CME Group plans to launch Silicon Data H100 Rental Index Futures and Silicon Data B200 Rental Index Futures on October 5, pending regulatory review, creating the first standardized financial instrumen…
CME Group will launch two compute futures contracts on October 5, 2026, enabling AI companies and investors to hedge against volatile GPU rental costs. The contracts, developed with Silicon Data, trac…
Nvidia released Nemotron 3.5 Lightning on August 11, an open-weight reasoning model with 30 billion total parameters and 3 billion active per token, designed to run agent workloads on a single Nvidia …
Nvidia Corp. is seeking $500 billion to fund AI infrastructure, partnering with financial giants to bridge the financing gap for customers building large-scale GPU clusters. The investment targets dat…
Nvidia Corp. released Nemotron 3.5 Lightning, a 30 billion-parameter mixture-of-experts model that delivers up to four times the output speed and 30% faster agentic task completion, alongside NeMo Swi…
Nvidia's H100 and B200 chips are increasingly treated as appreciating assets rather than depreciating capital expenditures, according to a commentary on the AI hardware market. This shift is driven by…
Nvidia is shifting AI infrastructure financing from operational expenditure to capital expenditure, positioning itself as the anchor that makes these investments safe for Wall Street, according to a n…
Stoa Markets has launched a structured marketplace for new and used AI servers, standardizing the Request for Quote process to bring transparency to GPU hardware pricing, and has already seen over $30…
Leopold Aschenbrenner's prediction of AGI by 2027 faces skepticism as scaling laws show diminishing returns, with the jump from GPT-3 to GPT-4 being a seismic shift but later iterations offering only …
A new analysis argues that AI accessibility, not raw capability, will determine which companies and countries win the next decade, citing hardware costs and the need for open-source models as key fact…
NVIDIA's H100, H200, and B200 GPUs offer different trade-offs for AI workloads, and choosing among them depends on factors like model size, memory requirements, and whether the use case is training or…
Google DeepMind released DiffusionGemma, an open-weight language model that generates text via discrete diffusion, achieving roughly 1,500 output tokens per second on a single H100 compared to about 3…
Nvidia's Jensen Huang announced at GTC that Vera Rubin delivers approximately 10x more inference throughput per watt compared to Blackwell (B200), but the comparison point matters because most organiz…
A 180-line roofline model built by an independent developer shows speculative decoding on Llama-3-70B collapses at batch 224, turning a 1.92x speedup into a 0.74x slowdown by batch 512, while the same…
A cost analysis across 24 providers and 378 GPU rental rates found that the price of one million output tokens ranges from $0.09 on a single AMD MI355X to $290.12 on eight NVIDIA H100s, with the gap d…
A developer's analysis shows that INT4 weight-only quantization speeds up decode but not prefill, because prefill is compute-bound while decode is memory-bound. The crossover point where a GEMM become…
Atomarine proposes deploying small modular nuclear reactors on maritime vessels to power floating data centers, addressing energy bottlenecks for AI training clusters. A 4,000-GPU cluster requiring 40…
Microsoft has decided to keep its capital expenditure unchanged, signaling a strategic pivot from building massive AI infrastructure to maximizing the return on existing hardware, according to an anal…
Compute costs could rise 10x or more in coming years, according to an analysis of AI lab economics. If a human-level software engineer could run on an H100 equivalent, that GPU should rent for over $2…
Semiconductor stocks are sliding across US and Asian markets as investors shift from the AI hype cycle to demanding proof of returns on massive capital expenditures. Big Tech is spending billions on N…