The Moment of Truth: Patching FlashInfer AllReduce Fusion for Blackwell SM120

"OK, now let me restart with all the patches and see if it compiles and runs"

In the middle of an intense optimization session for the GLM-5-NVFP4 large language model running on 8× NVIDIA RTX PRO 6000 Blackwell GPUs, the assistant issues a deceptively simple message ([msg 789]). It kills all running Python processes on the remote server and prepares to restart the inference stack. On its surface, this is a routine administrative action — clean up old processes before launching anew. But in context, this message represents a critical inflection point: the culmination of a deep investigative spiral into GPU architecture compatibility, the boundaries of open-source forking, and the razor-thin line between enabling a feature and breaking a system.

The Long Road to This Moment

To understand why this message matters, we must trace the path that led here. The session had already achieved remarkable results: by enabling FlashInfer CUTLASS MoE autotune for SM120 and increasing --max-running-requests to 1024, the assistant had pushed GLM-5-NVFP4 throughput from roughly 880 tok/s to nearly 3,740 tok/s at 1024 concurrent requests ([chunk 6.0]). Yet GPU power draw remained stubbornly around 250W out of a 600W TDP — the hardware was barely half-utilized. The bottleneck was clear: PCIe-based allreduce communication between the 8 GPUs was starving the compute units.

The solution seemed obvious: enable FlashInfer's allreduce fusion, a technique that overlaps allreduce communication with GEMM computation, hiding latency behind useful work. But there was a catch. The fusion kernels relied on NVIDIA's TRT-LLM communication primitives, which were compiled only for SM90 (Hopper datacenter, H100) and SM100 (Blackwell datacenter, B100/B200). The RTX PRO 6000 Blackwell GPUs are SM120 — the consumer/professional variant of Blackwell, with different architecture characteristics including smaller shared memory and missing support for certain cooperative grid synchronization features.

A Cascade of Patches

The assistant's first attempt to enable allreduce fusion ([msg 751]) ended in immediate failure. The server crashed with RuntimeError: No supported CUDA architectures found for major versions [9, 10] ([msg 754]). The flashinfer JIT compilation context explicitly rejected SM120. The assistant initially reverted the changes, concluding that the kernel literally didn't exist for SM120 ([msg 756]).

But then the user intervened with a directive that changed everything: "Please think big and don't be afraid to fork/modify code" ([msg 768]). This was permission — no, an instruction — to go beyond configuration tweaks and into deep code modification.

What followed was a systematic campaign of source-level patching across two repositories:

1. flashinfer/jit/comm.py (line 61): Changed supported_major_versions=[9, 10] to [9, 10, 12] — the version gate that was blocking SM120 from even attempting compilation ([msg 766]).
2. sglang/python/sglang/srt/layers/communicator.py (line 97): Re-added _is_sm120_supported to the allreduce fusion gate, after having previously removed it ([msg 767]).
3. sglang/python/sglang/srt/server_args.py (line 1686): Similarly re-enabled SM120 auto-detection for allreduce fusion ([msg 769]).
4. flashinfer/data/include/flashinfer/comm/trtllm_allreduce.cuh: The most critical patch. The CUDA header contained #if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900) && (__CUDA_ARCH__ < 1200)) which explicitly excluded SM120 (where __CUDA_ARCH__ equals 1200). The assistant changed this to __CUDA_ARCH__ >= 900, removing the upper bound ([msg 781]). The last patch was the most consequential. The #if block guarded calls to cudaGridDependencySynchronize(), a Hopper/Blackwell cooperative grid synchronization primitive. On SM120, this function may not be available or may behave differently. By removing the exclusion, the assistant was betting that either the function would work on SM120, or that the #else fallback path (which uses volatile memory operations for synchronization) would be sufficient.

The Assumptions Behind the Patch

The assistant's reasoning, visible across messages [msg 774] through [msg 785], reveals several key assumptions:

Assumption 1: The CUDA source code is architecture-agnostic. The assistant checked that the .cu files contained zero SM-specific arch guards ([msg 774]), and that the core algorithmic logic in the headers had no SM120 exclusions beyond the cudaGridDependencySynchronize gate. This suggested the kernels could compile for SM120 if the version gate were removed.

Assumption 2: SM120 supports cudaGridDependencySynchronize. The cooperative grid synchronization primitive was introduced in Hopper (SM90) and carried forward to Blackwell datacenter (SM100). Whether it works on SM120 is unknown — the assistant's patch assumes it does, or that the fallback path is acceptable. The comment "SM120 at __CUDA_ARCH__ == 1200 should be fine — the cooperative grid sync just won't be used, which means slightly less efficient synchronization but the kernel should still work" (<msg id=781 reasoning>) reveals this hopeful-but-uncertain stance.

Assumption 3: The JIT compilation will succeed with SM120 added. The flashinfer compilation context detects SM120 and adds it to TARGET_CUDA_ARCHS automatically ([msg 765]). The assistant assumed that once the version gate in comm.py was patched, the JIT compiler would successfully compile the TRT-LLM communication module for SM120.

