The Hard Ceiling of Fallback Kernels: A Methodical Optimization Campaign on DeepSeek-V4-Flash

Introduction

In any high-performance computing project, there comes a moment when the easy levers have all been pulled, the configuration knobs have been twisted to their limits, and the team must confront an uncomfortable truth: the bottleneck is architectural, not configurational. Message [msg 12492] in this opencode session captures exactly such a moment. The assistant, having spent multiple rounds deploying, tuning, and profiling the DeepSeek-V4-Flash model on 8× NVIDIA RTX PRO 6000 Blackwell GPUs (sm_120 architecture), delivers a synthesis that is equal parts measurement report, bottleneck diagnosis, and strategic pivot.

This message is the culmination of a sustained optimization campaign. The assistant had already pulled the latest upstream SGLang, generated SM120 FP8 GEMM autotune configurations, enabled NCCL LL (low-latency) communication, and deployed MTP (Multi-Token Prediction) speculative decoding via EAGLE. Each lever had been tested individually and in combination. The results were methodically recorded. And yet, aggregate throughput remained stubbornly capped at approximately 23 tokens per second at the target concurrency of C=16 — a far cry from the user's aspirational target of ~1000 tok/s.

What makes this message so significant is not just the data it presents, but the reasoning it embodies. The assistant does not simply report failure. Instead, it uses profiling data to pinpoint why every config lever failed, identifies the two kernels responsible for 77% of decode time, explains why they are running on the wrong hardware units (CUDA cores instead of tensor cores), and proposes a ranked, data-driven plan of attack. This is the moment the conversation pivots from configuration tuning to custom kernel development — a transition that mirrors the earlier K2.6 work in the same session.

The Context: A Campaign of Diminishing Returns

To understand why this message was written, one must appreciate the journey that preceded it. The assistant had been tasked with deploying DeepSeek-V4-Flash — a massive Mixture-of-Experts (MoE) model — on a machine equipped with 8× RTX PRO 6000 Blackwell GPUs. These GPUs feature the sm_120a architecture, which includes support for FP4 and FP8 tensor-core operations. The promise of this hardware is enormous throughput for quantized neural network inference.

The deployment had already achieved a significant milestone: prefill-decode disaggregation (PD disaggregation), where the prefill phase runs on GPU 0-3 (NUMA node 0) and the decode phase runs on GPU 4-7 (NUMA node 1), with KV cache transfer orchestrated via NIXL/UCX. This architectural separation is elegant — it isolates the compute-heavy prefill from the memory-bandwidth-bound decode — but it cannot fix a fundamental mismatch between the model's kernels and the hardware's capabilities.

The assistant had then run a systematic optimization campaign, documented in earlier messages ([msg 12482], [msg 12489]). Each lever was tested:

The Message: A Data-Driven Synthesis

Message [msg 12492] opens with a concise reasoning summary that frames the entire deliverable:

"I've completed the measurement phase with upstream pull, FP8 configs, NCCL LL, and MTP applied, profiling at C=16. The results show a clear progression: baseline at 10.2 tok/s, FP8 configuration bumping it to 10.7, and MTP delivering the biggest gain at 15.7 tok/s for C=1, though C=16 plateaued around 23 tok/s regardless of config changes."

This single paragraph encapsulates the entire optimization campaign. The assistant is signaling that the measurement phase is done — no more config levers remain to be tried. The plateau at ~23 tok/s is a hard ceiling, and the message exists to explain why that ceiling exists and how to break through it.

The assistant then presents the three configurations tested:

| Config | C=1 | C=8 | C=16 | |---|---|---|---| | baseline | 10.2 | ~24 | 23.2 | | A: +FP8 cfg +NCCL LL | 10.7 | 22.6 | 23.2 | | B: A +MTP | 15.7 | 23.2 | 23.6 |

The table is deliberately sparse — no footnotes, no qualifiers. The data speaks for itself. Config B achieves a 47% improvement at C=1 (10.2 → 15.7 tok/s), but at C=16, all three configurations converge to the same ~23 tok/s ceiling. The "Per-lever verdict" section that follows is brutally honest: "NCCL LL ≈ 0 (comm is 2%); FP8-config +6% @C1 only (FP8 GEMM is 6% of decode); MTP +47% @C1 but 0% at concurrency (verifier is batch-saturated)."

