Low GPU Utilization From Frequent Model Switching
Each model switch leaves GPUs waiting for weights to load. At 20 switches per hour, shorter load times raise effective compute utilization from 46.7% to 62.8%.
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The questions buyers actually ask, one per article. Each gives the answer first, then the data and the sources.
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Each model switch leaves GPUs waiting for weights to load. At 20 switches per hour, shorter load times raise effective compute utilization from 46.7% to 62.8%.
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Five checks for vendor inference benchmarks: baseline, platform and topology, run-to-run deviation, third-party report, reproduction material. Plus common traps.
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Split acceptance of an inference storage project into four gates on a weekly schedule: delivery, single-node baseline, main joint-test gate and stability sign-off.
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On Huawei Ascend 910B × 8, moving model storage from NFS to an FX100 all-flash array: measured serving load times for DeepSeek-32B and 70B, and where they apply.
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Both avoid full recompute. Across three concurrency levels on a 480B model, an external FX100 array gives more throughput and lower TTFT than one local NVMe drive.
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Full recompute versus reading KV cache back from FX100 on a 480B model at concurrency 16: time to first token, throughput, test conditions and how to reproduce.
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With long contexts, high time to first token usually means evicted sessions are being recomputed. Three fixes that need no new GPUs, with results from a 480B model.
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KV cache tiering keeps the KV cache that overflows GPU memory in host RAM and external flash, then reads it back instead of recomputing. Structure, gains and limits.
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The gain from KV cache tiering comes from cache hits. Short chats, low concurrency, never-repeated contexts and output-bound workloads benefit little. Here is why.
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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.
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