Hardware de IA
The silicon powering everything.
TSMC adds $100 billion to Arizona chip manufacturing
TSMC announced a further $100 billion investment in its Arizona fab complex, on top of $65 billion already committed. The expansion adds fabrication plants, advanced packaging, and a research centre. The first 4nm fab is expected to begin production in early 2025.
Read more →Tesla, SpaceX, and xAI announce Terafab — a $20 billion chip factory
Elon Musk announced plans for a vertically integrated semiconductor facility in Austin, Texas, targeting 2nm process technology and eventually one million wafer starts per month. The project, budgeted at $20–25 billion, would consolidate chip design, fabrication, memory, packaging, and testing under one roof — aimed at supplying custom AI chips for Tesla vehicles, Optimus robots, and SpaceX systems.
Read more →Helium supply shock removes 30% of global chipmaking gas
A major helium facility shutdown in the Middle East removed roughly 30% of global semiconductor-grade helium from the market, leaving chipmakers including TSMC and Samsung with weeks of reserves. Helium cools silicon wafers during lithography at temperatures no other gas can maintain, and there is no substitute. Spot prices doubled within days of the disruption.
Read more →OpenAI and Cerebras sign a $10 billion inference infrastructure deal
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.
Read more →NVIDIA acquires Groq for $20 billion
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.
Read more →NVIDIA Blackwell B200 GPUs reach mass production
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.
Read more →AMD ships MI355X, its most competitive data centre GPU
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.
Read more →AI data centre power demands strain US electrical grids
AI compute pushed electrical grids toward capacity limits. The largest US grid operator projected a six-gigawatt reliability shortfall by 2027. Chip designers responded by making energy efficiency a first-class design goal alongside raw performance.
Read more →Memory shortages cause 40–60% AI deployment delays
High-bandwidth memory hit severe shortages, creating bottlenecks even as GPU supply improved. Enterprise customers reported significant deployment delays. The pattern showed that AI hardware supply chains involve far more than GPUs alone — memory, packaging, and cooling all became chokepoints in sequence.
Read more →Google TurboQuant cuts memory use 6x and speeds attention 8x
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.
Read more →NVIDIA GeForce RTX 5090 launches — Blackwell arrives for consumers
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.
Read more →xAI xAI completes Colossus — 100,000 H100s in 122 days
xAI built the Colossus supercomputer cluster in Memphis, Tennessee in 122 days, assembling 100,000 NVIDIA H100 GPUs. It became operational in September 2024 and is used to train and serve the Grok family of models. The speed of construction — roughly four months for a cluster that would normally take over a year — demonstrated what could be done when power, space, and hardware were treated as an emergency procurement problem.
Read more →Microsoft launches Copilot+ PCs — AI becomes a hardware spec
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.
Read more →Cerebras builds WSE-3 with 4 trillion transistors on a single wafer
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.
Read more →NVIDIA announces the H100 Hopper GPU
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.
Read more →Apple ships the M1, bringing neural engines to consumer laptops
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.
Read more →NVIDIA launches the A100, its first GPU built for AI from scratch
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.
Read more →Google opens TPU access to cloud customers
After developing Tensor Processing Units internally since 2015, Google made TPU v3 pods available through Google Cloud. TPUs became the training hardware behind BERT and later PaLM, establishing the template for tech giants building their own AI silicon rather than relying entirely on NVIDIA.
Read more →Google reveals it has been running custom AI chips since 2015
At Google I/O, Google disclosed that custom Tensor Processing Units had been running in its data centres since 2015, powering Search, Street View, and AlphaGo. The announcement showed that the largest AI workloads were already outgrowing general-purpose hardware.
Read more →AlexNet wins ImageNet using two gaming GPUs
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.
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