This verdict is important because it explicitly rules out further configuration tuning as a viable path. The assistant is telling the user: we have exhausted the configuration space, and the bottleneck lies deeper — in the kernels themselves.

The C=16 Profile: Identifying the Bottleneck

The most critical section of the message is the C=16 profile table, which breaks down GPU kernel time by kernel name:

| kernel | C=16 | bs=1 | runs on | |---|---|---|---| | _mxfp4_slot_gemv_kernel (MoE) | 39% | 10% | CUDA-core tl.sum GEMV | | _tiled_sparse_decode_kernel (attn) | 38% | 63% | fp32 bmm | | elementwise long-tail (MoE epilogue) | ~14% | — | — | | FP8 matmul / NCCL | <1% / 1% | — | — |

This table is the Rosetta Stone of the entire optimization campaign. It reveals two critical insights:

First, the bottleneck shifts with batch size. At bs=1 (single request), attention dominates at 63% — the sparse decode kernel is iterating over all 512 top-k tokens with only 64 blocks on ~170 SMs, a classic occupancy pathology. But at C=16 (16 concurrent requests), the attention grid grows to (16, 64) = 1024 blocks, improving occupancy and reducing attention's share to 38%. Meanwhile, the MoE slot-GEMV kernel surges from 10% to 39%, because each of the 16 requests activates different experts, and the CUDA-core GEMV cannot exploit the tensor-core pathways.

Second, both dominant kernels run on CUDA cores (SIMT), not tensor cores. The _mxfp4_slot_gemv_kernel uses Triton's tl.sum reduction — a pure CUDA-core operation. The _tiled_sparse_decode_kernel is an fp32 batched matrix multiply (bmm) with online softmax, also on CUDA cores. Together, they consume 77% of decode time on hardware units that are fundamentally mismatched to the workload. This directly explains the user's <1% tensor-pipe-active measurement: the tensor cores are idle because the kernels never invoke them.

The assistant's phrasing is precise: "This exactly explains your <1% tensor-pipe-active: the two kernels that are 77% of decode run on CUDA cores / SIMT, not the sm_120a FP4/FP8 tensor cores." The bold emphasis is earned — this is the root cause that every prior measurement was pointing toward.

The Ranked Attack Plan

Having identified the bottleneck, the assistant does not stop at diagnosis. The message proposes a ranked, data-driven attack plan:

  1. Grouped FP4 tensor-core MoE (39% @C16): Swap the CUDA-core tl.sum GEMV for the in-tree cutlass_fp4_group_mm, which routes through sm_120a tensor cores. This is the highest-impact single change because it attacks the largest C=16 bottleneck and directly addresses the <1% tensor utilization.
  2. Tensor-core / split-K sparse-MLA decode (38% @C16, 63% @bs1): Reuse the split-K flash-decode approach developed earlier for K2.6. This is the primary latency lever and the secondary throughput lever.
  3. Fuse MoE epilogue/dequant long-tail (~14%): A lower-priority optimization that would consolidate the elementwise operations (silu, mul, mask, reduce, copy) that follow the MoE computation. The ranking is explicitly data-driven: "start with #1 (grouped FP4 MoE) since it's the largest C=16 share and the clearest tensor-core conversion." This is not a guess — it follows directly from the profiling data presented in the same message.

The Thinking Process: What the Reasoning Reveals

The assistant's reasoning section, which precedes the structured report, reveals the cognitive process behind the synthesis. The assistant reflects on the completed measurement phase, noting the "clear progression" and the "plateau around 23 tok/s." It explicitly connects the profiling data to the user's <1% tensor utilization observation, showing that the assistant is synthesizing two independent data sources — its own kernel profiling and the user's cufall measurement — into a unified explanation.

