Segment 65

In this sub-session, the assistant made significant progress on multiple fronts for the Kimi K2.6 inference stack. First, it built a complete native C/C++/CUDA DDTree inference engine (kdtree-engine/), delivering validated custom kernels for GPU tree building, tree-verify attention, and tree acceptance, along with an MVP FP32 transformer engine that matched a numpy golden reference token-for-token. Second, it addressed a severe throughput regression in the live SGLang service (~32 t/s at 5k context) by building diagnostic tools and isolating the root causes to the undertrained drafter's low acceptance rate on reasoning text and KV cache fragmentation, confirming the system was behaving as expected rather than suffering from a bug. Third, it responded to the user's request to extend the maximum context length from 32k to 200k, verifying model support and memory feasibility before modifying and restarting the service.

Three Threads of Inference Engineering: Building, Diagnosing, and Extending a Speculative Decoding Stack for Kimi K2.6 3518 words

Chunks

• Building a Native CUDA DDTree Inference Engine: From First Principles to Validated Kernels
  3323 words
• The Throughput Lever: Quantifying the MoE GEMM and Mapping the Road to B300
  2214 words
• The Three Acts of Engineering: Diagnosis, Extension, and Analytical Pivot in a Speculative Decoding Stack
  3047 words
• The Architecture of Diagnosis: How Three Threads of Inference Engineering Converged in a Single Session
  3005 words
• From Diagnosis to Deployment: Three Threads of Inference Engineering for Kimi K2.6
  2532 words