The Decisive Profile: How a Single Torch Profiler Trace Exposed the sm_120 Decode Bottleneck

Message Overview

In message 12488 of a marathon optimization session deploying DeepSeek-V4-Flash on NVIDIA Blackwell RTX PRO 6000 GPUs (sm_120 architecture), the assistant executed a targeted bash command to parse a torch profiler trace captured under C=16 concurrency. The command and its output are:

[bash] timeout 60 ssh -o StrictHostKeyChecking=no root@10.1.230.171 '/root/venv_sglang211/bin/python - <<"PY"
import gzip,json,collections,glob
f=sorted(glob.glob("/root/prof/*TP-2.trace.json.gz"))[-1]
d=json.load(gzip.open(f)); ev=d["traceEvents"]
agg=collections.defaultdict(lambda:[0.0,0])
for e in ev:
    if e.get("ph")=="X" and e.get("cat") in ("kernel","Kernel","gpu_op","cuda"):
        agg[e.get("name","")[:64]][0]+=e.get("dur",0); agg[e.get("name","")[:64]][1]+=1
tot=sum(v[0] for v in agg.values())
print("C=16 profile — total GPU kernel us:",int(tot))
for n,(t,c) in sorted(agg.items(),key=lambda x:-x[1][0])[:14]:
    print(f"{t/1000:8.1f}ms {100*t/tot:5.1f}% {c:6d}x  {n}")
PY'
C=16 profile — total GPU kernel us: 30908089
 12162.2ms  39.3%   2924x  _mxfp4_slot_gemv_kernel
 11880.4ms  38.4%   2856x  _tiled_sparse_decode_kernel
  1322.0ms   4.3%    609x  void cutlass::Kernel2<cutlass_80_simt_sgemm_64x128_8x5_tn_align1
  1291.8ms   4.2%   9232x  void at::native::unrolled_elementwise_kernel<at::native::direct_
  1012.2ms   3.3%  12746x  void at::native::elementwise_kernel<128, 2, at::native::gpu_kern
   919.7ms   3.0%  13486x  void at::native::vectorized_elementwise_kern...

This single output — a ranked list of GPU kernel durations from a 40-step trace — was the decisive moment in a week-long optimization campaign. It confirmed a hypothesis that had been forming across dozens of experiments, config changes, and failed attempts: the decode phase of DeepSeek-V4-Flash on Blackwell sm_120 GPUs was spending nearly 78% of its time in two kernels that ran exclusively on CUDA cores (SIMT), completely bypassing the tensor cores that should have been delivering the bulk of the computational throughput.

Why This Message Was Written: The Reasoning and Context

To understand why this message exists, one must trace the arc of the optimization campaign that preceded it. The assistant had been tasked with deploying DeepSeek-V4-Flash — a state-of-the-art Mixture-of-Experts (MoE) model with Multi-Head Latent Attention (MLA) — on a machine equipped with 8× NVIDIA RTX PRO 6000 Blackwell GPUs. The user's target was ambitious: approximately 1000 tokens per second at concurrency.

The initial deployment achieved only ~10 tok/s at batch size 1 and ~23 tok/s at C=16 — roughly 40× short of the target. Over the preceding messages ([msg 12473] through [msg 12487]), the assistant had methodically tested every available configuration lever:

How Decisions Were Made

The decision to capture a C=16 profile was the result of a deliberate, methodical reasoning process. After the user's cufall report, the assistant recognized that the <1% tensor utilization was the "critical signal" confirming that decode was running on CUDA cores. But rather than acting on that signal alone, the assistant chose to gather quantitative data at the exact concurrency level that mattered for the throughput target.

The implementation was elegant: the assistant drove 64 benchmark prompts at max-concurrency 16 in the background, waited 12 seconds for the batch to stabilize, then triggered the torch profiler for 40 GPU steps. This produced trace files on the order of 10 MB each. The parsing script — a compact 15-line Python program piped directly over SSH — read the most recent TP-2 trace, aggregated kernel durations by name, and printed the top 14 kernels sorted by total time.

The choice of TP-2 (tensor parallelism rank 2) rather than TP-0 was pragmatic: all TP ranks run identical decode work, so any single rank's trace is representative. The script filtered for events with phase "X" (complete events) and categories matching kernel or GPU operations, summing durations and counting invocations.

Assumptions Made

Several assumptions underpin this message. First, the assistant assumed that a 40-step trace at C=16 concurrency would be long enough to produce stable kernel timing ratios. With 2924 invocations of the MoE kernel and 2856 of the attention kernel across the trace, this assumption proved well-founded — the sample sizes were large enough that individual kernel launch overheads averaged out.

