The Inference Unbundling: Oracle Bets on SambaNova for Agentic AI

LAS VEGAS – October 27, 2026 – The artificial intelligence industry has spent the last three years consumed by a singular obsession: securing enough graphics processing units to train massive foundation models. But as these models transition from the laboratory into enterprise production, a new bottleneck has emerged. The relentless, looping demands of "agentic AI"—where models autonomously execute multi-step tasks, query databases, and self-correct—are breaking the economic and thermal limits of traditional infrastructure.

Today, Oracle Cloud Infrastructure (OCI) signaled a major structural shift in how the cloud industry plans to solve this problem. In a newly announced collaboration, Oracle will deploy SambaNova Systems' fifth-generation Reconfigurable Dataflow Unit (RDU) chips, the SN50, within its data centers. The partnership represents a critical validation of non-GPU architectures by a major hyperscaler and highlights a broader industry pivot toward specialized, power-efficient silicon designed specifically for the grueling demands of AI inference.

"Our customers want the right infrastructure for every AI workload, and inference demand is growing fast," said Karan Batta, senior vice president of Oracle Cloud Infrastructure, in the official announcement. "We plan to bring SambaNova’s RDU chips to OCI to give AI builders another efficient way to run models at scale."

The move is not merely about adding another vendor to the catalog; it is an architectural rethink of how AI is served to end-users, focusing intensely on the metrics that dictate enterprise ROI: tokens per second, total cost of ownership, and kilowatts per rack.

Unbundling Inference: The Disaggregated Approach

To understand why Oracle is integrating SambaNova's RDUs alongside its massive existing fleet of NVIDIA hardware, one must look at the mechanics of large language model (LLM) inference. The process is inherently divided into two distinct phases: "prefill" and "decode."

During the prefill stage, the model processes the user's prompt to generate a key-value cache. This is a highly compute-intensive task where traditional GPUs excel. However, the subsequent decode stage—where the model actually generates the response one token at a time—is fundamentally different. Decode is constrained not by raw compute power, but by memory bandwidth. When GPUs are forced to handle both stages, they often sit idle during the decode phase, waiting for data to move through memory bottlenecks.

SambaNova's SN50 architecture capitalizes on this inefficiency through a model known as disaggregated inference. By pairing NVIDIA GPUs for the prefill stage with SambaNova SN50 RDUs for the decode stage, the architecture ensures that each piece of silicon is doing exactly what it was built to do. The key-value cache is transferred directly between the hardware pools using high-speed interconnects, adding negligible latency.

The performance gains of this unbundled approach are substantial. Independent benchmarking by Artificial Analysis recently verified that SambaNova's disaggregated setup can deliver up to twice the speed of GPU-only configurations. At the RAISE Summit earlier this year, a heterogeneous setup utilizing an NVIDIA H200 rack for prefill and a SambaRack SN50 for decode achieved a staggering 850 tokens per second on short-context workloads for the MiniMax M2.7 model. Even on complex, long-context tasks, the system sustained over 450 tokens per second.

"Oracle doesn’t wait for the market to settle; it builds ahead of it," said Rodrigo Liang, co-founder and CEO of SambaNova. "Agents are driving the next wave of inference demand. They need tokens fast, all day, at a cost that works. That’s what SN50 was built to do. OCI will put that capacity in the hands of the builders who need it."

The Power Grid Reality Check

Beyond sheer speed, the Oracle-SambaNova partnership addresses a looming crisis in the data center industry: power consumption.

The latest generation of AI accelerators demands unprecedented levels of electricity. Systems like NVIDIA's GB200 NVL72 rack consume approximately 120 kilowatts (kW) at full load. Cooling these behemoths requires complex, expensive liquid cooling infrastructure. For many enterprises and cloud providers, retrofitting existing facilities to support 100 kW+ racks is a logistical and financial nightmare. Industry analysts note that while average rack densities are rising, traditional air-cooled racks typically hit their practical limits around 20 to 40 kW.

This is where the physical footprint of the SambaRack SN50 becomes a distinct competitive advantage. Drawing an average of 20 kW per rack, the SN50 is designed to scale within existing, air-cooled data centers. By keeping the power draw within the threshold of conventional perimeter cooling and close-coupled air systems, SambaNova allows infrastructure providers like Oracle to rapidly deploy high-performance inference capacity without waiting for multi-year data center retrofits or navigating the severe constraints of the modern power grid.

For sustainability officers and IT operations directors, the math is compelling. Achieving a claimed 5X maximum speed improvement over competitive chips while operating within a 20 kW thermal envelope directly translates to the 3X lower total cost of ownership that SambaNova advertises. It transforms agentic AI from an experimental luxury into a deployable enterprise utility.

Oracle’s 'Open Silicon' Gambit

The integration of SambaNova is part of a much larger, aggressive strategy by Oracle to position OCI as the premier "open silicon cloud."

While Amazon Web Services (AWS) and Microsoft Azure have invested heavily in their own custom silicon, Oracle is taking a different route. By aggressively onboarding a diverse portfolio of third-party AI accelerators, Oracle is attempting to outmaneuver its larger rivals through sheer optionality.

At its AI World event in Las Vegas, Oracle showcased a breathtaking expansion of its hardware ecosystem. Alongside the SambaNova integration, OCI announced plans to deploy Google's Tensor Processing Units (TPUs) by 2027, making it the first hyperscaler outside of Google to offer them. The company is also rolling out Cerebras Wafer-Scale Engines, Positron AI's Atlas inference chips, and OpenAI's Jalapeño accelerators, all while continuing to expand its multi-gigawatt OCI Superclusters powered by NVIDIA.

Industry experts view this as a shrewd move. By offering a fungible cloud where customers can mix and match architectures—perhaps using NVIDIA for training, Google TPUs for specific fine-tuning, and SambaNova for agentic inference—Oracle prevents vendor lock-in and allows CIOs to optimize their infrastructure spend down to the specific workload. With Oracle reporting $67 billion in AI infrastructure contracts signed in a single quarter earlier this year, the market is clearly responding to this heterogeneous approach.

The Economics of Agentic AI

Ultimately, the collaboration between Oracle and SambaNova is a response to the changing nature of AI software. We are moving from single-shot generative AI—where a user types a prompt and waits for a single response—to continuous, agentic workflows.

In an agentic system, an AI might be tasked with researching a competitor. The model will write a search query, read the results, realize it needs financial data, write a script to scrape a database, execute the script, analyze the output, and finally generate a report. This process requires dozens of interleaved model inferences, tool invocations, and orchestration decisions, often distributed across CPUs and specialized accelerators. Recent partnerships combining RDUs with advanced server CPUs for orchestration highlight the complexity of these new workloads.

Agentic AI runs non-stop. It chains model calls together, creating unpredictable, high-volume token demand. If the underlying infrastructure is slow or prohibitively expensive, the entire economic model of autonomous AI agents collapses. They require fast, efficient tokens that deliver a return on investment within the same fiscal year.

By bringing the SN50 to Oracle Cloud Infrastructure, the industry is taking a vital step toward making agentic AI economically viable at scale. It proves that the future of artificial intelligence will not be dictated by a single chipmaker or a monolithic architecture, but by specialized, disaggregated systems working in concert to balance speed, power, and cost. As the era of AI training gives way to the era of AI inference, the companies that master this balance will be the ones that define the next decade of technological progress.