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All Issues →Paper of the Week #3 — Half the FLOPs Is Not Half the Time
One integer halves a fine-grained MoE's expert compute (arXiv 2609.04575) and its Table 5 replicates on one A100 to within a point. The paper never reports time, so I measured it: nothing in HF transformers, nothing at batch 1 in the vLLM you run today, 1.35x at batch 8. Plus the OLMoE control and the iso-cost harness control promised in issue #2.
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Paper of the Week #3 — Half the FLOPs Is Not Half the Time
One integer halves a fine-grained MoE's expert compute (arXiv 2609.04575) and its Table 5 replicates on one A100 to within a point. The paper never reports time, so I measured it: nothing in HF transformers, nothing at batch 1 in the vLLM you run today, 1.35x at batch 8. Plus the OLMoE control and the iso-cost harness control promised in issue #2.
The paper stopped at half the experts. A quarter loses four points instead of thirty, and a model trained without renormalization tells you why.
Two cells arXiv:2609.04575 never ran: k₁=2 and k₁=3 on Qwen3.6-35B-A3B, and the whole k₂ trick on OLMoE, which was trained without renormalization. The second one is a clean test of the paper's mechanism.
One integer halves MoE expert compute. We measured the speed the paper didn't: free at batch 8, not at batch 1.
We reproduced Table 5 of arXiv:2609.04575 on one A100 and measured the throughput the paper leaves out: nothing in HF transformers, nothing at batch 1 in stock vLLM, 1.35× at batch 8.
Paper of the Week #2 — The Score Is in the Abstract, the Bill Is in Table 2
I rebuilt HoH's Planner→Developer→QA loop (arXiv 2609.01481) around Claude Code on 8 hidden-test tasks: the score gap stayed inside rerun noise while tokens tripled, 58k vs 177k. HoH's own Table 2 reports 3.25x. Plus the matched-loss control promised in issue #1, graded.
TurboQuant in vLLM on One A100 — Capacity, Speed, and Accuracy of All Four Presets on an 8B Model
vLLM 0.28, Qwen3-8B bf16, one A100 80GB: KV capacity, batched throughput, 32K decode, needle-in-haystack, and GSM8K for bf16, fp8, and all four TurboQuant presets — the 8B size vLLM's own study skipped.
Hybrid Mamba-Transformer, Measured — Qwen3.5-9B Fits 4.4x More Context and 3.6x More Requests on the Same A100
Cache memory of Qwen3.5-9B (24 linear + 8 attention layers) vs Qwen3-8B measured from 2K to 64K context on one A100: 4.4x smaller at 64K, +37% prefill and 3.6x concurrency in vLLM — Part 1's claims confirmed, with a guide to which measurements can legitimately show it.