The Decisive Profile: How One Trace File Exposed the sm_120 Bottleneck in DeepSeek-V4-Flash Deployment

Introduction

In the high-stakes world of large language model deployment on cutting-edge hardware, performance optimization often resembles detective work. Hypotheses are formed, levers are pulled, and when nothing moves the needle, the only honest path forward is to gather irrefutable evidence. Message [msg 12442] in this opencode session represents exactly such a moment: after an exhaustive multi-hour optimization campaign spanning NCCL tuning, CUDA graphs, tilelang fusion compilation, expert parallelism, and checkpoint format changes—all yielding marginal or negative results—the assistant finally pivots to a definitive GPU kernel profile. The result is a single, devastating data point that explains the entire performance ceiling: the _tiled_sparse_decode_kernel consumes 63% of all GPU kernel time, running on sm_120's CUDA cores instead of its tensor cores, and no amount of configuration tuning can fix that.

This article examines that message in depth: the reasoning that led to it, the technical decisions embedded in the profiling approach, the assumptions validated and invalidated, and the new knowledge it created that fundamentally reshaped the deployment strategy.

The Context: An Optimization Campaign Hits a Wall

To understand why message [msg 12442] was written, one must appreciate the preceding struggle. The assistant had been tasked with deploying DeepSeek-V4-Flash—a 13B-parameter MoE model using FP4 quantization—on a machine with 8× RTX PRO 6000 Blackwell GPUs (sm_120 architecture). The user's target was approximately 1000 tokens per second at concurrency 16, a number that seemed plausible given the hardware's 1.9 TB/s aggregate VRAM bandwidth and the model's ~13B active parameters.

The assistant had systematically worked through every available configuration lever. NCCL protocol tuning (LL+Ring) produced no measurable effect. CUDA graphs were already enabled. The tilelang indexer fusion, which could have accelerated the sparse-attention pipeline, failed to JIT-compile on sm_120 with CUDA 13.0. Switching the MoE backend from marlin to alternatives was invalid because the FP4-packed weights would be misinterpreted by FP8 methods. Expert parallelism (EP4) was tried and actually made throughput worse—dropping from ~23 tok/s to ~14 tok/s due to PCIe all-to-all communication overhead at small batch sizes.

The user had even suggested switching to the official NVIDIA NVFP4 quantization checkpoint, which the assistant fetched and applied via PR #25820. This did move the MoE execution onto tensor-core paths (Marlin W4A16 or cutlass FP4 grouped GEMM), yielding a ~24% improvement to ~28 tok/s at C=16. But that was still orders of magnitude from the target, and the assistant had noticed something critical: the MTP (Multi-Token Prediction) verifier was consuming enough GPU memory to halve the effective batch size from 16 to 8, explaining much of the remaining gap.

At this point, the assistant faced a decision. It could continue guessing at which component was the bottleneck—the indexer, the multi-head cache, the MoE, the attention—or it could gather definitive evidence. The context messages leading up to [msg 12442] show this internal debate: "I'm also second-guessing whether the bottleneck is truly unfixable or if EP could unlock a meaningful jump," and "Before I finalize that conclusion though, I'm second-guessing whether they might be expecting a different checkpoint variant." The assistant recognized that speculation had run its course and that a proper GPU profile was the only way to silence the uncertainty.

The Profiling Methodology: A Deliberate Technical Choice

The profiling approach in message [msg 12442] reflects several deliberate decisions. First, the assistant chose to use SGLang's built-in profiler API rather than an external tool like nsys or torch.profiler directly. The sequence of commands in the preceding message ([msg 12441]) shows a three-step process: /start_profile to begin recording, a /generate call to run a 48-token decode, and /stop_profile to finalize the trace. This approach is minimally invasive—it doesn't require restarting the server with special flags or modifying the serving code—and it captures exactly the workload of interest: a realistic decode generation.

Second, the assistant chose to analyze only the TP-0 (tensor-parallel rank 0) trace file. This is a reasonable simplification: in a TP4 configuration, all ranks execute the same kernels on different data shards, so the kernel profile on any single rank is representative. The 3.8 MB gzipped JSON file contains thousands of trace events, and parsing all four would be redundant.

Third, the parsing script itself makes several filtering choices worth examining. It looks for events where ph equals "X" (the Chrome Trace Event format's "complete" event, which has a duration) and cat is one of "kernel", "Kernel", "gpu_op", or "cuda". This multi-category fallback is important because different profiling backends and CUDA versions may label GPU kernel events differently. The script also truncates kernel names to 70 characters for readability, which is a practical concession for the terminal output.

The aggregation logic is straightforward: sum durations and count calls per unique kernel name, then sort by total time descending. The output shows the top 22 kernels (the slice [:22]), though only 6 are fully displayed before the output is truncated in the message.

The Output: A Single Kernel Explains Everything

The profile output is remarkably clear. Total GPU kernel time across the trace window is 3,830,552 μs (approximately 3.83 seconds). The top kernel, _tiled_sparse_decode_kernel, accounts for 2,412.5 ms—that's 63% of all GPU kernel time. It was called 3,276 times, meaning each invocation averages about 736 μs.

