VRAM and memory bottlenecks are central for local inference, with RTX 5090 64/96 GB rumors and tests showing KV cache can consume as much memory as model weights. Shifts in GPU hardware from AMD, NVIDIA, and Intel are reshaping options for self-hosted AI.
AMD CEO's declaration, prioritizing data center GPUs, marks a pivotal shift in the AI hardware landscape. This analysis explores the profound implications for organizations pursuing on-premise LLM deployments, redefining the Total Cost of Ownership, ...
AMD's CEO has defined the company as 'data center first', effectively closing the era of cheap consumer GPUs as a strategic priority. For anyone planning self-hosted LLM inference, the signal matters: silicon, packaging, and VRAM are shifting toward ...
NVIDIA has released CUDA 13.5 together with the Linux driver 620.70.06. The central change is general availability support for Vera Rubin, the NVIDIA GPU architecture. For teams working on self-hosted infrastructure and LLMs, the toolchain becomes av...
The rumor about GeForce RTX 5090 with 64 and 96 GB of VRAM hits the critical bottleneck for local LLM users: video memory. An analysis of implications for self-hosted deployments, quantization, TCO, and data sovereignty, beyond the hardware announcem...
A US GPU repair technician has reportedly claimed the existence of RTX 5090 cards with 64 and 96 GB of VRAM. The unverified rumor touches the core constraint for local inference: more video memory enables larger models, less aggressive quantization, ...
Intel has released version 26.40.40196.8 of its open-source Compute Runtime for Windows and Linux. The release prepares support for future D Series GPUs and OpenCL LEO extensions targeting Nova Lake P. More than a maintenance update, it signals that ...
A Mac M4 Max test with a Qwen3.8 27B model and 164k tokens of context shows cache and KV can cost as much as weights. The old 64GB rule no longer holds.
As first production units ship, NVIDIA revises some specifications for its Rubin GPU platforms. The timing signals a shift from validation to supply and forces AI infrastructure planners to reassess integration, power, and cooling assumptions.
