Build real-time voice agents on Together AI
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.
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Together AI Blog
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Together AI Brings NVIDIA Nemotron 3 to Developers on Day 0
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.
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New in Together GPU Clusters: Autoscaling, observability, and self-healing
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 supports shared enterprise workloads.
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FlashAttention-4: Algorithm and Kernel Pipelining Co-Design for Asymmetric Hardware Scaling
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 hybrid approach to softmax exponentials.
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Key research and product announcements at the AI Native Conf
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 Native Cloud.
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Cache-aware prefill–decode disaggregation (CPD) for up to 40% faster long-context LLM serving
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 for long-context LLM serving.
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Introducing Together AI’s new look
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.
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How speech models fail where it matters the most and what to do about it
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.
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Consistency diffusion language models: Up to 14x faster inference without sacrificing quality
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 step reduction — delivering up to 14.5x latency improvements
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Introducing Dedicated Container Inference: Delivering 2.6x faster inference for custom AI models
Together AI launches production-grade orchestration for custom AI models with 1.4x–2.6x faster inference.
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What do LLMs think when you don't tell them what to think about?
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 generates religious content, and Qwen outputs exam questions.
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Together AI welcomes Alon Gavrielov as VP of Infrastructure Strategy
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.
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Together Evaluations now supports comparing top commercial APIs vs. open source models
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, cost, and performance—all in one platform.
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Fine-tuning open LLM judges to outperform GPT-5.2
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 alignment—at 15x lower cost and 14x faster inference speeds.
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DSGym: A holistic framework for evaluating and training data science agents
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 state-of-the-art performance among open-source models through exe
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Optimizing inference speed and costs: Lessons learned from large-scale deployments
Learn how to reduce inference latency without massive cost using proven inference optimization tactics — improving throughput, GPU utilization, and cost efficiency while balancing throughput vs. latency tradeoffs.
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Learn how Cursor partnered with Together AI to deliver real-time, low-latency inference at scale
Together AI teamed with Cursor to build the real-time inference stack that keeps in-editor agents fast and reliable. They productionized NVIDIA Blackwell (B200/GB200), tuning ARM hosts, kernels, and FP4/TensorRT quantization for low latency and rapid model rollouts.
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Inside multi-node training: How to scale model training across GPU clusters
Learn how foundation models are trained at scale using multi-node GPU clusters, including distributed training techniques, infrastructure requirements, and practical steps to scale training efficiently.
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How to choose the right open model for production
Learn how to choose the right open-source model for production by evaluating model quality, benchmarking performance, and deploying open models that balance cost, speed, and accuracy.
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MiniMax Speech 2.6 Turbo now available natively on Together AI
MiniMax Speech 2.6 Turbo: State-of-the-art multilingual TTS with human-level emotional awareness, sub-250ms latency, and 40+ languages—now on Together AI.
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