{"slug": "ibm-inks-240m-infrastructure-deal-with-ai-optimized-cloud-operator-together-ai", "title": "IBM inks $240M infrastructure deal with AI-optimized cloud operator Together AI", "summary": "IBM Corp. will provide infrastructure to Together AI Inc. as part of a $240 million deal announced today, deploying a large cluster of HGX B300 systems on IBM Cloud in the first half of 2027. The platform processes about 400 trillion tokens per month, and the cluster will host inference workloads using open-source AI models. Together AI CEO Vipul Ved Prakash said, \"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.", "body_md": "### IBM inks $240M infrastructure deal with AI-optimized cloud operator Together AI\n\nIBM Corp. will provide infrastructure to artificial intelligence startup Together AI Inc. as part of a $240 million deal [announced](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) today.\n\nThe partnership comes a few weeks after the latter company [raised](https://siliconangle.com/2026/07/01/together-ai-raises-800m-grow-ai-optimized-public-cloud/) $800 million in funding. The consortium that provided the capital included Nvidia Corp., whose chips IBM will use to run Together AI’s workloads.\n\nSan Francisco-based Together AI operates a cloud platform optimized for machine learning. The platform runs on infrastructure that the company rents from partners such as Amazon Web Services Inc. Developers can use it to train custom AI models, fine-tune existing ones and perform inference. IBM disclosed today that the platform processes about 400 trillion tokens per month for customers.\n\nThe infrastructure deal announced today will see the tech giant deploy a “large cluster” of HGX B300 systems for Together AI. The hardware will become available through IBM Cloud in the first half of 2027.\n\nThe HGX B300 is a motherboard equipped with eight Blackwell Ultra chips. The Blackwell Ultra was Nvidia’s most advanced graphics card until the [introduction](https://siliconangle.com/2026/01/05/nvidia-debuts-rubin-chip-336b-transistors-50-petaflops-ai-performance/) of the Rubin in January. It can provide 15 petaflops of performance when processing data in the NVFP4 format.\n\nThe Blackwell Ultra comprises two chiplets linked together by a custom interconnect. They host 160 computing modules called streaming multiprocessors, which each comprise more than 130 cores. They also contain memory optimized to store intermediate results, temporary data that AI models generate while processing prompts.\n\nThe HGX B300 uses eight ConnectX-8 SuperNIC chips to move data between its Blackwell Ultra accelerators. A BlueField-3 data processing unit, or DPU, coordinates traffic to and from external systems.\n\nHardware makers use the HGX B300 to build AI servers that also include contain components. According to Nvidia, the machines [include](https://docs.nvidia.com/enterprise-reference-architectures/hgx-ai-factory/latest/components.html) at least two terabytes of memory and a pair of central processing units with 56 cores apiece. Those CPUs connect to two PCIe switches that enable users to attach several terabytes NVME flash memory.\n\nTogether AI will use IBM’s HGX B300 cluster to host workloads for customers of its public cloud. In particular, it will run inference workloads that use open-source AI models.\n\nThe AI startup offers five different inference services as part of its platform. One is optimized for media generation models that run in containers. Another service, Provisioned Throughput, is charged by the token to help customers optimize their costs. Together AI’s other inference offerings provide serverless infrastructure, dedicated machines and a batch processing environment that trades off some performance for a price reduction.\n\n“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,” said Together AI Chief Executive Vipul Ved Prakash.\n\n##### Image: [IBM](https://www.ibm.com/products/virtual-servers-classic)\n\n# A message from John Furrier, co-founder of SiliconANGLE:\n\nSupport our mission to keep content open and free by engaging with theCUBE community. **Join theCUBE’s Alumni Trust Network**, where technology leaders connect, share intelligence and create opportunities.\n\n**15M+ viewers of theCUBE videos**, powering conversations across AI, cloud, cybersecurity and more** 11.4k+ theCUBE alumni**— Connect with more than 11,400 tech and business leaders shaping the future through a unique trusted-based network.\n\n# Are you AWS customer? 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Our new proprietary theCUBE AI Video Cloud is breaking ground in audience interaction, leveraging theCUBEai.com neural network to help technology companies make data-driven decisions and stay at the forefront of industry conversations.", "url": "https://wpnews.pro/news/ibm-inks-240m-infrastructure-deal-with-ai-optimized-cloud-operator-together-ai", "canonical_source": "https://siliconangle.com/2026/08/11/ibm-inks-240m-infrastructure-deal-ai-optimized-cloud-operator-together-ai/", "published_at": "2026-08-12 00:36:43+00:00", "updated_at": "2026-08-12 01:05:02.661733+00:00", "lang": "en", "topics": ["ai-infrastructure", "ai-products", "artificial-intelligence"], "entities": ["IBM Corp.", "Together AI Inc.", "Nvidia Corp.", "Amazon Web Services Inc.", "HGX B300", "Blackwell Ultra", "Vipul Ved Prakash"], "alternates": {"html": "https://wpnews.pro/news/ibm-inks-240m-infrastructure-deal-with-ai-optimized-cloud-operator-together-ai", "markdown": "https://wpnews.pro/news/ibm-inks-240m-infrastructure-deal-with-ai-optimized-cloud-operator-together-ai.md", "text": "https://wpnews.pro/news/ibm-inks-240m-infrastructure-deal-with-ai-optimized-cloud-operator-together-ai.txt", "jsonld": "https://wpnews.pro/news/ibm-inks-240m-infrastructure-deal-with-ai-optimized-cloud-operator-together-ai.jsonld"}}