Infrastructure→Official→Together AI Blog
Together AI discusses the practical implications of different uptime percentages for inference services, explaining what is required to achieve 99%, 99.9%, and 99.99% availability. The post also describes the types of failures each level must withstand and suggests questions to consider when evaluating inference providers.
Why it matters: Understanding uptime guarantees is important for selecting reliable AI inference providers as these services become integral to production systems.
Jul 16, 2026
Models→Official→Together AI Blog
Thinking Machines Lab has released its first open model, Inkling, a 975-billion-parameter multimodal AI trained to understand video and audio. The model is available on Together AI's platform from day one, serving as the company's first public demonstration after a year and a half of developing AI infrastructure largely out of public view.
Why it matters: Inkling could help position Thinking Machines Lab as a competitor to Anthropic and OpenAI in the open model space.
Jul 15, 2026
Infrastructure→Official→Together AI Blog
Together AI announced improvements to its GPU clusters for production AI workloads, including passive health checks, automated node repair, enhanced Slurm reliability, OIDC authentication, and startup scripts. These features are designed to provide greater reliability and control for users running large-scale AI training and inference.
Why it matters: As AI workloads scale, reliable and controllable GPU infrastructure becomes increasingly important for production deployments, and Together AI's updates address key operational challenges.
Jul 15, 2026
Infrastructure→Official→Together AI Blog
Together AI has addressed a production bug known as 'Copy Fail,' which was traced back to a 732-byte code change. The company deployed a fix to resolve the issue in their infrastructure.
Why it matters: This highlights how even small code changes can lead to significant issues in AI infrastructure.
Jul 11, 2026
Infrastructure→Official→Together AI Blog
Together AI published a guide on designing multi-tenant GPU clusters that pool capacity while maintaining team isolation. The article explains how AI-native companies can achieve this balance and describes Together AI's practical implementation.
Why it matters: This guide provides practical insights for AI teams needing efficient GPU resource sharing without compromising isolation.
Jul 11, 2026
Research→Official→Together AI Blog
Together AI has announced a new technique called distribution-aware speculative decoding (DAS) that can speed up reinforcement learning (RL) rollouts by up to 50% without degrading reward quality. The method addresses the bottleneck of rollout generation in RL post-training by adaptively applying speculative decoding. The announcement was made on the Together AI blog.
Why it matters: This advancement could significantly reduce the time and cost of RL post-training, making it more practical for large-scale AI model development.
Jul 11, 2026
Models→Official→Together AI Blog
Together AI has partnered with Adaption to bring Together Fine-Tuning natively into the Adaptive Data platform. This integration enables teams to optimize datasets, run fine-tuning, evaluate results, and deploy stronger open models.
Why it matters: This partnership streamlines the fine-tuning workflow for open models, making it easier for teams to improve model performance directly from their data platform.
Jul 11, 2026
Infrastructure→Official→Together AI Blog
Together AI has achieved ISO 27001:2022 certification, validating its information security management system for enterprise-grade security in production AI workloads. This milestone demonstrates the company's commitment to maintaining high security standards.
Why it matters: The certification assures enterprises that Together AI meets internationally recognized security standards, which may encourage broader adoption of its AI infrastructure.
Jul 11, 2026
People Institutions→Official→Together AI Blog
Together AI will present nine papers at ICML 2026, showcasing research across the full AI stack. The company will be present at booth B714 in Seoul.
Why it matters: This highlights Together AI's active role in advancing AI research that informs their platform.
Jul 11, 2026
Models→Official→Together AI Blog
Together AI published real-world inference benchmarks for coding agents, reporting 31% higher throughput than TensorRT-LLM, 2× better time-to-first-token at saturation, and 76% lower cost than Claude Opus 4.6. The benchmarks focus on scaling inference for agentic coding workloads.
Why it matters: This demonstrates significant performance and cost improvements for deploying coding agents at scale, which could accelerate adoption of AI-assisted development.
