their AI stack. We're excited to support the eco
Catch us at @RaiseSummit! Our CEO @vipulved sits down with @wolfejosh of @Lux_Capital for a fireside chat 'AI Sticker Shock: Open Source as the Answer to Broken Tokenomics' Join us on the Master Stage
Provisioned Throughput is a new serverless product from @togethercompute w/ guaranteed TPMs. We created this for mission critical enterprise class applications. It functions like dedicated capacity bu
Together AI announced provisioned throughput capabilities, a feature that allows users to reserve dedicated model inference capacity at predictable costs. This offering enables organizations to optimi
We're introducing Provisioned Throughput: reserved inference capacity for frontier open models, with token-based pricing and a 99% uptime SLA. Serverless simplicity, guaranteed capacity, up to 90% low
GLM 5.2, running on Together AI's platform, has achieved the top ranking on Artificial Analysis benchmarks for both output speed and latency metrics. This announcement highlights Together AI's infrast
Introducing AI Browser Games! Watch open & closed models build small browser games head to head. Open models like Kimi K2.7 were faster, cheaper, & produced games similar to Opus 4.8. In some cases, o
This is what latency optimization looks like below the API 👇 Together ATLAS, NVIDIA Blackwell, CUDA, TensorRT-LLM, Dynamo, and custom kernels all working together to make inference faster for users. P
'Alex really hit the nail on the head, by sending data to a company that has extremely smart models, you are really giving up your business's recipe for them to copy.' Our CEO @vipulved when asked abo
Together AI, in partnership with NVIDIA and Lyra Lab, hosted a fireside chat led by Tri Dao discussing current trends and future directions in AI research and infrastructure development. The event bro
Open-source AI models provide users with full control, customization capabilities, and data ownership, addressing privacy and autonomy concerns compared to proprietary alternatives. The post highlight
Open source AI model usage has tripled from 10% to 30% of total AI tokens consumed within a year, reflecting a significant market shift toward open and modular AI architectures. This trend indicates g
This is a discussion about token economics, likely covering how tokens function as units of value or utility in blockchain and AI systems, shared by Vipul Ved on X (formerly Twitter) and referenced by
We analyzed GLM 5.2 vs Sonnet 5 for software engineering tasks using DeepSWE. GLM 5.2 gets you ~80% of Sonnet 5's capability at ~20% of the price. More insights in the thread! Deepdive: Sonnet 5 and G
We're releasing the full slides for our 2 hr deepdive session from the AI Engineer World's Fair. We covered how we build inference engines to serve agentic workloads at trillion token production scale
Together AI highlights the rapid growth of AI token consumption across multiple companies and teams, noting an increase from 30 billion tokens monthly to 400 trillion annually, with platforms like Cur
Together AI announced significant performance improvements in their AI infrastructure, achieving a 6x cost reduction per turn while maintaining p95 latency under 400ms, with new models being released
LIVE at 12p PT/3p ET: AI’s cleanest stories are getting messy. Meta has excess compute. AI-heavy companies are hiring, not shrinking. And open models keep getting better. @DanielTNiles on what this me
Our CEO @vipulved on @CNBC with @dee_bosa: your data is your recipe. As models get smarter, sending proprietary workflows, customer context, and business logic into closed systems becomes a strategic
Together AI announced a Series C funding round aimed at accelerating the adoption and development of open-source AI models and infrastructure. The funding enables the company to expand its platform fo
DSGym is a comprehensive framework designed to evaluate and train AI agents for data science tasks, providing a structured environment for benchmarking agent performance across various data science wo
ThunderAgent is an agentic inference system designed for fast and efficient execution of AI agent programs, developed by Together AI. The system appears to optimize program-aware inference by leveragi
TTT-Discover is a method that enables models to learn and discover patterns during test time rather than only during training, allowing for adaptation to new data distributions at inference. The appro
RARO (Reasoning via Demonstrations) is a method for training AI models to improve reasoning capabilities by learning from demonstrations rather than relying solely on external verifiers. The approach
This paper presents V1, a framework that unifies text generation with self-verification mechanisms to enable parallel reasoning processes in language models. The approach allows models to generate mul
Aurora is a unified training-serving system that integrates reinforcement learning with adaptive speculative training to optimize large language model inference and training efficiency. The system dyn
Untied Ulysses is a memory-efficient technique for context parallelism that processes attention heads in chunks rather than sequences, reducing memory overhead during transformer inference and trainin
Opportunistic Expert Activation (OEA) is a batch-aware expert routing technique for mixture-of-experts models that enables faster decoding without requiring model retraining. The method optimizes whic
ParallelKernelBench is a benchmarking framework designed to evaluate large language models' ability to generate optimized GPU kernels for multi-GPU computing environments. The benchmark assesses LLMs
Together AI advocates for democratizing artificial intelligence by making it abundant and affordable rather than concentrated among expensive proprietary systems. The post likely argues for open-sourc
