The Silent Deadlock: Diagnosing SGLang on Blackwell GPUs

[assistant] Still at the same point. Let me look at this differently — check if the scheduler processes are actually doing anything useful (like graph compilation):
[bash] ssh root@10.1.230.174 'nvidia-smi --query-gpu=index,memory.used,utilization.gpu --format=csv,noheader; echo "---"; ps aux | grep "sglang" | grep -v grep | awk "{print \$2, \$3, \$4, \$11}" | head -10'
0, 76017 MiB, 0 %
1, 76017 MiB, 0 %
2, 76017 MiB, 0 %
3, 76019 MiB, 0 %
4, 76017 MiB, 0 %
5, 76017 MiB, 0 %
6, 76017 MiB, 0 %
7, 76017 MiB, 0 %
---
48330 0.0 0.2 /root/ml-env/bin/python3
48466 0.0 82.8 sglang::scheduler_TP0
48467 0.0 83.0 sglang::scheduler_TP1
48468 0.0 82.5 sglang::scheduler_TP2
48469 0.0 83.0 sglang::scheduler_TP3
48470 0.0 82.8 sglang::scheduler_TP4
48471 0.0 82.9 sglang::scheduler_TP5
48472 0.0 82.8 sglang::scheduler_TP6
48473 0.0 83.0 sglang::scheduler_TP7
48474 0.0 0.2 sgl...

Introduction

Message <msg id=3153> is a quiet turning point in a long debugging saga. It arrives after the assistant has successfully built a custom sgl-kernel for SM120 (Blackwell architecture), loaded a 547-billion-parameter Kimi-K2.5 INT4 model in an astonishing 22 seconds (compared to vLLM's 25 minutes), and then waited — for minutes, across multiple rounds — for the server to become ready. It never does. In this message, the assistant pivots from passive waiting to active diagnosis, and the numbers it uncovers tell a stark story: the processes are alive, memory is allocated, but nothing is happening. Zero GPU utilization. Zero CPU utilization. A deadlock, silent and total.

This message is the diagnostic pivot — the moment when the assistant stops hoping the server will eventually start and instead asks the right question: are the scheduler processes actually doing anything?## The Context: A Long Road to Blackwell

To understand why this message matters, one must appreciate the journey that led to it. The assistant had been working for hours — across multiple segments and dozens of messages — to deploy Kimi-K2.5, a 1-trillion-parameter Mixture-of-Experts model, on a machine with 8 NVIDIA RTX PRO 6000 Blackwell GPUs. The environment had been painstakingly built: CUDA 13.1, PyTorch 2.9.1, flash-attn 2.8.3, vLLM 0.16, and a custom EAGLE-3 speculative decoding pipeline.

The EAGLE-3 experiment had been a major effort. The assistant trained a drafter model on 10,000 synthetic samples extracted from Kimi-K2.5's own reasoning outputs, patched API incompatibilities in the speculators library, and finally got it running in vLLM — only to discover a ~15% acceptance rate that made speculation slower than no speculation (0.66x throughput). This was a fundamental integration issue with vLLM's MLA (Multi-head Latent Attention) hidden state extraction, not a training quality problem.

The user then directed the assistant to pivot to SGLang, which has first-class EAGLE-3 support and is explicitly tested with Kimi-K2 drafters. The assistant built sgl-kernel for SM120 (a 48-minute compilation), verified it loaded correctly, and launched SGLang with the AQ-MedAI EAGLE-3 drafter. The model loaded in 22 seconds — a dramatic improvement over vLLM's 25 minutes. But then... nothing. The server never started listening. The log ended abruptly after the weight loading progress bar. The assistant waited, checked, waited again, and checked again across messages <msg id=3137> through <msg id=3152>. Each time: no listening port, no errors, no progress.

The Message Itself: A Diagnostic Pivot

Message <msg id=3153> opens with the phrase "Still at the same point" — a weary acknowledgment that the previous round of waiting (message <msg id=3152>) yielded nothing new. The log file still shows only the weight loading completion, 240 lines total, with no subsequent initialization messages. The assistant has been checking every 60-300 seconds for several rounds, and the situation has not changed.

But rather than continuing to wait passively, the assistant changes strategy. The key phrase is "Let me look at this differently." This signals a cognitive pivot: instead of asking when the server will be ready, the assistant now asks whether the processes are doing anything at all. This is a crucial distinction. The previous checks focused on external indicators: is the port open? Does the health endpoint respond? Does the log show new output? Now the assistant goes directly to the process level to inspect activity.

