The Allreduce Fusion That Wasn't: When Patching CUDA Architecture Gates Backfires on Blackwell SM120

Introduction

In the high-stakes world of large-scale ML inference, every microsecond counts. When running a 400B-parameter Mixture-of-Experts model like GLM-5-NVFP4 across eight RTX PRO 6000 Blackwell GPUs, the difference between 800 tok/s and 3,700 tok/s can be the difference between a viable deployment and a non-starter. But optimization is rarely a straight line upward. Sometimes, the most instructive moments come not from breakthroughs but from spectacular failures.

Message <msg id=801> captures one such moment: the assistant, having just patched FlashInfer's allreduce fusion code to support the SM120 architecture (consumer Blackwell GPUs), runs a benchmark and discovers that throughput has collapsed from ~1,867 tok/s to a paltry 236 tok/s, with GPU power draw dropping from ~250W to ~125W. The response is immediate and decisive: kill the server, revert the changes, and retreat to known working territory.

This message is a masterclass in diagnostic reasoning under pressure, the dangers of architectural assumptions, and the discipline of knowing when to cut losses. It is also a vivid illustration of the gap between datacenter and consumer GPU architectures, and the hidden complexity lurking behind innocent-looking #if preprocessor directives.

The Message: A Post-Mortem in Real Time

Let us examine the message in full:

The allreduce fusion made things much worse — 236 tok/s (down from 1,867) and power dropped to ~125W. The fusion is probably deadlocking or using an incompatible synchronization path for SM120. The cudaGridDependencySynchronize() change I made might be causing issues — SM120 may not support cooperative grid dependencies the same way.

>

Let me revert the allreduce fusion and go back to what was working. The issue is that cudaGridDependencySynchronize in the allreduce is causing excessive serialization or stalls on SM120.

>

[bash] ssh root@10.1.230.174 "pkill -9 -f sglang ; sleep 2 ; pkill -9 -f python ; sleep 2"

The structure is textbook scientific method: hypothesis, experiment, observation, conclusion, next steps. The assistant states the quantitative result (236 tok/s vs 1,867 tok/s, 125W vs ~250W), proposes a causal mechanism (deadlocking or incompatible synchronization), identifies the specific code change most likely responsible (the cudaGridDependencySynchronize() patch), and executes a clean revert.

The Context: How We Got Here

To understand why this message matters, we need to trace the thread that led to it. The assistant had been engaged in a multi-session effort to deploy GLM-5-NVFP4 on 8x RTX PRO 6000 Blackwell GPUs. After resolving NaN crashes during decode (by selecting the correct NSA backends), establishing baseline throughput, and diagnosing PCIe P2P latency as a bottleneck in the Proxmox virtualized environment, the team had moved to an LXC container on the host for bare-metal GPU topology.

By segment 6 of the conversation, the assistant had achieved impressive results: by enabling FlashInfer CUTLASS MoE autotune for SM120 and raising --max-running-requests to 1024, total token throughput had climbed to ~3,740 tok/s at peak. However, GPU power draw remained stubbornly around 250W out of a 600W TDP — less than half of what the hardware could theoretically deliver.

The bottleneck was identified: FlashInfer's allreduce fusion, which fuses the allreduce communication with MoE computation to reduce PCIe traffic and synchronization overhead, was disabled on SM120. The underlying TRT-LLM communication kernels only supported SM90 (Hopper) and SM100 (datacenter Blackwell). The consumer Blackwell architecture (SM120) was explicitly excluded.

The Patching Expedition

What followed was an ambitious multi-step patching campaign spanning messages <msg id=766> through <msg id=799>. The assistant systematically modified:

1. flashinfer/jit/comm.py: Added SM120 (major version 12) to supported_major_versions=[9, 10, 12].
2. sglang/srt/layers/communicator.py: Extended the SM gate from (is_sm90_supported or is_sm100_supported) to include is_sm120_supported.
3. sglang/srt/server_args.py: Modified the server argument validation to allow allreduce fusion on SM120.
4. flashinfer/data/include/flashinfer/comm/trtllm_allreduce.cuh: This was the critical find. 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. The assistant also verified that the other allreduce headers (trtllm_allreduce_fusion.cuh and trtllm_moe_allreduce_fusion.cuh) did not contain similar SM120 exclusions. The JIT cache was cleared, and the server was restarted with --enable-flashinfer-allreduce-fusion. In <msg id=799>, the server came up successfully. The allreduce IPC handles were allocated across all 8 ranks. The workspace initialized. The server was running with allreduce fusion on SM120. It seemed like a victory.

The Crash Landing

Then came the benchmark in <msg id=800>. The results were devastating:

• Output token throughput: 78.60 tok/s (down from ~1,867)
• Total token throughput: 235.79 tok/s
• GPU power: ~125W (down from ~250W) The fusion was not just failing to help — it was actively harming performance by an order of magnitude. The GPUs were running at 100% utilization but drawing only 125W, suggesting they were stalled on synchronization or memory operations rather than doing useful compute.

