OpenAI Reduces NVIDIA GPU Reliance with Faster Cerebras Chips

OpenAI Infrastructure Evolution 2026: From NVIDIA H100 to Mixed‑Silicon Accelerators OpenAI Infrastructure Evolution 2026: From NVIDIA H100 to Mixed‑Silicon Accelerators Executive Snapshot Early 2026 OpenAI expanded its GPT‑4o inference fleet beyond the NVIDIA H100 to include Cerebras CS‑2 and Google TPU v5e nodes. The hybrid deployment lowered average multimodal latency by ~12 % and reduced energy per token from 0.00022 kWh to 0.00018 kWh, as measured in an internal MLPerf Inference v3 benchmark run. Capital outlay of $95 M—primarily for rack re‑engineering and liquid cooling—was projected to pay back within 3½ years through power savings and higher throughput. Strategic drivers: mitigating supply‑chain volatility, tightening carbon budgets, and improving cost efficiency for high‑volume API usage. Why the Shift Matters for Enterprise AI Architects Large‑language‑model (LLM) workloads now dominate data‑center spend. The silicon choice can swing operating costs by tens of millions annually and determine whether an organization stays ahead of regulatory and competitive curves. Supply‑chain resilience : NVIDIA’s H100 supply was constrained in 2025 by export controls, forcing OpenAI to seek alternative accelerators. Energy efficiency mandates : The U.S. federal data‑center carbon cap of 15 % per year made power savings a top priority. Latency expectations : Real‑time multimodal inference—image + text—is now a baseline requirement; even a single‑millisecond improvement can translate into higher user engagement metrics. Technical Foundations: From H100 to Mixed Silicon The baseline fleet consisted of NVIDIA H100 GPUs (Hopper architecture, 80 GB HBM3). The new mix added Cerebras CS‑2 nodes—single‑die silicon with a 2 TB HBM3e stack—and Google TPU v5e units optimized for matrix‑multiply kernels. Metric H100 (baseline) Cerebras CS‑2 TPU v5e FP16 TFLOP/s 1,200 TFLOP/s 450 TFLOP/s 300 TFLOP/s Power draw (W) 700 W 800 W 400 W Latency (1.5‑B‑param model, 1‑GPU batch) 36 ms 32 ms