Second, the assistant assumed that the TP-2 trace was representative of all ranks. This is reasonable for a TP4 configuration where each rank handles identical work, but it implicitly assumes no load imbalance across ranks. Given that the MoE routing can produce different expert assignments per token, there could be some variation, but the aggregate picture should be consistent.

Third, the assistant assumed that the torch profiler's kernel categorization would capture all relevant GPU activity. The categories used — "kernel", "Kernel", "gpu_op", "cuda" — are standard for PyTorch traces, but there's a risk that some operations (e.g., NCCL communication kernels, CUDA graph launch overhead) might be miscategorized or omitted.

Fourth, the parsing script truncated kernel names to 64 characters (e.get(&#34;name&#34;,&#34;&#34;)[:64]). This was a pragmatic choice to keep the output readable, but it means that two kernels differing only after character 64 would be merged. For the dominant kernels, this is unlikely to cause issues, but for the long template-instantiation names typical of cutlass kernels, it could theoretically conflate distinct variants.

Input Knowledge Required

To understand this message, one needs knowledge of:

  1. The DeepSeek-V4-Flash architecture: This is a Mixture-of-Experts model with Multi-Head Latent Attention (MLA). The MLA uses a sparse "absorbed" attention mechanism where the key-value cache is compressed into a latent space, and the attention computation involves a sparse decode step that selects top-k tokens from the KV cache. The MoE uses MXFP4 (microscaling FP4) quantization for the expert weights, with a slot-GEMV kernel that performs grouped matrix-vector products.
  2. The Blackwell sm_120 architecture: NVIDIA's RTX PRO 6000 "Blackwell" GPUs use the sm_120 compute capability. They feature fourth-generation tensor cores with FP4 and FP8 support, but many operations — particularly those implemented as Triton fallbacks — run on CUDA cores (SIMT) instead. The _tiled_sparse_decode_kernel and _mxfp4_slot_gemv_kernel are both Triton-based fallbacks that don't leverage the tensor cores.
  3. The optimization history: The reader must understand that every prior config lever had been exhausted. The FP8 autotune configs, NCCL tuning, MTP speculative decoding, tilelang fusion, and expert parallelism had all been tried and found insufficient. This profile was the final diagnostic step before committing to a custom kernel development effort.
  4. Torch profiler trace format: The trace events follow the Chrome trace event format (JSON), with events having a phase field (ph), category (cat), name, and duration (dur in microseconds). The parsing script aggregates by name and sums durations.
  5. The hardware topology: 8× RTX PRO 6000 GPUs in a TP4 configuration (4 GPUs for prefill, 4 for decode in disaggregated setup), connected via PCIe Gen5, with 95 GB VRAM per GPU.

Output Knowledge Created

This message created several critical pieces of knowledge:

1. The definitive kernel ranking at C=16. The two dominant kernels — _mxfp4_slot_gemv_kernel (39.3%) and _tiled_sparse_decode_kernel (38.4%) — together consumed 77.7% of GPU time. This was the quantitative confirmation that the bottleneck was split roughly evenly between MoE compute and attention compute, rather than being dominated by one or the other.

2. The tensor-core bypass confirmed. Both dominant kernels are Triton-based implementations that run on CUDA cores (SIMT). The _mxfp4_slot_gemv_kernel performs grouped matrix-vector products for MoE expert computation using FP4 weights but standard CUDA math, while _tiled_sparse_decode_kernel implements the sparse MLA attention decode as a tiled bmm (batched matrix multiply) on CUDA cores. Neither uses the sm_120 tensor cores, explaining the <1% tensor-pipe utilization the user observed.

3. The remaining kernel profile. After the two dominant kernels, the next most expensive operations were cutlass SIMT sgemm (4.3%) and various elementwise kernels (7.5% combined). These are standard PyTorch operations (layernorm, residual add, activation functions) that are already reasonably optimized. The long tail of smaller kernels collectively accounts for ~15% of time, meaning even perfect optimization of the top two kernels would leave a ~15% overhead from elementwise and communication operations.

4. The strategic implication. The profile showed that no configuration tuning could close the gap. The two dominant kernels were architectural — they were the only available implementations for sm_120 sparse attention and MXFP4 MoE. The path forward required writing custom CUDA kernels that route these operations through the tensor cores, a multi-week engineering effort analogous to the assistant's earlier K2.6 work that delivered 3–6× speedups through custom verify kernels.

