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100% OPEN ACCESS RESEARCH · ZERO SIGNUP WALL · DIRECT FROM SOURCE AUDIT Published openly for distributed systems engineers, ML infra builders, and researchers. No email capture or gating.
DISCIPLINE: Distributed ML Inference & MoE Systems
TARGET: DeepInfer (DeepSeek-V4.1-Flash)
COMMIT: 9bd9cf243fc5
METHOD: Rigorous Static Source Teardown

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Defensive reverse-engineering, hardware-level architecture teardowns, and distributed ML inference audits. 100% open access, source-confirmed, zero marketing fluff.

FLAGSHIP TEARDOWN · 59 KB REPORT 100% OPEN ACCESS · NO SIGNUP

Dissecting DeepInfer: Architecture, Latency Hiding, and Memory Topologies in DeepSeek-V4.1-Flash Inference

An exhaustive, code-grounded systems dissection of DeepInfer at commit 9bd9cf243fc5. We trace zero-overhead scheduling (ZOS), low-rank FP4/FP8 Multi-Head Latent Attention (MLA), fixed-stride DFlash speculative drafting with Markov token recurrence (DSpark), deterministic Expert Parallel Load Balancing (EPLB), and the empirical hardware wall gating execution to NVIDIA Blackwell SM100/SM103 and Huawei Ascend 950.

Discipline: Distributed Inference Systems
Hardware Gate: SM100 / SM103 / Ascend 950
Focus: ZOS, MLA, MTP, EPLB, YoCo
Access: Public & Reproducible
Read Full Research Paper (Open Access) Full text available on-page with interactive navigation and evidence tagging.

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