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deepseek-v41-flash-fleet-notes

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Depth-loop + serving-economics notes, scripts, sample answers
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metadata 
license: mit
tags:
  - deepseek
  - llama.cpp
  - inference
  - interpretability
  - notes
pretty_name: DeepSeek-V4.1-Flash fleet notes

DeepSeek-V4.1-Flash on a heterogeneous llama.cpp fleet — notes and patches

Working notes from serving DeepSeek-V4.1-Flash-1017 at its native Q8_0/MXFP4 precision across four machines (Blackwell + Ampere + three V100s + a 4090, llama.cpp RPC), 2026-10-17/18. Shared for reference, not as polished PRs.

  • FINDINGS-kv-sharing.md — how V4.1 shares its compressed attention cache across layer groups (owners 2, 8, 14, 20), the index-key and top-k sharing maps, how we found it, and what it means for multi-GPU serving and for interpretability work.
  • patches/ — against ggml-org/llama.cpp PR #28696 head 2076412: dsv41-kv-replicas.patch (per-device replicas of reused KV layers: 3.9 -> 9.5 tok/s on our fleet), dsv41-rpc-graph-cache.patch (per-uid RPC graph cache), dsv41-rpc-cache-verify.patch (rpc tensor-cache integrity), dsv41-mxfp4-e8m0-vector-scale.patch (in-register E8M0 scale conversion in the x86 MXFP4 kernels, +30-39 % on AVX2). The full branch, including cherry-picks from cliecy/llama.cpp with attribution, is dsv41-fleet on our llama.cpp fork.
  • quality/ — the 30-prompt set (polymer chemistry, lab technique, coding) and the collection harness we use to compare quants blind.
  • BRANCH-NOTES.md — what the branch carries and who to credit.

Headline numbers on this fleet, same model file throughout: 1.3 tok/s (naive layer split) -> 3.9 (device boundaries aligned to the KV-sharing groups) -> 9.5 (per-device replicas) -> 9.3-10 (kernel + decode fixes). Everything outside VRAM (15 expert layers in RAM) is what remains; six V100s arriving this week should remove it.

Mirror: https://huggingface.co/datasets/Atlas3D/deepseek-v41-flash-fleet-notes

Credits: Atlas3DSS (owner, direction, hardware); Claude (Anthropic) did the measurement, engine work and writing in this shop; Astra (OpenAI Codex) reviewed every patch; cliecy, nicholasshirley, smalinin, Mermiges and the PR #28696 authors for the pieces we built on. MIT, like llama.cpp.

Added 2026-10-18 (evening)

  • FINDINGS-depth-loop.md: running layers 30-35 a second time at inference, with controls; free in decode time on our layout, outputs diverge, quality scoring in progress. Patch: patches/dsv41-depth-loop.patch; raw sample answers: quality/depth-loop-sample/.
  • FINDINGS-serving-economics.md: thinking vs best-of-N wall time at ~9 tok/s, why parallel decode does not scale with CPU-resident experts, per-request reasoning caps, a one-token logprob judge on a side GPU, and gated thinking with a seeded think block (quality/gated_think.py, quality/rejudge.py).