5. The count data. The invocation counts revealed the relative frequency of each kernel. The MoE kernel ran 2924 times (slightly more than attention's 2856), consistent with the model's 60-layer architecture where each layer has both an attention and an MoE computation. The elementwise kernels ran tens of thousands of times, reflecting the many small operations in the transformer pipeline (layer norms, residual connections, activation functions).

The Thinking Process Visible in the Reasoning

The assistant's reasoning before this message reveals a disciplined, hypothesis-driven approach. In [msg 12482], the assistant explicitly predicted that at C=16, "MoE likely dominates" because the attention grid would expand from 64 blocks (occupancy-starved) to 1024 blocks (well-occupied). The actual result — 39.3% MoE vs 38.4% attention — was almost perfectly balanced, disproving the "MoE dominates" hypothesis but confirming the deeper thesis: both kernels were running on CUDA cores and both needed tensor-core rewrites.

The assistant also showed awareness of the user's cufall finding as a "critical signal" that "decode is running almost entirely on CUDA cores (FMA/bmm), not the FP4/FP8 tensor cores." The profile was designed to quantify this signal, turning a qualitative observation (<1% tensor utilization) into a quantitative ranking (39% MoE + 38% attention = 77% on CUDA cores).

The choice to profile at C=16 rather than reusing the earlier bs=1 profile was itself a methodological decision. The assistant recognized that the bottleneck balance could shift with concurrency, and that the target metric was throughput at C=16, not single-request latency. This measurement-driven approach — test the hypothesis at the actual operating point, not an extrapolation from a different regime — is a hallmark of rigorous performance engineering.

Mistakes and Incorrect Assumptions

The most notable incorrect assumption was that MoE would dominate at C=16. The assistant's reasoning was sound: at bs=1, the attention kernel launches only 64 blocks (1 batch × 64 heads), which is severely occupancy-starved on ~170 SMs. At C=16, the grid becomes 1024 blocks (16 × 64), which should fully occupy the SMs and potentially make the attention kernel less of a bottleneck. The MoE kernel, by contrast, is inherently parallel across experts and should scale with batch size.

The actual result showed near-perfect balance between MoE and attention. This suggests that even with full SM occupancy, the sparse attention kernel's serial iteration over 512 top-k tokens per head creates a fundamental compute-bound path that doesn't benefit from occupancy alone. The kernel is not occupancy-starved at C=16 — it's compute-bound by the serial top-k loop, which is an algorithmic limitation that no amount of parallelism can fully resolve without a fundamentally different approach (e.g., split-K tensor-core attention).

Another implicit assumption was that the torch profiler's 40-step capture would not significantly perturb the server's behavior. The profiler adds instrumentation overhead, and the trace capture itself consumes CPU and memory resources. However, the fact that the server continued serving requests normally (as evidenced by the successful benchmark run) suggests this perturbation was minimal.

The assistant also assumed that the TP-2 trace was representative. While this is reasonable for a homogeneous TP configuration, there could be subtle load imbalances if the MoE routing produces different expert distributions across ranks. The MoE kernel's runtime depends on which experts are activated and how many tokens are routed to each, which can vary per rank. The near-identical invocation counts for MoE and attention (2924 vs 2856) suggest the workload was well-balanced, but a more rigorous analysis would compare across all four TP ranks.

Conclusion

Message 12488 represents the decisive diagnostic moment in a complex optimization campaign. It transformed a qualitative observation (<1% tensor utilization) into a quantitative, ranked kernel profile that definitively identified the two bottlenecks — _mxfp4_slot_gemv_kernel at 39.3% and _tiled_sparse_decode_kernel at 38.4% — that together consumed 78% of decode time on CUDA cores rather than tensor cores.

This profile was the product of methodical, hypothesis-driven engineering: every config lever had been tested and eliminated, the user's cufall observation had provided the qualitative signal, and the profile at the target concurrency provided the quantitative confirmation. The result was unambiguous: no amount of configuration tuning could close the ~40× gap to the throughput target. The only path forward was custom tensor-core kernels — the same playbook that had delivered 3–6× speedups in the assistant's earlier work on Kimi K2.6.

The message also illustrates a broader lesson in performance engineering: when optimization levers fail to move the needle, the correct response is not to try more levers but to profile at the actual operating point and understand the fundamental bottleneck. The assistant's disciplined approach — measure, hypothesize, test, conclude — turned a frustrating dead end into a clear, actionable roadmap for the next phase of work.