The command issued is a dual probe: nvidia-smi to check GPU utilization and memory, and ps aux to inspect the scheduler process tree. The output is devastatingly clear:

Assumptions and Their Validity

This message rests on several assumptions, most of which are well-supported by the evidence:

Assumption 1: Zero GPU utilization means no useful work is being done. This is generally valid. CUDA graph compilation, while it may not show sustained high utilization, typically produces some GPU activity as kernels are launched and profiled. Zero percent across all 8 GPUs for an extended period (the assistant had waited 5+ minutes since the last check) is unambiguous.

Assumption 2: The scheduler processes are the right thing to monitor. This is correct. In SGLang's architecture, the scheduler processes handle all inference work. If they're idle, the server cannot serve requests. The main Python process (PID 48330) is just the launcher/coordinator.

Assumption 3: The 83% RSS is shared memory, not per-process physical memory. This is a nuanced assumption. Earlier ([msg 3144]), the assistant verified that total system RAM usage was only 33 GiB, despite each scheduler process showing 83% RSS. This confirms the RSS values are misleading — they reflect mapped virtual address space, not exclusive physical memory. The assistant correctly interprets this rather than concluding the system is OOM.

One potential incorrect assumption is that the deadlock is within SGLang itself rather than in the underlying CUDA runtime or NCCL library. The assistant had just built sgl-kernel for SM120, and the undefined symbol issue encountered earlier ([msg 3123]) raised the possibility of binary incompatibility. The SM100 binary failed to load on SM120 with an undefined symbol error, and while the rebuilt binary loaded successfully, there could be runtime issues with CUDA driver APIs or PTX code paths that differ between SM100 and SM120. The assistant doesn't explicitly consider this in this message, though the next message ([msg 3154]) begins to explore it by trying --disable-cuda-graph.## Input Knowledge Required

To fully understand message <msg id=3153>, a reader needs considerable context accumulated over the preceding 30+ messages. This includes:

The SGLang architecture: Knowledge that SGLang launches separate scheduler processes for each tensor-parallel rank, that these processes handle model inference, and that the main Python process is just a coordinator. Without this, one might misinterpret the process list as showing 9 redundant Python processes doing nothing.

The SM120 compatibility saga: Earlier in the segment, the assistant discovered that SGLang's load_utils.py maps compute capability 120 to the sm100 directory, and that the SM100 binary had an undefined symbol on SM120. The assistant rebuilt sgl-kernel with SM120 support in CMake, but the load_utils.py still routes to the sm100 directory. The rebuilt binary happens to contain SM120 code paths, but the routing is fragile. This context is essential for understanding why the deadlock might be architecture-specific.

The memory usage pattern: Earlier checks ([msg 3144]) showed 33 GiB system RAM used despite 83% RSS per process. Without this context, seeing 83% RSS across 8 processes would suggest 664% memory usage, which is impossible. The assistant's earlier investigation into this discrepancy is what allows the correct interpretation here.

The weight loading speed: The model loaded in 22 seconds (vs 25 minutes in vLLM). This speed is itself suspicious — it suggests SGLang may be deferring some initialization (like CUDA graph compilation) to after weight loading, which would explain the post-loading stall. The assistant implicitly understands this and is checking whether that deferred work is actually happening.

The earlier EAGLE-3 failure: The assistant had just pivoted from vLLM EAGLE-3 integration, which failed due to MLA attention incompatibility. The user directed the pivot to SGLang specifically because of its first-class EAGLE-3 support. The assistant's first attempt with EAGLE-3 on SGLang also deadlocked ([msg 3131]-[msg 3145]), which is why this message tests the base model without speculation. The fact that even the base model deadlocks is significant — it means the issue is not EAGLE-3 specific but fundamental to SGLang on SM120.

