Ultrafast Frontier Inference Cerebras Deep Dive At Hot Chips 2026

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Introducing CS-4: The Fastest AI Accelerator in the Industry Learn more >>

Aug 25 2026 Ultrafast Frontier Inference: Cerebras Deep Dive at Hot Chips 2026 Jessica Liu

Last week at Supernova 2026, Cerebras introduced CS-4 : the fastest AI accelerator in the industry and the first system built on the new Cerebras Nexus rack-scale platform. We shared major advances in token speed, throughput, efficiency, and scalability that make CS-4 the new foundation for frontier AI. Today at the HOT CHIPS conference in Palo Alto, we shared how CS-4 achieves those gains and previewed the technology roadmap behind CS-5 and CS-6. CS-4 is the first system built on Nexus, our reusable rack-scale platform. Nexus supports three Wafer-Scale Engines, each housed in a modular compute backpack at the rear of the rack. Its modular power, cooling, and I/O architecture allows us to improve each part of the system independently, giving us a path to double token-generation speed year over year for the next several years while dramatically improving throughput and efficiency. Each compute backpack contains one WSE and its dedicated power, cooling, and I/O. The backpack reinvents the server as a modular unit designed for faster datacenter deployment: the front power rack is installed in the datacenter, and the compute backpacks can then be dropped in on-site.

More CS-4 system architecture details unveiled With advances in CS-4’s power delivery, cooling, and rack integration, the WSE delivers ultrafast AI performance reliably at datacenter scale. Power delivery built around the wafer Moving high current across a circuit board creates resistance and wastes power as heat. Conventional GPU systems place their power converters about 50 millimeters from the silicon, requiring current to travel through multiple layers of copper before reaching the processor. CS-4 places AC/DC converters about 0.5 millimeters from the wafer (100x less than GPUs), without a printed circuit board in the final power-delivery path. This allows CS-4 to deliver nearly twice as much power at almost the same voltage, with little additional resistive loss in the delivery path.

Cooling is built into each compute backpack Each CS-4 compute backpack contains its own water-conditioning system, making installation and maintenance faster in hyperscale datacenters. An energy meter tracks flow and inlet and outlet temperatures, while an actuator adjusts the flow to the cold plates. Dry quick-disconnect valves allow technicians to connect or remove a backpack without draining the system. Leak and condensation sensors can automatically place the backpack in a safe state and cut power to its power-supply modules. Water and compute remain at the rear of the rack, separated from the high-voltage AC equipment in the front. Supply and return manifolds run along the sides, while protected conduits keep fiber connections out of the service path. This allows a compute backpack to be replaced without disturbing the rack’s shared water or network infrastructure.

Power is centralized at the front of the rack The front of the CS-4 rack contains the shared power infrastructure for all three compute backpacks. The design supports several power and redundancy configurations, making it easier to integrate into different hyperscale datacenter environments. Each compute backpack can draw from up to 30 dedicated, air-cooled AC/DC power-supply modules. The modules accept up to 277 volts AC and deliver 54.5 volts DC. They support 5+1, 4+1, 3+1, and 4+2 feed-redundancy configurations, with each module protected by its own 30-amp circuit breaker. Up to six hard-wired AC feeds enter from the top of the rack. An integrated interconnect distributes power down both sides to the breakers and power supplies, eliminating additional rack-internal power cabling during field installation. The feeds share the load for each backpack, and the AC interconnect is fully phase balanced.

Nexus is designed to support multiple generations of Cerebras systems, beginning with CS-4. Its modular power, cooling, and I/O architecture allows each part of the platform to advance independently, so new technologies can be developed and deployed faster. Nexus was also co-designed to support our next-generation WSE, which will debut in CS-5. CS-5: the next speed standard CS-5, targeted for 2027, is designed to generate up to 10,000 output tokens per second per user on leading open-source models, including Gemma 4 31B and gpt-oss-120b. For agentic workloads, which often require many sequential model calls, faster output at each step can sharply reduce total task-completion time. For the largest frontier models, including multi-trillion-parameter models such as Kimi and GPT-5.6 Sol, CS-5 targets up to 5,000 output tokens per second per user and 3 million tokens per second per megawatt. The same architecture is designed to support models with more than 50 trillion parameters while maintaining interactive speeds.

CS-6: wafer scale goes 3D Building at wafer scale required rethinking packaging, power delivery, cooling, interconnect, and system design. Over the past decade, Cerebras has solved each of these challenges, turning the world’s first and only wafer-scale processor from a radical idea into a product shipping at scale. That experience has brought us to the next great frontier in computing. A wafer-scale processor already fills the largest practical area available in two dimensions. Adding substantially more memory means building upward while preserving the data locality that makes wafer scale fast. In 2024, we began turning that vision into CS-6. By integrating wafer-scale SRAM and compute with 3D-stacked DRAM through ultra-high-bandwidth connections, CS-6 is designed to dramatically expand memory capacity without sacrificing the locality that makes wafer scale fast. Wafer-scale SRAM already scales economically to accelerate even the largest models, including GPT-5.6 Sol and beyond. With tightly integrated DRAM, more of each model can reside on each system, reducing the infrastructure required to run it. The result is ultrafast inference in an order-of-magnitude smaller system footprint, bringing ultrafast AI speeds to everyone.

The architecture is the advantage AI performance depends heavily on how quickly data moves between compute cores. Conventional systems scale by connecting many GPUs and coordinating them through...