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Scout Report
Ben Levy is an emerging guard prospect in the Class of 2027 who is beginning to turn heads at La Lumiere School, one of the nation's premier prep basketball programs. Standing 6'3", Levy possesses the ideal size for a modern guard and is developing his skills within a system that has produced numerous Division I talents and NBA players over the years. His enrollment at La Lumiere demonstrates his commitment to competing at the highest level of prep basketball and positioning himself for future collegiate opportunities. While Levy has yet to receive his first official scholarship offer, his presence at La Lumiere puts him in an excellent environment to showcase his abilities against top-tier competition. The Lakers' rigorous schedule and elite coaching staff provide the perfect platform for young prospects like Levy to refine their games and gain exposure to college scouts. As a member of the Class of 2027, Levy still has ample time to develop his skills and build his recruiting profile. Given La Lumiere's track record of player development and the program's connections throughout college basketball, it would not be surprising to see Levy begin generating serious collegiate interest as he continues to mature and showcase his talents on the court.
Updated Feb 5, 2026 · Analysis by PrepRadar Scouting Team
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As we get into @NVIDIAGTC week, one topic I expect to get some attention is on the storage front. GPU utilization in AI inference is a storage problem as much as a compute problem. @Signal_65 worked with @HPE and @KamiwazaAI to test KV-Cache offloading to the HPE Alletra Storage MP X10000, and the results were significant. Full report: https://signal65.com/research/maximizing-gpu-utilization-with-hpe-alletra-storage-mp-x10000/ Key findings from our testing: ➡️ Output token generation rates increased up to 19.4x compared to systems with no KV-Cache ➡️ Time to first token improved up to 21.5x vs. no KV-Cache ➡️ Even vs. memory-only offload, adding the X10000 delivered a 5.9x token rate increase and 5.6x TTFT reduction ➡️ RDMA for S3 storage delivered up to 2x the throughput of traditional S3 over HTTP with 80% lower latency and dramatically reduced CPU overhead That last point matters. The table below shows why GPU Direct Storage via RDMA changes the equation: ~20 GB/s+ throughput, 5.1x latency reduction, and consistent P99 performance where traditional S3 showed high jitter.
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