WEBVTT

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This October 2026 update reports two qualified batch eight serving gains.

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Packed AWQ Qwen 3 eight B improves complete request throughput by 2.28 percent over native FP 16 KV.

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Dense Qwen 3 eight B, executing in FP 16, improves by 2.49 percent.

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K 16 V 8 uses 25 percent less persistent KV payload at equal token slots.

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That saving is not total GPU memory.

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The workload is one H 100 SXM 80, tensor parallelism one,8192 input tokens, batch eight, and 128 generated tokens.

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Prefix caching and chunked prefill are off.

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Each headline is a median paired ratio from seven fresh three arm process blocks, separate from its pilot.

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Each experiment keeps the same weight checkpoint across cache arms.

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AWQ weights remain packed.

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These are workload specific results.

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Earlier writer, scale, and graph work improved decode, but long prompt prefill held back complete requests.

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Compact gathers stored keys and already quantized values into one bounded shared FP 16 workspace, then uses native FA 3 prefill.

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Casting does not recover lost precision.

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There is no full value shadow per layer.

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Decode still reads compressed values.

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AWQ compact is 6.19 percent faster than the preceding mixed stack.

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Persistent KV and temporary workspace are separate.

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Compact owns about 296.14 mebibytes of constant GPU workspace, including 256 mebibytes of shared payload, plus variable metadata and 8 mebibytes of pinned CPU storage.

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Count aliases once.

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Fixed timing pools are 48 gibibytes for AWQ and 32 for dense FP 16.

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More token slots need not reduce reserved memory.

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A separate automatic pool test confirms 58 resident requests for compact,59 for preceding mixed, and 44 for native.

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Compact admits 31.82 percent more than native after workspace costs.

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This measures capacity, not throughput or queueing.

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Do not multiply the capacity increase by the batch eight speed ratio.

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The existing AWQ eight K, batch 32 result is about 1.44 percent slower than native, with mixed prefill and decode fallback.

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The BF 16 transfer has a small speed gain, but fails frozen GSM gross loss preservation despite a higher net score.

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It remains experimental.

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A separate symmetric AWQ recipe fails quality and has no comparative timing.

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Next, the matched batch 32 study will keep all eight historical models visible.

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It will recover old workload details where possible and otherwise label a new standardized comparison.

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It will separate complete requests, completed decode, prefill overhead, and capacity pressure.

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Positive claims require quality and timing gates.

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Regressions, unsupported routes, and missing cells will be reported.

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Source and reproduction settings are linked on the decode page.
