The Silent Deadlock: Diagnosing SGLang's SM120 Crash on Blackwell GPUs

In the high-stakes world of large language model deployment, few things are more disorienting than a silent failure. After investing days building an EAGLE-3 speculative decoding pipeline for the 1-trillion-parameter Kimi-K2.5 model, the assistant in this coding session encountered exactly that: a server that loaded perfectly, consumed 76 GB per GPU, and then simply... stopped. No error messages. No crash. No listening port. Just eight processes sleeping at 0% CPU and 0% GPU utilization, as if the model had been sedated rather than deployed. Message 3154 captures the precise moment of diagnosis and the strategic pivot that followed.

The Road to SGLang

To understand why this message matters, we need to trace the path that led here. The assistant had spent the previous segment building and testing an EAGLE-3 speculative drafter for Kimi-K2.5, a massive Mixture-of-Experts model running on eight NVIDIA RTX PRO 6000 Blackwell (SM120) GPUs. The training pipeline worked flawlessly: synthetic data generation ran for 5.3 hours, hidden state extraction achieved 3,165 tokens/second producing 828 GB of training data, and a 5-epoch finetune completed in 2.6 hours. But when the trained drafter was integrated with vLLM, the results were devastating. Both the custom-trained drafter and the pre-trained AQ-MedAI baseline achieved only a ~15% acceptance rate, yielding 0.66× throughput — worse than running without speculation at all. The root cause was a fundamental incompatibility between vLLM's EAGLE-3 implementation and Multi-head Latent Attention (MLA), the attention mechanism used by DeepSeek-derived architectures like Kimi-K2.5.

The user directed the assistant to pivot to SGLang, which offers first-class EAGLE-3 support and is explicitly tested with Kimi-K2 drafters. The assistant spent hours building sgl-kernel for SM120 (a 48-minute compilation), verifying it loaded correctly, and then launching the server. The initial results were breathtaking: SGLang loaded the 547 GB model in just 22 seconds, compared to vLLM's 25 minutes. But then the server never became available.

The Diagnostic Sequence

The assistant's first attempt with EAGLE-3 on SGLang ([msg 3131]) showed the model loading successfully but the server never started listening. After killing that process and trying a baseline test without EAGLE-3 ([msg 3147]), the same pattern emerged. Message 3150 captured the assistant's initial optimism — "Loaded in 22 seconds! Now waiting for warmup" — followed by a 300-second sleep. When the assistant checked the health endpoint, it returned nothing. The log showed only the server args dump, suggesting the process entered CUDA graph compilation and never emerged.

By message 3153, the assistant had gathered critical evidence. The nvidia-smi output showed all eight GPUs at 0% utilization with exactly 76,017 MiB of memory used — the model was loaded and sitting idle. The ps aux output revealed the eight scheduler processes (sglang::scheduler_TP0 through TP7) each consuming 82-83% of system memory but 0.0% CPU. The main Python process was also at 0.0% CPU. The server was not listening on port 8000. Crucially, the system had 416 GB of available RAM in buff/cache — this was not an out-of-memory situation.

The Reasoning in Message 3154

Message 3154 is the synthesis of all this diagnostic work. The assistant opens with a concise summary of the evidence: "All 0% CPU and 0% GPU. The processes loaded the model but then went completely idle." This is followed by the diagnostic conclusion: "Something is deadlocking." The assistant then proposes two hypotheses for the root cause: "Could be a NCCL issue with SM120 or a problem with the attention backend."

This is a remarkably precise differential diagnosis. The assistant has ruled out several possibilities through the preceding messages:

• OOM: Ruled out by free -h showing 416 GB available ([msg 3145])
• EAGLE-3 specific: Ruled out because the base server (without speculative decoding) also deadlocks ([msg 3147])
• Crash with error: Ruled out by checking the log for error/exception/warning messages ([msg 3140])
• Weight loading failure: Ruled out because 76 GB per GPU matches the expected model footprint The two remaining hypotheses target different layers of the SGLang stack. NCCL (NVIDIA Collective Communications Library) handles the inter-GPU communication required for tensor parallelism across eight GPUs. If NCCL initialization hangs — perhaps due to an SM120-specific issue with the NCCL topology detection or a CUDA version incompatibility — the scheduler processes would appear alive but never complete their initialization. The attention backend hypothesis targets SGLang's Triton-based attention implementation, which might have SM120-specific code paths that deadlock during CUDA graph compilation.