This kernel is the sm_120 Triton fallback for sparse MLA (Multi-head Latent Attention) decode. On sm_100 hardware, this operation would be handled by a fused DSA (Dynamic Speculative Attention) kernel that leverages the tensor cores and achieves high occupancy. On sm_120, however, the fused implementation doesn't exist, so the system falls back to a Triton kernel that launches only 64 blocks (one per attention head) on a GPU with ~170 SMs. Most SMs sit idle while the 64 active blocks serially iterate over all 512 top-k tokens. This is the same low-occupancy pathology that plagued the earlier K2.6 verify kernel, which the assistant had previously solved with a custom CUDA kernel achieving 3-6× speedup.

The second-place kernel, _mxfp4_slot_gemv_kernel at 379.8 ms (9.9%), is the MoE gate-MV (matrix-vector) operation—also running on CUDA cores rather than tensor cores. The third, _w8a8_block_fp8_matmul at 244.0 ms (6.4%), is the FP8 GEMM that handles the remaining non-MoE linear layers. The NCCL all-reduce kernel appears at 85.4 ms (2.2%), confirming that communication is not the bottleneck.

This distribution validates the assistant's earlier hypothesis that the sparse attention kernel was the primary limiter, but it also reveals something more nuanced: the attention kernel alone is the bottleneck, consuming nearly two-thirds of GPU time. Even if the MoE and GEMM kernels were perfectly optimized, the maximum theoretical improvement would be bounded by the 37% of time not spent in the sparse decode kernel.

Assumptions Validated and Invalidated

Message [msg 12442] validates several assumptions that had been implicit throughout the optimization campaign:

  1. The bottleneck is architectural, not configurational. The assistant had suspected that the sm_120 fallback kernels were the root cause, but lacked proof. The profile confirms that no NCCL tuning, CUDA graph optimization, or parallelism strategy could address a kernel that consumes 63% of GPU time.
  2. The attention mechanism, not the MoE, is the primary limiter. Earlier analysis had focused on MoE execution, and the NVFP4 checkpoint switch did improve MoE throughput. But the profile shows that even after that fix, attention dominates.
  3. The profile is reproducible and not an artifact. The 3,276 calls to the sparse decode kernel over the trace window correspond to the number of decode steps and attention operations in the 48-token generation, which is consistent with the model architecture. One assumption that is implicitly invalidated is the idea that "more profiling" would reveal a fixable issue. The assistant had considered running a profile earlier but hesitated due to iteration cost: "Disabling cuda graphs would let me see op-level attribution, though that requires another server restart with profiling enabled." The profile ultimately confirmed that there was no hidden lever—the bottleneck was exactly what the assistant feared.

Input Knowledge Required

To fully understand this message, one needs knowledge of:

Output Knowledge Created

This message creates several pieces of actionable knowledge:

  1. A quantified bottleneck: 63% of GPU time in _tiled_sparse_decode_kernel, with precise call count (3,276) and per-invocation duration (~736 μs). This is not speculation—it's measured data.
  2. A ranked priority list for optimization: Attention (63%) → MoE GEMV (10%) → FP8 matmul (6%) → NCCL (2%). Any optimization effort should focus on attention first, as it offers the largest potential gain.
  3. A ceiling on config-level improvements: Since the bottleneck is a single kernel running on CUDA cores, no configuration change (parallelism strategy, NCCL tuning, memory settings) can address it. The only path forward is a custom kernel replacement.
  4. A precedent for the solution: The earlier K2.6 work showed that a custom CUDA verify kernel could achieve 3-6× speedup over the Triton fallback. The same approach is now indicated for the DeepSeek-V4-Flash sparse decode kernel.
  5. A stopping criterion for the optimization campaign: The assistant can now report definitively that the hardware-model combination is bottlenecked by missing fused kernels on sm_120, and that the path to 1000 tok/s requires multi-week custom kernel development.

The Thinking Process: From Speculation to Certainty

The assistant's reasoning in this message is notable for its methodological rigor. The agent reasoning block shows a clear decision: "Now I'm parsing the TP-0 trace file to identify performance bottlenecks by aggregating CUDA kernel execution times." This is preceded by the recognition that speculation had reached its limit: "Before I conclude, I owe you a definitive profile of where the 94 ms/step actually goes."

The choice of Python over shell tools for parsing is also telling. The trace file is 9.8 MB gzipped, containing hundreds of thousands of events. A shell-based approach using jq or grep would be fragile and slow. The Python script with gzip and json libraries is robust and handles the compressed file directly. The use of collections.defaultdict with a lambda for the accumulator pattern is idiomatic Python for this kind of aggregation task.

The filtering logic (e.get("ph")=="X" and e.get("cat") in ("kernel","Kernel","gpu_op","cuda")) shows awareness of the variability in trace event schemas across different PyTorch/CUDA versions. The fallback to multiple category strings suggests the assistant has encountered this variability before and learned to hedge.

The output truncation at 22 kernels (showing only 6 complete lines) is a practical choice for terminal output, but it also reflects an understanding that the top few kernels tell the story. Once the sparse decode kernel is shown at 63%, the remaining detail is secondary.

Conclusion

Message [msg 12442] is a turning point in the DeepSeek-V4-Flash deployment. After hours of pulling levers that produced no meaningful improvement, the assistant finally gathered the evidence needed to make an honest, data-driven assessment. The profile doesn't just identify a bottleneck—it quantifies it, prioritizes it, and eliminates the possibility that any configuration change could fix it. The path forward is now clear: either accept the ~28 tok/s ceiling, or invest in custom CUDA kernel development for the sm_120 sparse attention path, following the same playbook that succeeded for K2.6.

This message exemplifies a crucial skill in ML infrastructure engineering: knowing when to stop guessing and start measuring.