The Reasoning in Message 801

The assistant's analysis in <msg id=801> is remarkably precise given the limited information available. Let us unpack each element:

Hypothesis: Deadlocking or Incompatible Synchronization

The assistant correctly identifies that the cudaGridDependencySynchronize() call is the most likely culprit. This CUDA cooperative grid synchronization primitive was introduced for Hopper (SM90) and refined for datacenter Blackwell (SM100). It allows threads across different thread blocks in the same grid to synchronize without launching a new kernel. On SM120 (consumer Blackwell), this feature may:

1. Not be supported at all: The hardware might lack the necessary shared memory or warp-level primitives.
2. Behave differently: The synchronization semantics might differ, causing excessive stalls.
3. Serialize execution: Instead of enabling overlap, the sync might force sequential execution of the allreduce and MoE computation, negating the entire purpose of fusion.

The Quantitative Signal

The assistant cites two key metrics: throughput (236 tok/s vs 1,867 tok/s) and power draw (~125W vs ~250W). The power drop is particularly telling. At 125W per GPU (out of 600W TDP), the GPUs are essentially idling while waiting. This is consistent with a synchronization deadlock or severe serialization where most threads are blocked.

The Decision: Revert

The most important aspect of this message is the decision to revert. This is not a failure — it is a disciplined retreat. The assistant could have continued debugging, trying different patches, or investigating the synchronization behavior. Instead, they correctly recognize that:

1. The fusion was supposed to improve performance but made it 8x worse.
2. The root cause (SM120 synchronization incompatibility) is a deep architectural issue, not a quick fix.
3. The working configuration (without fusion) was achieving acceptable throughput.
4. Further investigation should be done offline, not while the server is down. The pkill -9 command is brutal but necessary. When dealing with GPU processes that may be in a deadlocked state, clean process termination is essential before reconfiguration.

Assumptions Made

Several assumptions underpin the assistant's reasoning, some explicit and some implicit:

Explicit Assumptions

1. The cudaGridDependencySynchronize() change is the cause: The assistant explicitly states this. This is a reasonable inference given that this was the only semantic change to the CUDA kernel — the other patches were just enabling gates and version checks.
2. SM120 does not support cooperative grid dependencies the same way: This is a plausible architectural assumption. Consumer GPUs often lack features present in their datacenter counterparts, even when sharing the same architecture generation.

Implicit Assumptions

1. The CUDA kernel code itself is correct for SM120: The assistant assumed that removing the < 1200 guard would allow the kernel to compile and run correctly on SM120. In fact, the kernel might rely on SM90/SM100-specific features (like specific shared memory sizes, warp sizes, or instruction set extensions) that are subtly different on SM120.
2. The JIT compilation produced correct code: The fact that the server started without crashes suggested the kernel compiled successfully, but silent correctness issues (wrong results, deadlocks) are harder to detect.
3. The benchmark is representative: The assistant used a standard benchmark configuration (256 prompts, 256 input tokens, 128 output tokens). This assumes the failure mode is consistent across workloads.

Mistakes and Incorrect Assumptions

The Upper Bound Exclusion Was Intentional

The most significant mistake was treating the __CUDA_ARCH__ < 1200 guard as an oversight rather than an intentional exclusion. The original code was:

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

The < 1200 guard was almost certainly placed there because the cudaGridDependencySynchronize() function (or the cooperative grid dependency mechanism it relies on) was not validated on SM120 hardware. The FlashInfer and TRT-LLM developers likely knew that SM120 support was untested and chose to exclude it rather than risk silent failures.

By removing this guard, the assistant enabled a code path that had never been exercised on SM120. The result was predictable in hindsight: the synchronization primitive either doesn't work correctly on SM120 or has different performance characteristics that make it detrimental.

Underestimating Architecture Differences

The assistant's earlier investigation (messages <msg id=774> through <msg id=778>) found that the CUDA source files contained no SM-specific arch guards and that the headers used generic CUDA primitives. This led to the conclusion that "they should compile fine for SM120." However, the absence of explicit architecture checks does not guarantee correct behavior — CUDA primitives can have architecture-specific implementations or performance characteristics that are invisible at the source level.

The Power Signal Was Misread Initially

In <msg id=800>, the assistant observed that GPU utilization was 100% despite power draw of only ~125W. This was interpreted as "GPUs are running at 100% utilization" — but 100% utilization with low power draw is a classic sign of a synchronization-bound workload where the GPU is active but stalled on memory or synchronization operations, not doing useful compute.

