IBM, Together AI Launch $240M NVIDIA-Powered Inference Cluster on IBM Cloud

NVIDIA HGX B300

• First large-scale inference cluster with Together AI on IBM Cloud using NVIDIA HGX B300 systems to help enterprises run AI workloads, designed for fast and efficient production

IBM (NYSE: IBM) announced a collaboration with Together AI to deliver IBM and NVIDIA AI infrastructure. Under a multi-year $240M agreement between IBM and Together AI, IBM is positioned to deploy a large cluster of NVIDIA HGX B300 systems on IBM Cloud with expected availability in Q1 2027. Together AI will use this cluster to provide open-source model inference. This deployment is the first dedicated, large-scale cluster built for inference on IBM Cloud using HGX B300 systems and NVIDIA Spectrum-XTM Ethernet networking. According to NVIDIA, it is built to deliver 30x more AI factory output compared to prior generations.

This collaboration aims to enable Together AI to deliver better performance and token economics to enterprises as they look to efficiently scale their AI deployments. Together AI is built on the principle that open-source models are essential for the future of AI and developers should be able to build using open, modular stacks. The company recently raised an $800M Series C financing round at an $8.3B valuation to expand its AI Native Cloud and its platform spans capabilities across inference, training, fine tuning, and agentic workflows. The company reports that it has seen significant momentum for its inference product, now serving 400 trillion tokens monthly.

Together AI selected IBM with NVIDIA because of their innovative product roadmaps and their ability to deliver GPU capacity at the pace required for rapid AI scaling and lowest token cost. Building on IBM's expertise in delivering enterprise-grade cloud capabilities, this collaboration aims to help Together AI to continue its expansion into the enterprise space while making open-source AI more accessible to developers and enterprises around the world.

"Enterprises want the performance of the best frontier models without the closed-model price tag, and that only works if the infrastructure underneath is fast and reliable at scale," said Vipul Ved Prakash, CEO at Together AI. "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."

IBM and NVIDIA Expand Collaboration to Power the Next Wave of AI Innovation

"Enterprises are in a race to adopt agentic AI at scale to drive real business outcomes," said Alan Peacock, General Manager of IBM Cloud. "IBM and NVIDIA are delivering scalable, economical, enterprise-grade AI infrastructure that can help Together AI accelerate innovation for the next generation of AI infrastructure." 

"AI factories are becoming essential enterprise infrastructure—like electricity and telecommunications—turning compute and data into intelligence," said Dion Harris, Senior Director, HPC and AI Infrastructure Solutions, NVIDIA. "With NVIDIA HGX B300 systems and NVIDIA Spectrum-X Ethernet networking on IBM Cloud, IBM and Together AI will deliver an accelerated computing platform to help enterprises deploy open-source AI with the performance, efficiency and scale required for real-time AI services."

This work is the latest example of a larger collaboration between IBM and NVIDIA, who continue to work together to advance AI across infrastructure and software, to deliver performance and efficiency for enterprise and startup clients. 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. Additionally, IBM and NVIDIA recently announced progress across GPU-native data analytics, unstructured data extraction, on-premises and cloud infrastructure, and consulting services. These advancements are designed to help organizations operationalize AI at scale. For more information about the IBM and NVIDIA collaboration, visit www.ibm.com/products/gpu-ai-accelerator/nvidia.

Statements regarding IBM's future direction and intent are subject to change or withdrawal without notice, and represent goals and objectives only.

About IBM

IBM is a leading provider of global hybrid cloud and AI, and consulting expertise. We help clients in more than 175 countries capitalize on insights from their data, streamline business processes, reduce costs and gain a competitive edge in their industries. Thousands of governments and corporate entities in critical infrastructure areas such as financial services, telecommunications and healthcare rely on IBM's hybrid cloud platform and Red Hat OpenShift to affect their digital transformations quickly, efficiently and securely. IBM's breakthrough innovations in AI, quantum computing, industry-specific cloud solutions and consulting deliver open and flexible options to our clients. All of this is backed by IBM's long-standing commitment to trust, transparency, responsibility, inclusivity and service. Visit www.ibm.com for more information.

About Together AI

Together AI is the AI Native Cloud, combining state-of-the-art open-source models, high-performance infrastructure, and frontier research in AI efficiency and scalability. Founded in 2022, Together AI powers over a million of developers and some of the world's most demanding AI workloads, delivering production-scale inference, training, and reinforcement learning for the next generation of AI-native companies.

TCS Launches Autonomous Engineering Lab with NVIDIA to Accelerate Industrial AI Deployment

Tata Consultancy Services (TCS), a global leader in IT services and consulting, has announced the launch of the TCS Autonomous Engineering Lab Powered by NVIDIA at its Global Axis campus in Bengaluru. This new facility is positioned as India’s first physical AI hub dedicated to accelerating industrial AI solutions, with a focus on mobility and manufacturing.

