0
Applied AI·October 18, 2026·1 min read

'The GPU never reads the same text twice': Belgian startup Corbenic wants to turn your server's SSD into AI memory, but its 14x speedup owes more to reading less

Share

The real story in Corbenic’s claimed 14x speedup is not magic SSDs as “AI memory” but smarter cache reuse and avoiding redundant GPU reads. For teams running vLLM, SGLang, or Llama.cpp at scale, the next efficiency frontier is software-level KV cache orchestration across cheap storage tiers, not just buying more HBM.

Applied AI

Behold, Intel plans a 480GB GPU, a video card with more memory than the average laptop SSD has storage — but of course it's for AI, and curiously, it will use LPPDR5X, not even GDDR7

A 480GB, 1.3 PFLOP FP8, 350W GPU built on LPDDR5X is a bet that memory capacity and energy efficiency will matter more than raw peak bandwidth for many AI workloads. If you’re planning model architectures or infra spend, assume the next wave of accelerators will let you keep far larger models and contexts on a single card—simplifying scaling patterns but changing your cost/performance assumptions.

Applied AI

Anthropic commits billions to a 'public benefit fund'

Putting company stock into a charitable LLC hardwires a parallel power center into Anthropic’s cap table — future governance fights won’t just be between investors and management. For operators, this is another data point that AI labs are experimenting with ownership structures as much as model architectures, which may influence how you think about long-term alignment, partnership risk, and who actually controls the roadmap.