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mini-DeepInfer
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Choose a subject below. Original repository documents and working outlines are marked separately.按实验、排障或设计查找。原始仓库文档与研究提纲会分别标明。
From 3% to 11.9% of DeepInfer: Three Prefill Kernels on 4 × B200从官方的 3% 到 11.9%：4 × B200 上的三项 prefill 优化
Paired, profiled measurements of the real DeepSeek-V4.1-Flash: tensor-core sparse attention, a fused indexer and a local-row MoE combine took mini-DeepInfer from 1,085 to 4,316 input tok/s/GPU — and showed where the remaining 8.4× lives.真实 DeepSeek-V4.1-Flash 上的配对、带 profile 的测量：tensor core 稀疏注意力、融合 indexer 和只合并本地行的 MoE，把 mini-DeepInfer 从 1,085 提到 4,316 输入 tok/s/GPU，并定位了剩余 8.4 倍差距所在。
Reading DeepInfer in 2,000 Lines of PyTorch用约 2,000 行 PyTorch 读懂 DeepInfer
mini-DeepInfer keeps DeepInfer's engine design — paged state, prefill/decode steps, zero-overhead scheduling and deterministic sampling — and drops the scale, so a DeepSeek-V4.1 request can be followed from the queue to its next token.mini-DeepInfer 保留 DeepInfer 的引擎设计——分页状态、prefill/decode 分步、零开销调度与确定性采样——去掉规模相关的部分，让一个 DeepSeek-V4.1 请求可以从入队一路跟到下一个 token。
Performance Report B1: A Fused Indexer Score性能分析报告 B1：融合 indexer 打分
A single fused kernel replaces the chunked einsum for indexer scoring: 12.2× faster per layer, +31% real 4-GPU prefill throughput, 3.1 GiB less peak memory.用一个融合 kernel 取代分块 einsum 做 indexer 打分：单层快 12.2 倍，真实四卡 prefill 吞吐 +31%，峰值显存降低 3.1 GiB。
Performance Report A: Sparse Attention on Tensor Cores性能分析报告 A：稀疏注意力改用 tensor core
Real V4.1 4-GPU prefill goes from 1,085 to 2,572 tok/s/GPU (2.37×) after the sparse-attention kernel moves from CUDA cores to tensor cores.稀疏注意力 kernel 从 CUDA core 改用 tensor core 后，真实 V4.1 四卡 prefill 吞吐从 1,085 提高到 2,572 tok/s/GPU（2.37 倍）。
Performance Report C1: MoE Combine Over Local Rows, In-Place Dequantization性能分析报告 C1：MoE 只合并本地行，原地反量化
Combining only the local expert rows and dequantizing in place cuts routed experts from 526 to 252 ms/step and lifts real 4-GPU prefill to 4,316 tok/s/GPU.只合并本地专家的行并原地反量化，路由专家从 526 降到 252 ms/步，真实四卡 prefill 提高到 4,316 tok/s/GPU。
A DeepInfer-Style Runtime DesignDeepInfer 风格的运行时设计
The engineering boundaries, state invariants and verification rules mini-DeepInfer commits to, written before the implementation.mini-DeepInfer 要坚持的工程边界、状态不变量和验证方法，先于代码实现写成。
mini-DeepInfer Technical Notes: From a Request to DeepSeek-V4.1mini-DeepInfer 技术说明：从请求到 DeepSeek-V4.1
A code tour of how mini-DeepInfer turns one request into reproducible V4.1 inference steps: lifecycle, attention state, paged blocks, ZOS, sampling and evidence.代码导览：mini-DeepInfer 如何把一个请求变成可复现的 V4.1 推理步骤——生命周期、注意力状态、分页 block、ZOS、采样与正确性证据。
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