IBM, Together AI team to offer open-source AI inference in the cloud

IBM and Together AI signed a $240 million multi-year agreement to offer open-source AI inference services on IBM Cloud, powered by Nvidia HGX B300 systems with Spectrum-X Ethernet networking. Together AI CEO Vipul Ved Prakash said the collaboration will make open-source AI more accessible to enterprises. The deal comes as Gartner forecasts AI-optimized IaaS spending to grow 96% in 2026 to $42 billion.

IBM has inked a $240 million deal with neocloud provider Together AI https://tracxn.com/d/companies/together-ai/ fcIBLE0rJMeK3FAdcfzE0H41jE36bJd0FDBWalYo6cY funding-and-investors to offer a cloud service that will run large capacity, open-source AI inference workloads https://www.networkworld.com/article/4175890/cisco-ai-traffic-is-radically-reshaping-wans.html . The service will be based on a large cluster of Nvidia HGX B300 systems running Spectrum-X Ethernet https://www.networkworld.com/article/4200086/nvidia-unveils-spectrum-x-networking-platform-designed-to-connect-millions-of-gpus.html networking and residing in IBM Cloud. Together AI will then use this cluster to provide open-source model inference services for enterprise customers. The Together AI platform supports open models such as DeepSeek https://www.together.ai/models-providers/deepseek , Nemotron, MiniMax, Kimi, and GLM that the company says gives developers and customers the freedom to customize and fine-tune https://www.together.ai/fine-tuning them at a lower-cost than a single proprietary model for their own applications. Together AI offers GPU infrastructure and AI services to developers and enterprises. A hybrid environment on IBM Cloud powered by Nvidia GPUs connected with Nvidia Spectrum-X Ethernet networking and Together AI’s inference platform gives organizations a reliable foundation to build, deploy and scale AI systems, IBM stated. In addition, the technology behind the agreement is important to IBM as it relies on infrastructure it is working to develop with Nvidia https://www.networkworld.com/article/3849538/ibm-broadens-access-to-nvidia-technology-for-enterprise-ai.html . The two vendors recently expanded their collaboration to grow the use of GPU-native data analytics, intelligent document processing, on-premises and regulated infrastructure deployments to help enterprise customers move AI from pilot to production, according to IBM. Vipul Ved Prakash, CEO at Together AI, said the collaboration with IBM will help Together AI continue its expansion into the enterprise space and make open-source AI more accessible to developers and enterprises. “Working alongside IBM with Nvidia gives us that foundation. This cluster lets us bring production-grade inference to more companies, faster, and it’s a big step in our push to make open-source AI the obvious choice for enterprises,” Prakash said in a statement https://newsroom.ibm.com/2026-08-11-IBM-and-Together-AI-Sign-Multi-Year-Agreement-to-Scale-Open-Source-AI-Inference-with-NVIDIA-AI-Infrastructure-on-IBM-Cloud . The deal is the latest to contribute to a swell of AI-optimized infrastructure as a service IaaS activity. IBM Cloud competes with AWS, Google Cloud, Microsoft Azure, Coreweave, Lambda and many others in a rapidly expanding AI IaaS market. Gartner just this week forecast https://www.gartner.com/en/newsroom/press-releases/2026-08-10-gartner-forecasts-worldwide-artificial-intelligence-optimized-iaas-spending-to-grow-96-percent-in-2026 that worldwide spending on AI-optimized IaaS is projected to grow 96% through 2026, reaching $42 billion. The market will sustain high growth and reach $66 billion in 2027, according to the firm. In particular, the growing share of inference workloads is expected to reshape cloud investment priorities: “The rise of agentic AI https://www.gartner.com/en/doc/best-practices-preparing-for-ai-agent-services-opportunities amplifies compute intensity through multistep, autonomous execution, making inference the dominant consumption model and positioning AI-optimized IaaS as a critical enabler of enterprise AI strategies https://www.gartner.com/en/ai ,” Gartner stated. “As organizations shift from model development to production-scale deployment, fine-tuned and domain-specific models DSMs are increasingly integrated into customer-facing and operational systems, requiring continuous, real-time execution rather than periodic training,” said Hardeep Singh https://www.linkedin.com/in/hardeepsingh31/ , senior principal research analyst at Gartner, in a statement. “This shift is accelerating the cloud consumption patterns and creating sustained demand for AI-optimized infrastructure.” In 2026, global spending on inference $23.3 billion will surpass that of training $19 billion , Gartner stated. Fifty-five percent of AI-optimized IaaS spending is forecast to support inference in 2026 and is set to reach 59% in 2027.