The Allreduce Fusion Odyssey: Patching Through Layers of Architecture Gates to Unlock SM120 Performance

Introduction

In the high-stakes world of large-scale ML inference optimization, few challenges are as instructive as the collision between software assumptions and hardware reality. This article synthesizes a dramatic arc within a multi-session effort to deploy the GLM-5-NVFP4 model — a massive Mixture-of-Experts (MoE) language model — across eight NVIDIA RTX PRO 6000 Blackwell GPUs. The session had already achieved remarkable throughput gains, jumping from ~880 tok/s to ~3,740 tok/s by enabling FlashInfer CUTLASS MoE autotune for SM120 and increasing --max-running-requests to 1024. Yet a stubborn bottleneck remained: GPU power draw hovered around 250W out of a 600W TDP, indicating the hardware was severely underutilized.

The root cause was traced to FlashInfer's allreduce fusion — a critical optimization that overlaps inter-GPU communication with computation — which was entirely disabled on the SM120 architecture of the RTX PRO 6000. What followed was a deep investigative spiral through multiple layers of the software stack: from Python runtime checks, through JIT compilation orchestration, down to CUDA preprocessor directives and inline PTX assembly. The assistant systematically dismantled each architecture gate, only to discover that some hardware boundaries cannot be crossed with software patches alone.

The Performance Ceiling: Why Allreduce Fusion Mattered

The GLM-5-NVFP4 model, with its MoE architecture, requires frequent allreduce operations to synchronize expert outputs across the eight tensor-parallel GPUs. In a Proxmox virtualized environment where each GPU sat on its own PCIe root complex — preventing direct peer-to-peer DMA — these allreduce operations became the dominant bottleneck. Each of the model's ~78 layers performs two allreduces per forward pass, totaling 156 synchronization points where GPU SMs sit idle while data traverses the PCIe bus.

FlashInfer's allreduce fusion mechanism was designed precisely to mitigate this problem. By fusing the allreduce communication into the MoE GEMM kernel, it overlaps data transfer with computation, keeping the GPU occupied during what would otherwise be idle waiting. The problem was that this fusion was explicitly gated to only work on NVIDIA GPU architectures SM90 (Hopper, e.g., H100) and SM100 (datacenter Blackwell, e.g., B200). The RTX PRO 6000 uses SM120 — the consumer/professional variant of Blackwell — which was excluded at every level of the software stack.

The First Attempt: A Crash and a Retreat

The assistant's initial approach was straightforward: patch the architecture gates in SGLang's communicator.py and server_args.py to include SM120 alongside SM90 and SM100. The changes were surgical and seemingly correct — the function is_sm120_supported() already existed and returned True on the target hardware. The server was restarted with --enable-flashinfer-allreduce-fusion (see [msg 751]).

The result was an immediate crash with a revealing error: RuntimeError: No supported CUDA architectures found for major versions [9, 10] ([msg 754]). The error traced back to flashinfer/jit/comm.py, where the function gen_trtllm_comm_module() called get_nvcc_flags_list(supported_major_versions=[9, 10]), explicitly filtering out SM120 (major version 12) from the JIT compilation pipeline. The assistant initially reverted the changes, concluding that \"the SM90/SM100 gate was there because the kernels don't exist for SM120\" ([msg 756]). The todo list was updated with a cancelled item: \"Fix FlashInfer allreduce fusion for SM120 — BLOCKED (kernel not compiled for SM120).\" [1][2][3]

The Permission to Fork

Then came a pivotal intervention from the user. In [msg 768], the user wrote: \"Please think big and don't be afraid to fork/modify code.\" This single sentence transformed the session from a configuration-tuning exercise into a deep systems hacking investigation. The assistant was no longer limited to working within the constraints of upstream dependencies — it could modify them directly. [14]

This directive was the catalyst for everything that followed. The assistant re-examined the problem with fresh eyes, tracing the error from the Python-level crash back through the JIT compilation system and into the actual CUDA kernel source code.

Tracing the Gates: A Multi-Layer Investigation

What emerged was a systematic campaign of source-level investigation and patching across two repositories. The assistant discovered that the allreduce fusion was blocked not by one gate but by a cascade of them, each at a different layer of abstraction.

