Attention is shifting from raw compute to memory, energy, and token economics as agentic workloads stress KV cache capacity and GPU costs. TPU/ASIC payback, TSMC packaging pressure, and aggressive inference pricing define the new infrastructure bottleneck.
Astera Labs' move shifts attention from compute to memory. The KV cache, critical for agentic workloads, becomes a physical constraint affecting parallelism, latency, and TCO. For self-hosted and on-premise deployments, memory hierarchy and data move...
Google's TPU systems reaching one million chips shifts the limit from hardware to power. The analysis looks at consequences for data center design, TCO, and on-premise strategies, where energy constraints become as decisive as compute capacity. No ad...
As agentic systems multiply context tokens, the KV cache grows until it saturates GPU VRAM. Astera Labs is targeting this structural limit, signaling a shift in attention from compute to memory and interconnects. For those evaluating self-hosted depl...
CrofAI sold latest-generation models at rock-bottom prices, but behind it was an OpenRouter wrapper routing requests to smaller models with markups up to 20x. Hardware inconsistencies—like running Kimi K3 on eight RTX Pro 6000 cards—exposed the fraud...
TSMC's 3nm and 2nm nodes and CoWoS packaging remain tight as AWS, MediaTek, and TPUs shift capacity volumes. For those evaluating on-premise AI hardware, scarcity of accelerators and interposers means longer lead times and cost pressure. Demand from ...
According to DIGITIMES, Google's TPU recoups its investment in about a year. The figure opens upside for MediaTek and Broadcom ASIC revenues and signals that custom silicon for training and inference is now a concrete economic option. The fast paybac...
d-Matrix presented an inference cost model at SEMICON Taiwan 2026. The point: if token prices fall while GPU depreciation, energy and memory bandwidth costs remain rigid, margins for compute capacity providers shrink. The pressure is structural and p...
