Together AI

Together AI has enhanced its GPU Clusters with passive health checks and auto node repair, improving reliability and operational control for large-scale training and inference. The new features, including Slinky 1.0, aim to minimize downtime and streamline cluster management as organizations scale.

Together AI's introduction of passive health checks and auto node repair in their GPU Clusters enhances reliability and operational control, which is crucial for builders and PMs managing large-scale AI projects. This development minimizes downtime, allowing teams to focus on innovation rather than maintenance, making it an attractive proposition for investors looking at scalable AI infrastructure.

Together AI has achieved ISO 27001:2022 certification, validating its Information Security Management System for secure AI workloads. This certification enhances governance around sensitive data, access control, and incident response, ensuring robust protection for customers using its global platform.

Together AI's achievement of ISO 27001:2022 certification signifies a commitment to robust information security for AI workloads, which is crucial for builders and PMs focusing on enterprise solutions. For investors, this certification enhances the company's credibility and marketability, indicating a lower risk profile in handling sensitive data.

MiniMax's M3 model introduces a 1M-token context and multimodal capabilities, optimized for efficient inference with a 9x speedup in prefill and 15x in decoding, supported by Together AI's cloud infrastructure.

The introduction of MiniMax's M3 model with 1M-token context and multimodal capabilities allows builders and PMs to create more complex and contextually aware applications, significantly enhancing user experience. For investors, the 9x speedup in prefill and 15x in decoding represents a critical advancement in AI efficiency, indicating potential for higher returns in scalable AI solutions.

Together AI developed the fastest speech-to-text stack, achieving 20 hours of transcription in under 10 seconds using NVIDIA's Parakeet-TDT 0.6B v3 and OpenAI's Whisper Large v3. Key optimizations included TensorRT for encoder efficiency and GPU-based decoding, resulting in a 2-3x faster performance.

Together AI's development of the world's fastest speech-to-text stack, capable of transcribing 20 hours of audio in under 10 seconds, signals a significant leap in real-time transcription capabilities. This advancement can enhance applications in accessibility, customer service, and content creation, making it a critical consideration for builders and PMs focused on integrating efficient speech recognition into their products.

Together AI's new benchmark for coding agents reveals that its Together Inference Engine achieves 31% higher TPS than TensorRT-LLM, maintaining under 1s TTFT at 625 TPM per GPU. This performance is crucial for handling high concurrency and long context requests in production environments.

Together AI's new benchmark for coding agents, demonstrating a 31% higher transactions per second (TPS) than TensorRT-LLM, is significant for builders and PMs as it indicates improved performance for high-concurrency applications. For investors, this development suggests a competitive edge in the market, potentially leading to increased adoption and revenue opportunities in AI-driven solutions.

Violin is an open-source video translation tool by Together AI, utilizing Whisper V3 for ASR, Deepseek V4 Pro for translation, and Cartesia’s Sonic 3 for TTS, enabling high-quality multilingual video accessibility. It features an interactive chat assistant for user queries and is designed for content creators and developers alike.

Violin's open-source video translation tool leverages advanced AI technologies to enhance multilingual accessibility for video content. This development signals a growing demand for tools that enable creators to reach broader audiences, presenting opportunities for builders and PMs to integrate similar capabilities into their products, while investors can identify potential market growth in the video translation sector.

Together AI's Voice Finder tool allows developers to quickly search over 600 voices across multiple TTS models, including MiniMax and Deepgram. By using prompts or audio samples, users can find suitable voices based on 15+ metadata attributes, streamlining the process of selecting the right voice for applications like fintech support or meditation guides.

Together AI's Voice Finder tool enables developers to efficiently select from over 600 TTS voices using metadata attributes, significantly reducing the time spent on voice selection for applications like fintech and meditation. This development is crucial for builders and PMs looking to enhance user experience through personalized voice interactions, while investors can see potential for increased adoption of voice technology in various sectors.

DeepSeek-V4 transforms million-token context into a serving-systems challenge, as explored by Together AI on NVIDIA HGX B200. Key innovations include compressed KV layouts, prefix caching, and optimized kernel maturity for efficient long-context inference workloads.

The development of DeepSeek-V4, which addresses million-token context as a serving-systems challenge, is significant for builders and PMs as it highlights the need for optimized infrastructure in AI applications. Investors should note that innovations like compressed KV layouts and prefix caching can lead to more efficient long-context inference, potentially enhancing the performance and scalability of AI systems.

