Databricks and NVIDIA: Building for the Agentic Era

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Summary

Databricks and NVIDIA are expanding their collaboration to deliver an end-to-end AI platform that accelerates model training, inference, and agentic AI development on governed enterprise data.

New capabilities include Multinode training in AI Runtime, GPU support in Databricks Free Edition, Model Serving Enhancements, and support for NVIDIA technologies such as NVIDIA Agent Toolkit.

Customers can leverage NVIDIA’s industry-specific AI frameworks directly within Databricks to accelerate use cases across healthcare, life sciences, supply chain, robotics, digital twins, and document intelligence.

The Full Stack of AI, Accelerated NVIDIA accelerated computing powers some of the most demanding AI workloads on Databricks, from large-scale training, fine-tuning, and inference to industry-specific AI solutions. Today at Data + AI Summit, we're highlighting how NVIDIA AI infrastructure lies at the center of new announcements from Databricks AI Runtime, Model Serving, and Industry AI solutions, including a look at how the new NVIDIA Vera CPU will power the next generation of agentic infrastructure. "Our partnership with NVIDIA spans the full AI lifecycle. From NVIDIA accelerated infrastructure powering distributed training in AI Runtime to software running inside our serving and developer platforms. We're excited to combine NVIDIA technology with the data and governance capabilities of Databricks to unlock incredible value for our customers: enterprise AI that's fast, scalable, and built on a foundation they can trust." — Adam Conway, SVP, Product, Databricks “Databricks enables enterprises to build, deploy, scale and govern AI agents that are informed by their most valuable resource: business data. Through our expanded partnership, NVIDIA and Databricks are supercharging the next wave of enterprise AI by embedding full-stack NVIDIA accelerated computing with Vera CPUs, Rubin GPUs, NVIDIA Quantum InfiniBand networking and NVIDIA Agent Toolkit software into the Databricks platform.” — Pat Lee, Vice President, Enterprise Strategic Partnerships, NVIDIA Here's how Databricks and NVIDIA are building an AI platform together, from GPUs for training and inference, to purpose-built CPUs for the agentic era. 1. Training and Fine-Tuning Databricks AI Runtime (AIR) brings NVIDIA GPU acceleration directly to data and AI teams, so they can train and fine-tune models on governed enterprise data without managing separate GPU infrastructure. With AIR, customers obtain the advanced NVIDIA hardware and networking, directly where their governed data is on Databricks: NVIDIA Hopper GPUs with NVIDIA Quantum InfiniBand : purpose-built for multi-node distributed training. Whether you're pre-training a foundation model or running large-scale fine-tuning, AIR provides built-in support for NVIDIA’s high-bandwidth, low-latency GPU interconnects (RDMA-capable networking) that eliminate communication bottlenecks across nodes. AIR is also being prepared for the NVIDIA Blackwell architecture, ensuring customers are always on the leading edge of accelerated computing. NVIDIA GPUs in Free Edition: at DAIS, we’re excited to announce the support of GPUs within Databricks Free Edition, supporting developers, students, and startups worldwide to build and deploy their AI workloads on GPUs. Support for NVIDIA containers: Soon, Databricks will support NGC containers and custom NVIDIA CUDA environments, enabling them to run natively on data within the platform.

AI Runtime enables seamless access to NVIDIA GPUs within Databricks. 2. Inference: NVIDIA Acceleration in Databricks Model Serving Databricks Model Serving powers production inference for thousands of Databricks customers. At the core of Model Serving, NVIDIA hardware and software deliver the low-latency, high-throughput inference at scale our customers need, across frontier models like Qwen, GPT-OSS and custom neural networks our customers build. Additional serving capabilities include NVIDIA hardware and Triton Inference Server . Model Serving supports leading inference-optimized GPUs with Triton's advanced dynamic batching and optimized performance coming soon. With Model Serving, customers can serve the models they train on NVIDIA hardware directly on managed Databricks infrastructure. 3. Agentic infrastructure: exploring NVIDIA Vera for the next compute bottleneck The rise of autonomous agents introduces a new infrastructure challenge. While GPUs excel at model inference, the agent harness, tool calls, CPU-powered analytics and managing multi-step reasoning, all run on CPUs. Today's CPUs are often the bottleneck: latency in tool calling, communication overhead between agent steps, and inconsistent performance under load all degrade the agentic experience. NVIDIA Vera is a next-generation CPU designed specifically for this workload. Engineered for three core use cases, agentic workloads, reinforcement learning, and CPU-based data analytics, Vera delivers: High-performance NVIDIA-designed, Arm-compatible cores that deliver up to 3x faster SQL queries and 80% faster agentic performance, optimized for the latency-sensitive, bursty compute patterns such as tool calls and agent orchestration Massive memory bandwidth for the data-intensive operations that agents perform between model calls Fast core-to-core communication helping deliver predictable performance as agent complexity scales

The vision is an end-to-end NVIDIA-accelerated stack on Databricks: models run on NVIDIA GPUs for inference, while the agent harness and tool calls could run on Vera CPUs, each workload on silicon purpose-built for its characteristics. Developers customize models on Databricks using proprietary data, deploy them via Model Serving, and the surrounding agentic infrastructure runs on compute designed from the ground up for that exact pattern. 4. Developer experience: making accelerated AI easier to build NVIDIA Agent Toolkit: Deploy on Databricks Built on Databricks Apps, teams can host and run NVIDIA Agent Toolkit, NVIDIA's open source development platform for building, customizing, and deploying agentic AI workflows, directly within their Databricks environment. This means you get: NVIDIA Agent Toolkit capabilities: guardrails, tool use, retrieval-augmented generation, and multi-step reasoning, running in applications hosted on Databricks....