Oxmiq Labs is proposing High Bandwidth Flash (HBF) as a new capacity tier for AI inference, aiming to offer significantly more storage at a comparable cost to High Bandwidth Memory (HBM). Their presentation at Hot Chips 2026 outlines HBF hardware specifications that provide 8 to 16 times the capacity of HBM for the same price. While HBM excels in bandwidth-intensive tasks, Oxmiq suggests HBF could be effectively deployed in mixture-of-experts (MoE) models, particularly for storing expert weights and offloading KV caches, thereby optimizing cost and capacity for AI inference workloads. AI
IMPACT Could offer a more cost-effective solution for storing large AI models and their associated data, potentially lowering inference costs.
RANK_REASON The item discusses a proposed hardware specification and deployment strategy for AI compute, presented at a technical conference. [lever_c_demoted from research: ic=1 ai=0.7]
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- AI inference
- Helen Bamber Foundation
- High Bandwidth Flash
- High Bandwidth Memory
- Hot Chips 2026
- Kimi-K2 1T
- mixture of experts model
- Oxmiq Labs
- UCIe
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