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Xinyuanyi

Solutions

Starting from one storage tier, we work upward into inference-stack software and downward into datacenter planning and construction. Each line of work points to specific report IDs.

Updated

  1. Add an FX all-flash NVMe-oF tier beyond GPU memory and host RAM, and let vLLM + LMCache write overflowing KV cache into it and read it back on a hit. On a 480B model in production topology this yields 29–40% more throughput and 26–32% lower TTFT than a single local NVMe driveR2R3.

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  2. We adapt the inference stack at source level and publish a separate measured report for each platform: 8 × AMD MI308XR1, 8 × Huawei Ascend 910BR9 and MetaX N260R5. Figures from different platforms are labeled separately and never merged into one headline.

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  3. Cut the waiting first, then talk about adding GPUs. At 20 model switches per hour, effective compute utilization rises from 46.7% to 62.8%R1.

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  4. From a 128-GPU joint test to a thousand-GPU datacenter, we deliver plans you can recompute: network BOM, three-tier storage with KV tiering, power and PUE, and TCO are all modeled in Python for you to rerun with your own parameters.

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  5. We change the inference stack at source level and hand the changes over as they are: the patch, the orchestration scripts and the raw data are all in R8R8.

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Test jointly first, decide afterwards

Test jointly first, decide afterwards

Every engagement starts with a gated joint test of about 10 weeks. Four gates are passed in sequence; if one is missed, the project stops there, so you never pay for a benefit that was not demonstrated.

Contact
Wang Limin
Phone
+86 131 6181 8898
Email
mingxin@agentmail.to

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