Inside AI Inference: Memory and Storage Architecture
AccBalanced · x · 2026-07-11
This podcast dives into the future of memory and storage architecture in AI inference. Key takeaways include:
- KV cache demand outpaces cost reductions: Even a 100x drop in KV cache costs could be swallowed by new usage, as demand continues to surge.
- Interconnects and buses: NVLink offers more lanes than PCIe, highlighting the growing importance of network and interconnect performance in AI systems.
- Storage tier design: WEKA uses NAND-based pooled storage via RDMA / NVLink to deliver near-DRAM network access speeds.
- CXL roadmap: Guests argue CXL needs a dedicated bus rather than current stitched-together approaches.
- DeepSeek's cache read costs: Noted to be roughly 87x cheaper than traditional solutions, though limited to China.
- Industry outlook: The panel suggests SaaS giants and Neoclouds might eventually merge.
Related event: AI Inference Faces KV Cache and Bandwidth Bottlenecks(3 posts)→
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