Assumption 4: No other SM120 exclusions exist deeper in the dependency chain. The assistant checked the fusion headers (trtllm_allreduce_fusion.cuh, trtllm_moe_allreduce_fusion.cuh) and found no < 1200 exclusions (<msg id=784-785>). But there could be issues in other included headers, in the cutlass extensions, or in the NVSHMEM bindings.

What This Message Actually Does

The subject message itself is simple: it kills all sglang and Python processes on the remote machine, waits, and confirms cleanup. But its purpose is profound: it resets the stage for the first-ever attempt to run FlashInfer allreduce fusion on SM120 hardware.

The message is structured as a single bash command piped through SSH:

pkill -9 -f sglang ; sleep 2 ; pkill -9 -f python ; sleep 2 ; pgrep -f python || echo 'clean'

The double kill (first sglang-specific, then all Python processes) is defensive — ensuring no lingering processes from the previous crashed server attempt ([msg 754]) remain. The sleep 2 between kills gives the OS time to release GPU memory. The final pgrep with || echo 'clean' confirms the environment is ready.

Input Knowledge Required

To understand this message, a reader needs:

• The architecture of SGLang: How the server launches, what --enable-flashinfer-allreduce-fusion does, and why it's distinct from the MoE runner backend.
• FlashInfer's JIT compilation model: That flashinfer compiles CUDA kernels at runtime via NVCC, and that the compilation_context gates which architectures are supported.
• NVIDIA GPU architecture numbering: SM90 = Hopper H100, SM100 = Blackwell B100/B200 (datacenter), SM120 = Blackwell RTX PRO 6000 (consumer/professional). The distinction between "datacenter Blackwell" and "consumer Blackwell" is crucial — they share the same architecture generation but have different feature sets.
• The PCIe allreduce bottleneck: Why 8 GPUs communicating over PCIe (without NVLink/NVSwitch) creates a communication bottleneck that allreduce fusion aims to hide.
• Cooperative grid synchronization: The cudaGridDependencySynchronize primitive and why it matters for overlapping communication with computation.

Output Knowledge Created

This message, combined with the server restart that follows ([msg 790]), creates knowledge about:

1. Whether the patches compile: The JIT compilation of the TRT-LLM communication module for SM120 is the first test. If it fails, the approach is fundamentally blocked.
2. Whether the kernels run correctly: Even if compilation succeeds, the fused allreduce might produce incorrect results, hang, or crash at runtime due to synchronization issues on SM120.
3. Whether performance improves: The entire exercise is motivated by the GPU utilization gap (250W vs 600W TDP). If allreduce fusion works but doesn't improve power draw or throughput, the bottleneck lies elsewhere.

The Thinking Process Visible in the Reasoning

The assistant's reasoning across the preceding messages reveals a methodical investigative process. When the first crash occurred ([msg 754]), the assistant didn't give up — it traced the error back through the stack: from the RuntimeError in the server log, to gen_trtllm_comm_module() in comm.py, to the get_nvcc_flags_list call with supported_major_versions=[9, 10] ([msg 755]). Each step was a grep or sed command peeling back another layer.

The investigation of the CUDA source files (<msg id=774-785>) is particularly instructive. The assistant checked:

• Whether .cu files had SM-specific arch guards (they didn't)
• Whether the headers had __CUDA_ARCH__ guards (they did, and one had the critical < 1200 exclusion)
• Whether the fusion headers had similar exclusions (they didn't)
• Whether the sglang Python layer had SM gates (it didn't) This is a textbook example of tracing a compatibility issue through a complex software stack: Python → JIT compilation → CUDA C++ headers → PTX/assembly. Each layer can introduce its own architecture gate, and all must be aligned for the feature to work.

The Broader Significance

This message captures a moment that every engineer working at the frontier of AI infrastructure recognizes: the pause before hitting "enter" on a risky change. The assistant has forked and patched two open-source projects (flashinfer and sglang) in ways their authors never tested. The patches touch CUDA synchronization primitives, JIT compilation pipelines, and architecture detection logic. If they work, the team gains a significant performance boost on unique hardware. If they fail, the server crashes and hours of debugging lie ahead.

The user's instruction to "think big and not be afraid to fork/modify code" ([msg 768]) was the catalyst. Without it, the assistant would have accepted the SM120 limitation and moved on. With it, the assistant pushed past the boundary of "supported configurations" into the territory of "what could work if we make it work." This is the essence of infrastructure engineering at the edge — when the hardware you have doesn't match the software you need, you write the software.

What follows this message ([msg 790]) is the server launch with all patches active. Whether it succeeds or fails, the knowledge gained — about SM120's compatibility with TRT-LLM communication primitives, about the robustness of flashinfer's JIT compilation, about the feasibility of allreduce fusion on non-datacenter Blackwell — is valuable. This message is the pivot point between investigation and validation, between theory and practice, between "what if" and "what happens."