Deploy any Hugging Face model effortlessly with Goose and Together's Dedicated Container Inference. This solution allows users to run models in a production-grade GPU environment with just one prompt, eliminating setup complexities and enabling immediate deployment on release day.

The launch of Goose and Together's Dedicated Container Inference allows builders and PMs to deploy Hugging Face models with minimal setup, streamlining the path from development to production. This efficiency can significantly reduce time-to-market for AI applications, making it a crucial development for investors looking for scalable solutions in the AI space.

As AI transitions from research to production, the focus for AI-native teams is shifting towards efficient and reliable model deployment at scale. This involves overcoming challenges related to resource management and performance optimization to ensure models operate effectively in real-world applications.

The shift towards efficient inference at scale is critical for builders and PMs as it directly impacts the feasibility of deploying AI models in real-world applications, requiring advancements in resource management and performance optimization. For investors, this development signals potential growth opportunities in companies that can successfully navigate these challenges and deliver scalable AI solutions.

Together AI swiftly mitigated the Copy Fail vulnerability (CVE-2026-31431) by disabling the algif_aead interface across its infrastructure, preventing potential privilege escalation and cross-tenant risks in AI workloads. This proactive measure ensured minimal operational impact while maintaining security in shared kernel environments.

The swift mitigation of the Copy Fail vulnerability (CVE-2026-31431) by Together AI highlights the importance of proactive security measures in AI infrastructure. Builders and PMs should prioritize security in shared environments to prevent privilege escalation risks, while investors should recognize that robust security practices can enhance the overall reliability and trustworthiness of AI systems.

Together AI partners with Adaption to integrate Together Fine-Tuning into Adaptive Data, enhancing model training efficiency. This collaboration enables users to optimize datasets and achieve an average 82% increase in data quality, facilitating the deployment of fine-tuned models on Together AI's infrastructure.

The partnership between Together AI and Adaption to integrate Together Fine-Tuning into Adaptive Data significantly enhances model training efficiency, achieving an average 82% increase in data quality. This development is crucial for builders and PMs as it streamlines the deployment of fine-tuned models, ultimately reducing time-to-market and improving product performance.

DeepSeek-V4 Pro is now available on Together AI, offering 1.6T-parameter MoE with 512K context for serverless inference. It supports three reasoning modes and reduces costs for repeated long-context queries, with input pricing at $2.10 per million tokens.

The release of DeepSeek-V4 Pro on Together AI, featuring 1.6T parameters and 512K context for serverless inference, significantly lowers costs for applications requiring long-context queries. This development allows builders and PMs to create more efficient AI solutions while investors can recognize potential cost savings and scalability in AI deployment.

Together AI has launched the NVIDIA Nemotron 3 Nano Omni, a multimodal AI model that combines video, audio, images, and language reasoning. This model, utilizing a hybrid Mamba-Transformer architecture, allows developers to build agentic applications with high efficiency and low latency, streamlining deployment from prototype to production without infrastructure management.

The launch of the NVIDIA Nemotron 3 Nano Omni by Together AI enables developers to create multimodal AI applications with improved efficiency and reduced latency. This development allows builders and PMs to streamline their deployment processes, while investors can recognize the potential for scalable solutions in the rapidly evolving AI landscape.

Together AI's Distribution-aware Speculative Decoding (DAS) framework accelerates RL rollouts by over 50% without altering model outputs, addressing the rollout bottleneck in reinforcement learning. This improvement is crucial for large models like DeepSeek-R1, which experience significant delays during the rollout phase, consuming 70% of total training time.

Together AI's Distribution-aware Speculative Decoding (DAS) framework significantly speeds up reinforcement learning rollouts by over 50%, directly addressing a major bottleneck in training efficiency. This development is crucial for builders and PMs working on large models, as it reduces training time and costs, while investors should note its potential to enhance product competitiveness and accelerate deployment timelines.

Multi-tenant GPU clusters enable AI-native teams to share resources efficiently while maintaining isolation, preventing idle capacity and ensuring predictable access. This architecture supports pooled economics without chaos, allowing teams to operate as if they have dedicated clusters.

The development of multi-tenant GPU clusters allows AI-native teams to optimize resource usage while ensuring isolation and predictable access. This architecture can significantly reduce operational costs and improve efficiency, making it a crucial consideration for builders, PMs, and investors focused on scalable AI solutions.