Building AI-native memory systems, from silicon to tokens. X-Mem™ Networked Memory Tier™.

Joined July 2024
Sparse attention reads less KV data. The full KV cache still needs fast, high-capacity memory. Our XM-Sparse Pilot VM testbed with SGLang HiSparse + Netpreme X-Mem™ shows why bandwidth and capacity matter for LLM inference. See the benchmarks. netpreme.com/blog/xm-sparse-…
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Cracking the Memory Wall at #OCPSummit26: a lively discussion on AI inference, KV cache, and memory tiers. Thanks to Hanrui Wang (@hanrui_w), Sudeep Bhoja, Angela Yeung, Kartik Srinivasan, and Rakesh Cheerla (@RakeshCheerla) for joining us—and to Bob Brennan for moderating. #AIInfrastructure
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Heading to #OCPSummit26? Join Netpreme on Oct 14 from 5:30–8:30 pm for a panel and happy hour on the AI memory wall. Bob Brennan moderates a discussion with Ryan Wang (Nebius), Rakesh Cheerla (Netpreme) & others TBA. Space is limited. Request a spot: luma.com/sp6j7s7i
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Speaker update: Angela Yeung, SVP and Head of Product at @cerebras, joins our Oct 14 AI memory wall panel with Ryan Wang (Nebius), Sudeep Bhoja (d-Matrix), and Rakesh Cheerla (Netpreme). Bob Brennan moderates. Request a spot: luma.com/sp6j7s7i
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Speaker update: Sudeep Bhoja, co-founder and CTO of @dMatrix_AI, is joining our AI memory wall panel on Oct 14. We’re looking forward to hearing his perspective alongside Ryan Wang and Rakesh Cheerla.
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