๊ฐ€์ž… ํ›„ ์ดˆ๋Œ€ ๋งํฌ๋ฅผ ๊ณต์œ ํ•˜๋ฉด ๋™์˜์ƒ ์žฌ์ƒ ๋ฐ ์ดˆ๋Œ€ ๋ณด์ƒ์„ ๋ฐ›์„ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.

KVCache.AI
@KVCache_AI
Hi, this is official account. We build systems for efficient LLM serving, including KTransformers, Mooncake and AgentENV.
๊ฐ€์ž… August 2018
109 ํŒ”๋กœ์ž‰ ์ค‘    1.1K ํŒฌ
Excited to team up with @radixark to make large-scale RL data movement faster ๐Ÿš€ Miles is built for high-performance, large-scale post-training, and Mooncake is now integrated as a rollout data-transfer backend for the fragmented, heterogeneous data moving between rollout and training in disaggregated RL. On rollout data captured from Miles: โšก 10โ€“14ร— faster remote GET โšก 1.2โ€“1.6ร— faster PUT By turning fragmented rollout objects into efficient bulk I/O while preserving their original structure, Mooncake helps reduce rollout-to-training handoff latency without changing the RL programming model. Read more:
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