AI Hardware

OpenAI partnered with Cerebras to deploy 750 megawatts of wafer-scale inference hardware, designed for real-time GPT-5 inference. Cerebras separately revived its IPO plans for mid-2026.

NVIDIA bought Groq's inference chip technology and engineering team in its largest acquisition. Groq's engineers joined a new Real-Time Inference division, reflecting the industry shift from training hardware toward inference-optimised systems.

NVIDIA's Blackwell architecture hit full-scale production after initial GB200 NVL72 systems shipped to cloud providers in late 2024. The B200 offered roughly 2.5x speed and 25x energy efficiency gains over Hopper for inference work.

AMD released the Instinct MI355X, claiming four times the performance of its MI300X for AI training and inference. The chip gave cloud providers a credible alternative to NVIDIA and some leverage on pricing.

Google researchers published TurboQuant, an algorithm that compresses the KV cache — the memory store holding context during inference — by six times, while speeding up attention computation eightfold. The approach made it practical to run much longer context windows on existing hardware, easing one of the chief bottlenecks driving the memory shortages of 2025.

NVIDIA released the GeForce RTX 5090 on 30 January 2025, priced at $1,999 and built on the Blackwell consumer architecture. With 92 billion transistors and 3,352 AI TOPS, it was twice as fast as the RTX 4090 for AI-accelerated workloads. DLSS 4 introduced multi-frame generation, producing up to three AI-rendered frames for every real frame.

Microsoft introduced the Copilot+ PC category in May 2024, requiring a minimum 40 TOPS neural processing unit from any manufacturer. The first devices shipped on Qualcomm Snapdragon X Elite, with ARM NPUs handling on-device tasks like live captions, image generation, and semantic search without a cloud call. Intel and AMD followed with their own NPU-equipped chips by year-end, making dedicated AI silicon standard in consumer laptops.

Cerebras announced its third-generation Wafer-Scale Engine on TSMC 3nm, packing roughly 4 trillion transistors onto a single wafer-sized die. The company raised $1.1 billion at an $8.1 billion valuation to scale production. It remains the largest chip ever built.

The H100 introduced a Transformer Engine built specifically for large language models, with up to 9x faster training over the A100. Demand massively outstripped supply throughout 2023, with individual GPUs trading above $40,000 on secondary markets. This was the chip behind the GPT-4 era.

Apple's first custom ARM silicon for Mac included a 16-core Neural Engine running 11 trillion operations per second on TSMC 5nm. It proved that dedicated ML hardware in a consumer device could outperform general-purpose processors in both speed and energy efficiency.

The A100 delivered 19.5 teraflops of FP32 performance with multi-instance GPU technology that let one chip run multiple AI jobs simultaneously. It became the standard training hardware for GPT-3, DALL-E, and the first wave of foundation models.

Alex Krizhevsky's deep neural network won the ImageNet competition by a wide margin, trained on two NVIDIA GTX 580 consumer graphics cards with 3 GB of memory each. The result proved that GPUs designed for gaming could train neural networks far faster than CPUs — the insight that would eventually redirect NVIDIA's entire business.