Jul 11, 2026
Models→Official→Together AI Blog
Together AI has made NVIDIA Nemotron 3 Super and Nemotron 3 Nano Omni available on its platform. Nemotron 3 Super offers efficient multi-agent reasoning and a 1M-token context window, while Nemotron 3 Nano Omni is a single open model that can process video, images, audio, and text for agentic workloads at scale.
Why it matters: These launches provide developers with production-grade, multimodal AI models optimized for agentic reasoning and scalable deployment.
Jul 11, 2026
Infrastructure→Official→Together AI Blog
Together AI has announced an integration with Goose that allows users to deploy any Hugging Face model in a single session using Dedicated Container Inference. This approach removes setup complexity, enabling models to run in a production-grade GPU environment immediately upon release.
Why it matters: This integration streamlines AI model deployment, making it more accessible to developers without requiring infrastructure expertise.
Jul 11, 2026
Models→Official→Together AI Blog
Together AI has launched DeepSeek-V4 Pro, featuring a 512K context length and controllable reasoning modes. The model offers cached-input pricing for long-context workloads such as code agents, document intelligence, and research synthesis.
Why it matters: This release provides developers with a powerful, cost-efficient model for complex reasoning tasks requiring extended context.
Jul 11, 2026
Infrastructure→Official→Together AI Blog
Together AI published a blog post detailing how it serves MiniMax-M3 efficiently, enabling 1M-token context and multimodality. The optimizations include KV-block-major sparse attention, paged MSA decode, optimized index scoring, and a Rust-based multimodal gateway.
Why it matters: This demonstrates practical techniques for deploying large multimodal models with long context windows, which is critical for enterprise applications requiring processing of extensive documents and multiple data types.
Jul 11, 2026
Infrastructure→Official→Together AI Blog
Together AI published a blog post detailing the inference systems work required to serve DeepSeek-V4, which supports million-token context. The post covers compressed KV layouts, prefix caching, kernel maturity, and endpoint profiles for long-context workloads on NVIDIA HGX B200 hardware.
Why it matters: This highlights the growing importance of inference infrastructure as models scale to million-token contexts, a key challenge for enterprise AI deployment.
Jul 11, 2026
Companies Funding→Official→Together AI Blog
Together AI has announced an $800 million Series C funding round aimed at accelerating the shift to open-source AI. The company emphasized that the economics of closed models do not scale and shared plans for future development.
Why it matters: This significant investment highlights growing market confidence in open-source AI as an alternative to proprietary systems.
Jul 11, 2026
Research→Official→Together AI Blog
Together AI has published a blog post outlining research focused on improving the efficiency, reliability, and scalability of AI inference. The post discusses the challenges AI-native teams face as they transition from building models to deploying them in production environments.
Why it matters: Improving inference efficiency can help AI-native teams deploy models more effectively at scale.
Jul 11, 2026
Models→Official→Together AI Blog
Together AI compared Kimi K2.7 Code and Claude Fable 5 by generating 12 landing pages. Kimi K2.7 Code cost 94% less and achieved scores within a few points of Claude Fable 5 on every page. The blog discusses the factors that influenced these results.
Why it matters: This comparison demonstrates a substantial cost advantage for Kimi K2.7 Code while maintaining similar quality, which could impact developer tool selection.
Jul 11, 2026
Research→Official→Together AI Blog
Together AI released ParallelKernelBench, a benchmark that tests LLMs on writing fast multi-GPU CUDA kernels across 87 real workloads. The best-performing model solves under a third of the tasks, though some generated kernels outperform any public implementation.
Why it matters: This benchmark highlights both the current limitations and emerging potential of LLMs in high-performance computing code generation.
Jul 11, 2026
Models→Official→Together AI Blog
Together AI has developed what it claims is the world’s fastest speech-to-text stack, according to benchmarks by Artificial Analysis. The company attributes this achievement to optimizing the entire system path for automatic speech recognition, rather than focusing solely on GPU inference.
Why it matters: Faster speech-to-text systems could reduce latency in real-time transcription and voice applications, improving the practicality of AI-powered speech recognition.
Jul 11, 2026