The lab represents a strategic expansion of TCS’ collaboration with NVIDIA, moving beyond technology integration into deeper capability building and enterprise-wide transformation. By combining TCS’ Industrial Autonomy & Engineering (IA&E) expertise with NVIDIA’s full-stack AI infrastructure, the lab provides enterprises with a scalable pathway to move from pilot projects into production-scale deployment.

Sreenivasa Chakravarti, Global Head & Vice President of Industrial Autonomy and Engineering at TCS, described Bengaluru as the “engine of India’s economy,” noting that the lab harnesses this energy to reimagine what’s possible with AI. He emphasized that the facility will enable rapid movement from concept to real-world impact, shaping the future of mobility and industrial systems. Alvin DaCosta, Vice President at NVIDIA, highlighted the importance of specialized infrastructure to bridge the gap between simulation and deployment, stressing that the lab offers enterprises a robust environment to validate and implement next-generation solutions.

The lab’s offerings span a wide spectrum of industrial AI applications. TCS DriveSphere™, an AI-led mobility platform, enables software-defined vehicles through digital twins, predictive analytics, and lifecycle management. Advanced driver assistance systems (ADAS), autonomous driving capabilities, and intelligent perception systems are supported by NVIDIA’s AI stack. In manufacturing, AI is embedded into machines and plants to enable predictive maintenance, automated quality inspection, and real-time process optimization. High-fidelity digital twin simulations further accelerate design validation and scenario testing, reducing risks and improving time-to-market.

Beyond technology, the lab is designed to help enterprises reimagine products, operations, and business models. By embedding intelligence across the engineering and manufacturing lifecycle, businesses can transition toward intelligent, software-defined, and autonomous enterprises. The controlled environment allows for rapid prototyping and validation, ensuring that solutions are tested thoroughly before rollout.

This initiative builds on the longstanding partnership between TCS and NVIDIA in AI and accelerated computing. With the Bengaluru lab, the collaboration enters a new phase—one that emphasizes scaled deployment, client engagement, and transformative impact across industries worldwide.

For a global audience, the launch underscores India’s growing role as a hub for industrial AI innovation, while also highlighting how enterprises worldwide can leverage this facility to accelerate their journey toward autonomy and intelligence.

TCS to Hire 8,900 AI Engineers, Eyes Big Tech Acquisitions

Tata Consultancy Services (TCS) is planning to deploy between 5,900 and 8,900 forward‑deployed AI engineers and is actively scouting acquisitions in AI, data security, and cybersecurity — marking one of its biggest strategic pivots in years.

Key Highlights

• Scale of Deployment: TCS aims for 1–1.5% of its workforce to serve as forward‑deployed engineers (FDEs), embedding directly with clients to accelerate AI adoption.
• Headcount Impact: With ~590,000 employees at end‑June 2026, this translates to 5,900–8,900 engineers.
• Acquisition Strategy: After years of organic growth, TCS is now evaluating AI, data security, and cybersecurity acquisitions to strengthen its positioning.
• Annual Investment: TCS spends about $1 billion annually on talent development and AI accessibility.

Strategic Context

Focus Area	Details	Competitive Angle
Forward‑deployed engineers	Embed with clients, tailor AI tools to business needs	Competes with OpenAI, Anthropic, Microsoft
AI acquisitions	Target firms in AI, data security, cybersecurity	First major inorganic push since late 2025
Talent development	$1B annual spend on training, niche recruitment	Builds internal AI‑native expertise
Client differentiation	Deep knowledge of customer environments	Counters fears of outsourcing disruption

⚔ Competitive Landscape

• OpenAI Deployment Company: Embeds engineers directly in enterprises, backed by $4B funding.
• Anthropic: Expanding Claude‑trained FDEs for enterprise workflows.
• Microsoft: Scaling Copilot deployments across industries.
• Indian peers: Wipro and LTM are also building large pools of FDEs, intensifying domestic competition.

📉 Risks & Challenges

• Investor Concerns: AI could reduce demand for large engineering teams, shorten project timelines, and pressure pricing.
• Revenue Growth: TCS’s AI revenue growth slowed to 13% in Q1 2026, down from 28% in the prior quarter.
• Execution Risk: Balancing retraining vs. external hiring for thousands of FDEs may strain resources.

🌍 Why It Matters for India

• Gurugram and Bengaluru — hubs for IT services — are likely to see early hiring waves of these AI deployment engineers.
• TCS’s pivot signals that India’s IT giants are betting on AI integration services as the next growth frontier, not just cost arbitrage.