Chunk 8.0

In this chunk, the assistant completed writing the remaining 10 improvement documents (02–11) for the local research repo, then systematically executed Tier 1 optimization tests. Piecewise CUDA graphs were blocked due to an incompatibility between `torch.compile(fullgraph=True)` and FlashInfer’s FP4 JIT code—even after patching `get_cuda_version` to avoid subprocess calls and adding `@torch.compiler.disable` to `fp4_quantize`, the `fullgraph` requirement prevented graph breaks. MSCCLPP and Single Batch Overlap were tested next; both yielded only ~2% improvement over baseline, confirming that allreduce latency is not the primary bottleneck. The assistant then moved to Expert Parallelism (EP8) via `--moe-a2a-backend flashinfer`, which successfully launched with EP8 topology and slightly lower per-GPU memory usage. However, EP8 was 10–14% slower at low concurrency and crashed under moderate load (256 concurrent requests), with server logs showing autotuner failures for the M256 tile and NCCL errors. Key achievements include establishing a rigorous baseline across four concurrency levels (1, 10, 256, 1024), ruling out communication-side optimizations as transformative, and demonstrating that the core bottleneck remains the small per-expert GEMMs (memory-bandwidth-bound). The EP8 crash is under investigation—likely due to OOM or NCCL communication failures during all-to-all—but the approach remains promising for high concurrency if stability can be resolved. The overarching theme is that hardware constraints (SM120’s 99KB shared memory, lack of TMEM, and limited FP4 throughput for small matrices) dominate performance, and only optimizations that fundamentally increase per-expert compute efficiency (e.g., EP, L2 pinning, persistent kernels) can unlock meaningful gains. The assistant continues to iterate methodically, documenting each result and pivoting when blocked.

The Systematic Optimization Campaign: From Documentation to Execution on Blackwell GPUs 2678 words

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