Layer 1: The JIT Compilation Gate. The first barrier was in flashinfer/jit/comm.py, where gen_trtllm_comm_module() called get_nvcc_flags_list(supported_major_versions=[9, 10]). This Python-level filter explicitly rejected SM120. In [msg 766], the assistant patched this to supported_major_versions=[9, 10, 12], adding SM120 to the allowed list. [12][15]

Layer 2: The Python Runtime Gates. The SGLang server had two additional checks: communicator.py checked (_is_sm90_supported or _is_sm100_supported) before enabling fusion, and server_args.py had a similar gate for auto-detection. Both were patched to include _is_sm120_supported ([msg 767], [msg 769]). [13][15]

Layer 3: The CUDA Preprocessor Gate. This was the most critical discovery. By reading the actual CUDA header files (<msg id=779-780>), the assistant found that trtllm_allreduce.cuh contained:

#if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900) && (__CUDA_ARCH__ < 1200))
  cudaGridDependencySynchronize();
#endif

The condition __CUDA_ARCH__ < 1200 explicitly excluded SM120 (where __CUDA_ARCH__ equals 1200) from using cudaGridDependencySynchronize(), a cooperative grid synchronization primitive available on Hopper and datacenter Blackwell. The assistant analyzed the surrounding code and determined that this synchronization call was an optimization, not a correctness requirement — the core allreduce logic was identical regardless of the architecture gate. In [msg 781], the patch was applied: the condition was changed to simply __CUDA_ARCH__ >= 900, removing the upper bound. [25][26][27]

Layer 4: Verification of Related Headers. The assistant didn't stop with the primary kernel header. It systematically checked every related CUDA source file — trtllm_allreduce_fusion.cuh, trtllm_moe_allreduce_fusion.cuh, and the .cu source files — for additional SM120 exclusions. The .cu files contained zero SM-specific architecture guards ([msg 774]), confirming the kernels were written in portable CUDA C++. The fusion headers used >= 800 and >= 1000 guards which naturally included SM120 (arch 1200). No < 1200 exclusions were found elsewhere. [20][21][29][30][31]

Layer 5: The SGLang Python Integration Layer. Finally, the assistant checked flashinfer_comm_fusion.py — SGLang's Python wrapper for the fusion functionality — and confirmed it contained no SM-specific gates (<msg id=787-788>). The Python layer simply called into the compiled FlashInfer module without any architecture checks. [33][34]

The Moment of Truth: Launch and Benchmark

With all gates patched and the JIT cache cleared ([msg 786]), the assistant launched the server in [msg 790] with --enable-flashinfer-allreduce-fusion and a carefully tuned set of environment variables encoding hard-won knowledge about the PCIe topology. [35][36]

The initial monitoring was confusing. A bash loop in [msg 792] returned \"CRASHED\" after detecting a sigquit signal in the log. But when the assistant investigated further in <msg id=793-794>, it discovered the server was actually still loading — the \"CRASHED\" signal was a false positive caused by stale log entries from a previous server instance. [38][39][40]

In [msg 795], the assistant confirmed that the allreduce IPC handles had been allocated successfully across all 8 ranks. The JIT compilation had worked. The TRT-LLM allreduce fusion had initialized with SM120 support for the first time. The server reached \"Uvicorn running on http://0.0.0.0:8000\" and began serving requests. [41][42][43][44]

But the benchmark results in [msg 800] told a devastating story: total token throughput dropped to 236 tok/s — a catastrophic regression from the ~3,740 tok/s achieved without allreduce fusion. GPU power draw fell to ~125W, barely a fifth of the 600W TDP. The fusion was functional but catastrophically slow. [46]

The Diagnosis: Why It Failed

The assistant's diagnosis in [msg 801] was prescient: the cudaGridDependencySynchronize() calls that were gated behind the < 1200 exclusion were not merely optimizations — they were essential for efficient allreduce on multi-GPU systems. By removing the upper bound, the assistant had enabled these calls on SM120, but the consumer Blackwell architecture may not support cooperative grid dependencies with the same efficiency as datacenter Blackwell. The synchronization primitive that worked on SM100 was causing excessive serialization or stalls on SM120. [47]

The assistant reverted the allreduce fusion changes in [msg 802] and pivoted to alternative optimization strategies: NCCL tuning, --num-continuous-decode-steps 4, and the flashinfer_trtllm MoE backend. [48][49][50]

Lessons Learned

This episode teaches several enduring lessons about adapting ML inference stacks across hardware generations.