Input Knowledge Required

To fully understand this message, the reader needs:

1. CUDA architecture numbering: SM90 = Hopper (H100), SM100 = datacenter Blackwell (B200), SM120 = consumer Blackwell (RTX PRO 6000). The fact that consumer and datacenter variants have different architecture numbers despite sharing the "Blackwell" branding is crucial.
2. Cooperative grid synchronization: cudaGridDependencySynchronize() is a CUDA primitive that allows threads across different blocks in the same grid to synchronize. It was introduced in Hopper and relies on hardware-specific shared memory and warp-level primitives.
3. Allreduce fusion in MoE models: In Mixture-of-Experts models, each token is routed to a subset of experts. The allreduce operation combines partial results from different GPUs. Fusing the allreduce with the MoE computation can reduce PCIe traffic and latency, but requires careful synchronization.
4. The GLM-5-NVFP4 model: A ~400B parameter MoE model using NVFP4 quantization (NVIDIA's 4-bit floating point format). It requires significant engineering to deploy efficiently.
5. The infrastructure context: The GPUs are in a Proxmox VM with PCIe passthrough, meaning P2P communication goes over PCIe rather than NVLink, making allreduce fusion particularly important for performance.

Output Knowledge Created

This message creates several important pieces of knowledge:

1. SM120 allreduce fusion is non-functional: The TRT-LLM allreduce fusion kernels do not work correctly on SM120 (consumer Blackwell). This is a concrete finding that future optimization efforts must account for.
2. The specific failure mode: Performance drops by ~8x and power drops by ~50%, suggesting synchronization deadlock or severe serialization rather than a correctness crash.
3. A validated debugging methodology: The assistant demonstrated a clear pattern: hypothesize, patch, test, measure, diagnose, revert. This methodology is reproducible and valuable for future debugging sessions.
4. A boundary condition for the deployment: The maximum achievable throughput without allreduce fusion is ~1,867 tok/s (at the time of this message), and further gains will require either fixing the fusion or finding alternative optimization paths.

The Thinking Process

The assistant's reasoning in this message reveals several cognitive patterns:

Quantitative First

The assistant leads with numbers: "236 tok/s (down from 1,867)" and "power dropped to ~125W." This grounding in quantitative evidence is essential for diagnosing performance issues. The magnitude of the regression (8x) immediately signals that this is not a minor tuning issue but a fundamental incompatibility.

Causal Chain Reasoning

The assistant traces a causal chain: allreduce fusion → cudaGridDependencySynchronize() → SM120 incompatibility → deadlocking/stalls → low throughput and power. Each link is explicit and falsifiable.

Principled Retreat

The decision to revert is not presented as a failure but as a tactical withdrawal. The assistant does not say "I give up" but "Let me revert... and go back to what was working." This preserves the working configuration while creating space for deeper investigation.

Clean State Management

The pkill -9 -f sglang ; sleep 2 ; pkill -9 -f python ; sleep 2 sequence shows an understanding of process management on GPU systems. GPU processes can linger in deadlocked states, and a clean kill with a sleep ensures all resources are released before the next attempt.

Broader Implications

This message has implications beyond this specific deployment:

The Consumer-Datacenter Gap

The RTX PRO 6000 is a $7,000+ GPU, yet it lacks features present in the datacenter B200. This gap is not just about memory or compute — it extends to synchronization primitives, communication libraries, and kernel support. Anyone deploying inference on consumer Blackwell GPUs needs to account for these missing features.

The Danger of Removing Guards

The __CUDA_ARCH__ < 1200 guard was there for a reason. While it's tempting to view such guards as overly conservative, they often represent real hardware limitations. The assistant's experience is a cautionary tale about the risks of "just removing the check."

The Value of Clean Reverts

In complex systems engineering, the ability to cleanly revert a failed experiment is as important as the ability to make changes. The assistant's immediate pivot from "it's running!" to "kill it and revert" demonstrates operational maturity.

Conclusion

Message <msg id=801> is a small but dense moment in a long optimization journey. It captures the moment when a promising optimization path collapses under empirical testing, the diagnostic reasoning that identifies the likely cause, and the disciplined decision to revert and regroup.

The allreduce fusion on SM120 remains an open problem — one that may require deeper changes to the TRT-LLM communication kernels, or acceptance that consumer Blackwell GPUs cannot fully utilize this optimization. But the knowledge gained from this failure is valuable: the boundary of what works on SM120 has been precisely defined, and future efforts can focus on either fixing the synchronization primitive or finding alternative approaches.

In the broader narrative of the session, this message marks a turning point. The assistant had been on a winning streak of optimizations, pushing throughput from ~880 tok/s to ~3,740 tok/s. The allreduce fusion failure is a reminder that not all optimizations are additive, and that the interaction between software and hardware is full of surprises. The best engineers are not those who never fail, but those who fail fast, diagnose accurately, and revert cleanly.