The reasoning also reveals the assistant's prioritization logic: "My next priority is attacking the two highest-impact issues: grouped FP4 tensor-core MoE optimization for throughput, and split-K/tensor-core sparse decode for latency and secondary throughput gains." This is a deliberate choice to parallelize the attack on both bottlenecks, recognizing that they are co-equal at the target concurrency.

Notably, the assistant does not propose any further configuration changes. The reasoning implicitly acknowledges that the configuration space has been exhausted — every lever has been tried, and the only remaining path is custom kernel development. This is a mature engineering judgment: knowing when to stop tuning and start building.

Assumptions and Their Validation

Several assumptions underpin this message, and the assistant is careful to validate them explicitly:

Assumption 1: Config tuning could significantly improve throughput. This was the working hypothesis throughout the optimization campaign. It was tested and found to be false — at C=16, no config lever moved throughput beyond ~23 tok/s. The assistant documents this clearly, preventing any future re-treading of the same ground.

Assumption 2: MTP speculative decoding would help at concurrency. This is a common expectation — speculative decoding should improve throughput regardless of batch size. The assistant's measurement shows that it does not, because the verifier becomes batch-saturated. The model card had predicted this, and the assistant confirms it empirically.

Assumption 3: The bottleneck was something tunable. This turned out to be incorrect in the most instructive way. The bottleneck was not a misconfiguration or a missing optimization — it was a fundamental architectural mismatch between the kernels and the hardware. The FP8 matmul, which does use tensor cores, accounts for less than 1% of decode time because it is only 6% of the decode compute. The remaining 94% runs on CUDA cores.

Assumption 4: The bottleneck would be the same at all batch sizes. The C=16 profile disproves this dramatically. At bs=1, attention dominates at 63%. At C=16, MoE and attention are co-equal at ~39% each. The batch-dependent shift is a crucial insight that would be missed by profiling only at single-request concurrency.

Input Knowledge Required

To fully understand this message, the reader needs knowledge spanning several domains:

Output Knowledge Created

This message creates several pieces of durable knowledge:

  1. A definitive bottleneck diagnosis: The two kernels responsible for 77% of decode time at C=16 are identified by name, with their execution platform (CUDA cores) and their share of total time.
  2. A validated ceiling: The ~23 tok/s aggregate throughput at C≥8 is established as a hard ceiling that cannot be broken by configuration changes alone.
  3. A ranked attack plan: Three optimization targets are prioritized by impact, with the grouped FP4 MoE ranked first based on its C=16 share and its clear tensor-core conversion path.
  4. A documented findings record: All data is recorded in /root/dsv4_bench/PROFILE_FINDINGS.md, ensuring reproducibility and providing a baseline for measuring future improvements.
  5. A clean handoff state: The server is running Config A, with the MTP script ready. The assistant offers to prototype the grouped FP4 MoE conversion, including a numerical-parity gate against the current kernel.

The Broader Significance

This message is a textbook example of measurement-driven optimization. The assistant does not guess at the bottleneck — it profiles, measures, and ranks. It does not propose solutions without evidence — every recommendation is tied to a specific percentage of GPU time. And it does not oversell — the recommendation is framed as a question ("Want me to prototype it?"), acknowledging that the user may have different priorities or additional context.

The message also demonstrates the value of knowing when to stop. The assistant could have continued tweaking configuration parameters indefinitely, chasing marginal gains. Instead, it recognized that the ceiling was architectural and pivoted to kernel development — the same playbook that delivered 3-6× speedups in the earlier K2.6 work. This judgment — the ability to distinguish between a tuning problem and a fundamental architecture problem — is the mark of an experienced systems engineer.

In the broader narrative of this opencode session, message [msg 12492] is the inflection point. Everything before it was configuration and deployment. Everything after it will be custom kernel engineering — writing CUDA and Triton kernels that route through the tensor cores, attacking the 77% bottleneck one kernel at a time. The message is both a conclusion and a beginning, a summary of what was learned and a roadmap for what comes next.