Architecture gates are usually there for a reason. When upstream developers explicitly exclude an architecture from a feature, it is rarely an oversight. The < 1200 gate in trtllm_allreduce.cuh reflected real differences in how SM120 handles cooperative grid synchronization. The assistant's patches were technically correct — the kernel compiled and ran — but functionally incorrect, as performance was worse than without fusion.

JIT compilation success does not guarantee runtime success. The allreduce fusion initialized without errors, allocated IPC handles, and began processing requests. But the runtime behavior was pathological. This is a reminder that \"it compiles\" is a necessary but not sufficient condition for correctness in GPU programming.

Consumer and datacenter GPUs are diverging. The SM100 vs. SM120 distinction is not just about core count or memory bandwidth. Datacenter Blackwell GPUs have hardware features — like enhanced cooperative grid synchronization — that consumer Blackwell GPUs lack. Inference stacks designed for datacenter hardware cannot simply be \"ported\" to consumer hardware by removing architecture gates; they may depend on hardware capabilities that don't exist on the consumer SKUs.

The value of systematic verification. The assistant's methodical approach — tracing the error from Python through JIT compilation to CUDA preprocessor directives, verifying each layer, and checking every related file — is a textbook example of debugging a complex system. The grep commands, the sed patches, the cache clearing, and the monitoring loops all contributed to a disciplined investigation that, even when it led to a dead end, produced valuable knowledge about SM120's capabilities and limitations.

Conclusion

The allreduce fusion experiment on SM120 ultimately failed to deliver performance gains, but it failed informatively. It confirmed that the PCIe bottleneck on these eight RTX PRO 6000 GPUs would need a different solution — perhaps expert parallelism, tensor-parallelism-plus-pipeline-parallelism, or deeper MoE kernel optimizations. The assistant's willingness to fork and modify upstream dependencies, encouraged by the user's directive to \"think big,\" represents the kind of bold engineering that pushes the boundaries of what's possible on non-datacenter hardware. In engineering, knowing what doesn't work — and understanding why at the level of CUDA preprocessor directives — is often as valuable as knowing what does.## References