The Strategic Decision

The assistant's response to this diagnosis is a model of disciplined debugging. Rather than continuing to probe the deadlocked processes (which would yield no new information), the assistant decides to kill everything and restart with modified parameters. The specific changes are:

1. --disable-cuda-graph: This flag disables SGLang's CUDA graph compilation, which is the most likely place where the deadlock occurs. CUDA graphs pre-compile entire execution sequences for maximum performance, but they can trigger subtle bugs on new GPU architectures. If the deadlock is in graph compilation, disabling it would allow the server to start (albeit with lower performance).
2. More verbose logging: The assistant plans to increase log verbosity to capture where exactly the initialization hangs. The current log ends abruptly after weight loading, with no indication of what phase follows. More verbose logging would reveal whether the hang is in NCCL initialization, attention backend setup, CUDA graph compilation, or some other phase. The kill command itself is brutal but necessary: it sends SIGKILL to all Python processes, then uses fuser to find and kill any process holding NVIDIA device files, then cleans up /dev/shm/ (shared memory files that can interfere with NCCL). This aggressive cleanup ensures a clean state for the next attempt.

Assumptions and Their Validity

The assistant makes several assumptions in this message, most of which are reasonable given the available evidence. The assumption that the deadlock is related to SM120 compatibility is well-supported: SGLang's sgl-kernel had to be specially compiled for SM120, and the load_utils.py initially mapped SM120 to the sm100 binary directory (<msg id=3122-3123>). The Blackwell architecture is new enough that software support is still maturing.

The assumption that disabling CUDA graphs might help is also reasonable, though not guaranteed. CUDA graph compilation is a complex process that involves capturing GPU kernel launches and replaying them. On a new architecture, the graph capture itself might trigger driver-level issues, or the compiled graphs might contain instructions that deadlock on SM120 hardware.

One assumption that proved incorrect earlier was that the EAGLE-3 drafter was causing the issue ([msg 3145]). The assistant initially killed the EAGLE-3 server and tried a baseline test, correctly deducing that if the base server also deadlocked, the issue was not with speculative decoding. This was a sound scientific approach: isolate variables and test the simplest case first.

The Broader Context

This message sits at a critical juncture in the larger narrative. The assistant has been working for days to deploy Kimi-K2.5 with speculative decoding, and every path has hit a wall. vLLM's EAGLE-3 integration is fundamentally broken for MLA-based models. SGLang loads the model at breathtaking speed but then deadlocks on the Blackwell architecture. The assistant is running out of options.

What makes this message particularly interesting is the emotional and cognitive state it reveals. There is no frustration, no panic, no rushed decision-making. The assistant methodically gathers evidence, forms hypotheses, and plans the next experiment. The tone is clinical: "Let me kill this and try with --disable-cuda-graph and more verbose logging." This is the hallmark of experienced debugging — the recognition that when a system goes completely silent, the only productive response is to change the parameters and try again with better instrumentation.

Input and Output Knowledge

To fully understand this message, the reader needs knowledge of several domains: the SGLang inference server architecture (especially its process model with TP workers), the NVIDIA GPU architecture naming scheme (SM120 = Blackwell), the role of NCCL in multi-GPU communication, the concept of CUDA graph compilation for inference optimization, and the weight distribution pattern of MoE models like Kimi-K2.5 (76 GB per GPU across 8 GPUs for a 547 GB model).

The message creates new knowledge about SGLang's behavior on Blackwell GPUs: specifically, that the server deadlocks after successful weight loading, that this is not related to EAGLE-3 speculative decoding, and that the deadlock occurs during the initialization phase between weight loading and serving. This knowledge will inform the next debugging steps and potentially contribute to SGLang's SM120 support roadmap.

Conclusion

Message 3154 is a masterclass in diagnostic reasoning under uncertainty. Faced with a silent deadlock — one of the most frustrating failure modes in distributed systems — the assistant systematically rules out plausible causes, identifies the most likely suspects, and designs an experiment to gather more information. The decision to disable CUDA graphs and increase verbosity is not a random guess but a targeted intervention based on understanding where in the initialization sequence the hang is likely occurring. Whether this debugging strategy succeeds or not, the reasoning process itself is worth studying as a model of disciplined troubleshooting in complex ML infrastructure.