Together AI is promoting a rescheduled presentation about sparse attention mechanisms and kernel optimizations in collaboration with MiniMax AI, scheduled for 10:45am PT. The session focuses on techni
Multi-GPU kernels are the real test for coding models. Today at @aiDotEngineer, @simran_s_arora shared ParallelKernelBench, an open-source benchmark for evaluating whether LLMs can write fast CUDA ker
Nemotron 3 Ultra is taking off on Together AI 🚀 In just days, it's climbed to 35B tokens/day on @OpenRouter. That's the community voting with their tokens for open models that are fast, efficient, and
Our research team has 9 papers at ICML next week! Spanning the full stack from frontier agents to GPU kernels, we're excited to share what our researchers and collaborators have been working on. If yo
Together AI announced their Series C funding round, which was covered in a reporting piece by the New York Times. The announcement was shared via their official X (formerly Twitter) account, linking t
Together AI announced a Series C funding round and shared details about their company vision, current capabilities, and future direction in open-source AI development and inference optimization. The a
ParallelKernelBench is a benchmark tool or methodology developed by Together AI for evaluating the performance of parallel kernel execution in machine learning systems. The benchmark likely measures m
Together AI is announcing their participation in ICML 2026 (International Conference on Machine Learning) and inviting the community to visit them at the conference. The announcement likely includes d
Tasks moved from closed models to open models on Together AI cost one-fifth to one-seventh as much. That's from @DecagonAI co-founder @AshwinSreenivas in the NYT today. This is what abundant intellige
Together AI presented a 2-hour technical deep dive on building inference engines capable of handling trillion-token agentic workloads, covering architecture and optimization strategies for large-scale
As open models get stronger, more workloads move into the competitive inference market. That pushes the real fight toward speed, cost, reliability, and control. Together AI is where open models become
Open models are not just a pricing story. They are what happens when the AI stack becomes modular: models, APIs, harnesses, tools, and inference all improving independently. Together AI is building th
Together AI announced a submission portal for winners at aicup.io, crediting @nutlope and @federicobianchy for creating the initiative. This appears to be related to an AI Cup competition or challenge
To celebrate the start of @aiDotEngineer AI Engineer World's Fair, we're launching a bracket competition! Use an agent or pick manually to choose your winners for the Round of 32 by 9:00 am PT Tuesday
More reason why we’re excited about GLM-5.2 on Together 👇 Strong enough for serious coding work, cheap enough to change routing decisions, and easy to access through the tools developers already use.
There's a big difference between a single model call and serving an agent at scale. @ZainHasan6 breaks down what actually changes. Catch our team this Monday at 9 a.m. PST for their open-source infere
next week at @aiDotEngineer, we are joining @togethercompute for a conversation on what goes into running agents at scale. @olive_jy_song, Research Lead, RL at MiniMax, and @realDanFu, VP of Kernels a
As token usage explodes, model choice becomes product strategy. Teams are already testing models like GLM-5.2 because they want frontier quality, better tokenomics, and more control over cost, data, a
What happens when AI agents collaborate on open science? At @aiDotEngineer World’s Fair, @james_y_zou will share work on EinsteinArena and DSGym, from multi-agent math discovery to better evaluation f
Cost per iteration is the unlock. GLM-5.2 on Together AI can generate polished web apps for a few cents. At that price, developers can explore more directions, compare more versions, and keep the best
I love using GLM 5.2 for web app iteration. My workflow: generate 6 variations, then pick the best one and continue iterating on it. I built Recast to make this even easier. Give it a prompt, get 6 va
LLMs are getting better at writing GPU kernels. Multi-GPU kernels are the harder test. At @aiDotEngineer World's Fair, @simran_s_arora will share ParallelKernelBench, an open-source benchmark built fr
Together AI built the world’s fastest speech-to-text stack. Parakeet on Together transcribes ~302 seconds of audio per second of processing time, the top speed factor reported by @ArtificialAnlys. In
400T tokens is what production adoption looks like. Teams are moving real workloads to open models because they want frontier quality, better tokenomics, and more control over inference. Together AI g
A tangible comparison of GLM performance stacked against Opus 4.8 on web tasks by @nutlope. GLM 5.2 is chattier, but still faster when served by @togethercompute and over 3x cheaper. Announcing GLM Ar
Agentic coding changes what inference engines need to handle. At AI Engineer World’s Fair, Together AI engineers will lead a hands-on workshop on how inference engines work and what it takes to serve
Announcing GLM Arena! A series of tests (infographics, svgs, sites, ect..) ran on GLM 5.2 and Opus 4.8, with prompts included. On average, GLM 5.2 produced 2x the tokens but was still faster + 3x chea
Together AI highlights how the growth of open-source AI models is benefiting their platform and the broader ecosystem. The article likely discusses how open-source initiatives, including collaboration
An agentic loop (compile, test, profile, revise) helps. Gemini 3 Pro went from 24 to 35/87 correct, then plateaued after ~20 steps. Feedback fixes syntax, not rank coordination, collective ordering, o
.@cartesia runs one of the hardest inference workloads: real-time voice. Their stack has to keep long-lived streams moving, serve millions of audio minutes a day, and hold model latency around 90ms. T