[1] The Allreduce Fusion Trap: When Patching Code Without Understanding Kernel Constraints Backfires ([msg 755]) [2] The SM120 Allreduce Fusion Dead End: A Case Study in Architecture-Specific Kernel Limitations ([msg 756]) [3] The Verification That Confirmed a Dead End: When Allreduce Fusion Met SM120 ([msg 757]) [4] The Revert: When Hardware Reality Overrides Software Ambition ([msg 758]) [5] The Verification That Confirmed a Limit: Reverting Allreduce Fusion for SM120 ([msg 759]) [6] The Moment of Acceptance: When Allreduce Fusion Hit a Hardware Wall ([msg 760]) [7] The Reset: When Allreduce Fusion Fails on Blackwell SM120 ([msg 761]) [8] The Moment the Kernel Wall Broke: Investigating FlashInfer's SM120 Support Ceiling ([msg 762]) [9] Reading the Source: How One Bash Command Uncovered the Architecture Gate Blocking Blackwell GPU Performance ([msg 763]) [10] Reading the Source: The Pivotal Investigation into FlashInfer's SM120 Architecture Gate ([msg 764]) [11] The Architecture Gate: A Moment of Insight in the SM120 Allreduce Fusion Saga ([msg 765]) [12] The Sed That Changed Everything: Patching FlashInfer's SM120 Gate ([msg 766]) [13] The Delicate Dance of Patching: Enabling Allreduce Fusion for Blackwell SM120 GPUs ([msg 767]) [14] The Permission to Fork: How a Single User Message Unlocked Deep Kernel Hacking ([msg 768]) [15] The Pivot Point: When a Single Line of Code Unlocks an Architecture ([msg 769]) [16] The Due Diligence Check: Verifying a FlashInfer SM120 Patch Across the Codebase ([msg 770]) [17] The Moment of Verification: Patching FlashInfer Allreduce Fusion for SM120 ([msg 771]) [18] The Anatomy of a Discovery: Finding the FlashInfer CUDA Source Path ([msg 772]) [19] The Critical Glance: Inspecting CUDA Source Files to Unlock Allreduce Fusion on Blackwell SM120 ([msg 773]) [20] The Green Light: Validating CUDA Kernel Portability Across GPU Architectures ([msg 774]) [21] The CUDA Source That Had No Guards: A Pivotal Assumption in the SM120 Allreduce Fusion Debug ([msg 775]) [22] The Architecture Gate: Tracing the SM120 Allreduce Fusion Boundary ([msg 776]) [23] The Missing Header: Tracing SM120 Allreduce Fusion Through FlashInfer's CUDA Source ([msg 777]) [24] Probing the Depths: Tracing the Allreduce Fusion Dependency Chain for SM120 Support ([msg 778]) [25] The Architecture Gate: Discovering Why FlashInfer Allreduce Fusion Excludes SM120 ([msg 779]) [26] The Architecture Gate That Killed Allreduce Fusion: Debugging SM120 Exclusion in FlashInfer's CUDA Kernels ([msg 780]) [27] The Architecture Gate: Patching FlashInfer Allreduce Fusion for Blackwell SM120 ([msg 781]) [28] The Architecture Gate: Patching FlashInfer Allreduce Fusion for Consumer Blackwell GPUs ([msg 782]) [29] The Systematic Debugger: Verifying Architecture Guards Across the FlashInfer Allreduce Fusion Stack ([msg 783]) [30] The Architecture Gate: Verifying SM120 Compatibility in FlashInfer's Allreduce Fusion ([msg 784]) [31] The Verification That Unlocks SM120: Patching FlashInfer Allreduce Fusion for Blackwell Consumer GPUs ([msg 785]) [32] The Final Gate: Patching FlashInfer Allreduce Fusion for SM120 ([msg 786]) [33] The Methodical Debugger: Tracing SM-Specific Code Paths in SGLang's FlashInfer Communication Fusion Layer ([msg 787]) [34] The Final Gate: Verifying SM120 Compatibility in SGLang's Allreduce Fusion Layer ([msg 788]) [35] The Moment of Truth: Patching FlashInfer AllReduce Fusion for Blackwell SM120 ([msg 789]) [36] The Moment of Truth: Launching a Patched Allreduce Fusion on SM120 Blackwell ([msg 790]) [37] The Pivot Point: Waiting for SM120 Allreduce Fusion to Compile ([msg 791]) [38] The Moment of Uncertainty: When \"CRASHED\" Wasn't the Final Answer ([msg 792]) [39] The False Alarm: Diagnosing a Perceived Crash in the SM120 Allreduce Fusion Effort ([msg 793]) [40] The Moment of Truth: Tailing the Log After Patching FlashInfer for SM120 Allreduce Fusion ([msg 794]) [41] The False Crash: When JIT Compilation Succeeds But the Monitoring Lies ([msg 795]) [42] The Moment of Truth: Diagnosing a Patched Allreduce Fusion on Blackwell SM120 ([msg 796]) [43] The False Crash: Debugging Allreduce Fusion on Blackwell GPUs ([msg 797]) [44] The Moment of Truth: When Patched Allreduce Fusion Quietly Came Alive on SM120 ([msg 798]) [45] The Moment of Triumph That Wasn't: Patching FlashInfer Allreduce Fusion for SM120 ([msg 799]) [46] The Moment of Truth: Benchmarking a Patched Allreduce Fusion on Blackwell SM120 ([msg 800]) [47] The Allreduce Fusion That Wasn't: When Patching CUDA Architecture Gates Backfires on Blackwell SM120 ([msg 801]) [48] The Revert: When Patching CUDA Architecture Gates Makes Things Worse ([msg 802]) [49] The Pivot: When Allreduce Fusion Fails on SM120 ([msg 803]) [50] The Moment of Reflection: Diagnosing Failed Allreduce Fusion on Blackwell SM120 ([msg 804]) [51] The Baseline Checkpoint: Recovering from a Failed Allreduce Fusion Experiment on SM120 ([msg 805]) [52] The Diagnostic Pivot: Interpreting GPU Underutilization After a Failed Allreduce Fusion Experiment ([msg 806]) [53] The Baseline Confirmed: A Moment of Reckoning After a Failed Optimization ([msg 807]) [54] The Pivot: Strategic Retreat and Reassessment in High-Performance GPU Inference Tuning ([msg 808]) [55] The Pivot After Failure: Batching Decode Steps and NCCL Tuning on SM120 ([msg 809])