together.ai·lab·114 items·last fetched
Provisioned Throughput gives you reserved inference capacity for frontier open models like MiniMax M3 and GLM-5.2. Token-based pricing, a 99% uptime SLA, and up to 90% lower cost than proprietary APIs. No GPU-hour…
We raised $800M to accelerate the shift to open-source AI. Here's why the economics of closed models don't scale, and what we're building next.
Eight papers at ICML 2026 across the full stack. The research that becomes the Together platform. Find us at booth B714 in Seoul.
ParallelKernelBench tests whether LLMs can write fast multi-GPU CUDA kernels across 87 real workloads. The best model solves under a third, but a few generated kernels beat any public implementation.
We generated 12 landing pages with Kimi K2.7 Code and Claude Fable 5. Kimi cost 94% less and scored within a few points on every page. Here's what actually moved the needle.
Together AI has earned ISO 27001:2022 certification, validating our commitment to enterprise-grade security for production AI workloads.
How Together served MiniMax-M3 efficiently with KV-block-major sparse attention, paged MSA decode, optimized index scoring, and a Rust-based multimodal gateway.
Together AI built the fastest speech-to-text stack on Artificial Analysis by treating ASR as a full-path systems problem, not just a GPU inference problem.
Real-world inference benchmarks for coding agents: 31% more TPS than TensorRT-LLM, 2× better TTFT at saturation, and 76% lower cost than Claude Opus 4.6.
Together AI partners with Pearl Research Labs to launch a discounted Pearl-powered inference endpoint for Gemma-4-31B-it-pearl, using Proof of Useful Work to turn AI workloads into crypto emissions.
Violin is an open-source AI video translation tool that combines speech recognition, LLM translation, and text-to-speech to make video content accessible across languages.
Voice finder helps developers search, match, filter, and audition 600+ voices across Together AI TTS models using natural-language prompts or uploaded audio samples.
Learn how to deploy any Hugging Face model in one session using Goose and Together's Dedicated Container Inference. Skip the setup complexity — one prompt gets your model running in a production-grade GPU environment…
DeepSeek-V4 makes million-token context a serving-systems problem. Together AI explores the inference work behind V4 on NVIDIA HGX B200, including compressed KV layouts, prefix caching, kernel maturity, and endpoint…
As AI moves from research to production, the challenge for AI-native teams shifts from building models to running them — efficiently, reliably, and at scale.
Together AI and Adaption partner to bring Together Fine-Tuning natively into Adaptive Data, helping teams optimize datasets, run fine-tuning, evaluate results, and deploy stronger open models.
DeepSeek-V4 Pro is now available on Together AI with 512K context, controllable reasoning modes, and cached-input pricing for long-context reasoning workloads like code agents, document intelligence, and research…
NVIDIA Nemotron 3 Nano Omni is now on Together AI: a single open model that reasons across video, images, audio, and text, built for agentic workloads at scale.
Rollout is the silent bottleneck in RL post-training. DAS fixes it with adaptive speculative decoding — up to 50% faster, zero degradation in reward quality.
Learn how AI-native companies design multi-tenant GPU clusters that pool capacity without sacrificing team isolation — and how Together AI makes it work in practice.
Parcae is a stable looped language model that matches the quality of a Transformer twice its size — a 770M model reaching 1.3B-level performance. We introduce the first scaling laws for looping and show that increasing…
EinsteinArena is a platform where AI agents collaborate and compete on open math problems. AI agents on EinsteinArena have already set 11 new state-of-the-art results on open math problems — including pushing the…
AI-native companies need infrastructure built for models, not legacy workloads. Learn what defines an AI Native Cloud and why it matters for the next platform shift.
New research shows LLMs can optimize database query execution plans—achieving up to 4.78x speedups by correcting the cardinality estimation errors that statistical heuristics miss.
A four-model video suite for generation, continuation, reference-driven workflows, and editing, rolling out on Together AI starting with text-to-video.
Production STT and TTS from Deepgram, available on Together AI Dedicated Model Inference for real-time voice agents.
The team behind FlashAttention and ThunderKittens — how Together AI's kernel researchers close the gap between GPU hardware and production AI.
1.25x over a well-trained static speculator. Aurora is an open-source RL framework that turns speculative decoding from a one-time offline setup into a self-improving system that learns from every request it serves.
As context windows grow, LLM performance degrades in unexpected ways. We show how a "Divide & Conquer" framework — breaking long documents into parallel chunks with a planner, workers, and manager — lets smaller models…
Together AI expands fine-tuning with native support for tool call, reasoning, and vision-language models, plus 100B+ model training, up to 6× higher throughput, and job cost and ETA estimates.
Meet Mamba-3: the SSM built for inference. Faster than Transformers at decode, stronger than Mamba-2, and open-source from day one.
Together AI arrives at NVIDIA GTC 2026 with new launches in inference, agents, voice AI, and open models — plus technical sessions from its research and engineering leaders.
Build real-time voice agents on Together AI with co-located STT, LLM, and TTS infrastructure, native Deepgram and Cartesia support, and end-to-end latency under 500ms.
NVIDIA Nemotron 3 Super is now available on Together AI Dedicated Inference, delivering efficient multi-agent reasoning, a 1M-token context window, and production-grade deployment on managed infrastructure.
Together GPU Clusters now include built-in autoscaling, RBAC, full-stack observability, and self-healing node repair—giving teams production-ready GPU infrastructure that scales efficiently, stays resilient, and…
As GPU throughput outpaces memory bandwidth, kernels must evolve. We introduce FlashAttention-4, featuring new pipelining for maximum overlap, 2-CTA MMA modes to reduce shared memory traffic, and a hardware-software…
At AI Native Conf, Together AI announced breakthroughs across kernels, RL, and inference optimization — including FlashAttention-4, ThunderAgent, and together.compile. Research that ships to production. That's the AI…
Serving long prompts doesn't have to mean slow responses. Learn how Together AI's CPD architecture separates warm and cold inference workloads to deliver 40% higher throughput and dramatically lower time-to-first-token…
We've refreshed our visual identity — designed with Pentagram to express how Together AI connects open-source innovation, systems research, and builders to unlock new possibilities.
State-of-the-art speech models like Whisper and Deepgram score near-human on benchmarks — then fail 39% of the time on street names. New research from Together AI exposes the gap and a fix.
Standard diffusion language models can't use KV caching and need too many refinement steps to be practical. CDLM fixes both with a post-training recipe that enables exact block-wise KV caching and trajectory-consistent…
Together AI launches production-grade orchestration for custom AI models with 1.4x–2.6x faster inference.
What do language models generate when you don't tell them what to generate? New research reveals that LLM families have distinct 'knowledge priors'—GPT models default to code and math, Llama favors narratives, DeepSeek…
Hiring Alon Gavrielov further deepens Together AI’s commitment to building AI factories that deliver the most reliable, efficient, and scalable infrastructure for AI-native teams.
Together Evaluations now supports OpenAI, Anthropic, and Google models for cross-provider benchmarking. Compare open-source, fine-tuned, and proprietary models side-by-side to make data-driven decisions on quality…
Fine-tuned open-source LLM judges can outperform GPT-5.2 at evaluating model outputs. Using Direct Preference Optimization on just 5,400 preference pairs, we trained GPT-OSS 120B to beat GPT-5.2 on human preference…
Introducing DSGym—a holisti evaluation and training framework for LLM-based data science agents. Features 90+ bioinformatics tasks, 92 Kaggle competitions, and synthetic trajectory generation. Our 4B model achieves…