Output Knowledge Created

This message produces several concrete pieces of knowledge:

  1. Confirmed deadlock: The zero GPU and CPU utilization across all 8 scheduler processes definitively rules out "slow initialization" as an explanation. The server is not going to become ready if left alone.
  2. Process-level diagnosis: The specific identification of sglang::scheduler_TP* processes as the stuck workers narrows the search space. The issue is in the inference runtime, not in the HTTP server or tokenizer.
  3. Memory allocation succeeded: The 76 GB per GPU and 83% RSS per process confirm that model weight loading completed successfully. The deadlock occurs after weight loading, during what should be warmup, CUDA graph compilation, or server initialization.
  4. Not an OOM situation: The low system RAM usage (33 GiB) and consistent GPU memory across all 8 GPUs rule out resource exhaustion as the cause.
  5. Symmetry across ranks: All 8 TP workers show identical behavior (0% CPU, 0% GPU, ~83% RSS). This symmetry strongly suggests a collective operation — likely NCCL allreduce or a barrier — that is waiting for a response that never arrives. In a deadlock caused by a single rank crashing, you'd expect some processes to show different states. This knowledge directly informs the next action. In the following message ([msg 3154]), the assistant kills the stuck processes and relaunches with --disable-cuda-graph and more verbose logging, attempting to isolate whether CUDA graph compilation is the deadlock point.## Mistakes and Incorrect Assumptions While the diagnostic reasoning in this message is sound, there are a few subtle points worth examining critically. The assumption that zero GPU utilization definitively means deadlock is strong but not ironclad. There is a possibility that SGLang's warmup phase involves CPU-only work (e.g., building CUDA graphs without launching them, or performing host-side validation) that would not register on nvidia-smi. However, the CPU utilization is also zero, which rules out this possibility. If the processes were doing CPU-intensive graph preparation, ps would show non-zero CPU%. The combination of zero GPU and zero CPU is indeed diagnostic of a deadlock. The assumption that the deadlock is within SGLang's code may be too narrow. The deadlock could be in the CUDA driver itself, in NCCL, or in PyTorch's CUDA runtime. The assistant had just built sgl-kernel against CUDA 13.1, but the system also has CUDA 12.8 installed for flash-attn compatibility. There could be library version conflicts or driver incompatibilities that manifest as a hang rather than a crash. The assistant doesn't check dmesg or CUDA driver logs in this message, which might reveal driver-level issues. The interpretation of 83% RSS as shared memory is correct in this case, but the assistant doesn't explicitly verify it with tools like smem or by checking /proc/[pid]/smaps. The earlier system memory check ([msg 3144]) provides supporting evidence, but the assistant is relying on an inference rather than a direct measurement. This is a reasonable shortcut for a debugging session, but it's worth noting as an unverified assumption. The lack of strace or GDB investigation is a notable omission. Given that the processes are alive and idle, attaching strace -p to one of the scheduler processes would reveal what system call it's blocked on (e.g., futex for a pthread mutex, poll for a socket, or ioctl for a CUDA device). This would pinpoint the deadlock location. The assistant doesn't take this step, likely because the next message ([msg 3154]) opts for a different debugging strategy (restarting with different flags). In a production debugging context, strace would be the faster path to root cause.

The Thinking Process Visible in the Message

The reasoning in this message is compact but reveals a structured thought process:

  1. Observation: The log hasn't progressed beyond weight loading, and the server isn't listening.
  2. Hypothesis generation: Maybe the processes are doing something useful that doesn't produce log output or open a port — like CUDA graph compilation.
  3. Test design: Check GPU utilization (for CUDA work) and process CPU usage (for any activity). The dual check covers both compute domains.
  4. Data collection: Run nvidia-smi for GPU metrics and ps for process metrics, in a single SSH command to get a consistent snapshot.
  5. Analysis: All zeros across both metrics = deadlock. The 83% RSS confirms the model is loaded but nothing is running.
  6. Implication: The deadlock is in the initialization phase after weight loading, not in weight loading itself. The assistant doesn't state the conclusion explicitly in this message — it lets the data speak. But the choice of commands and the framing ("check if the scheduler processes are actually doing anything useful") reveals that the assistant has already formed the deadlock hypothesis and is gathering confirmatory evidence. The next message ([msg 3154]) acts on this conclusion by killing the processes and trying a different configuration.

Conclusion

Message <msg id=3153> is a masterclass in efficient systems debugging. In a single SSH command, the assistant transforms a vague "server isn't ready" problem into a precise "all 8 TP workers are deadlocked after weight loading" diagnosis. The zero-GPU, zero-CPU snapshot is unambiguous and actionable. It rules out slow initialization, silent crash, and resource exhaustion in one shot.

This message also illustrates the importance of domain knowledge in debugging. The assistant knows to check scheduler processes specifically, understands that 83% RSS across 8 processes is shared memory rather than OOM, and recognizes that symmetric behavior across all ranks points to a collective operation deadlock. This knowledge didn't come from the current message alone — it was built across dozens of earlier messages that explored SGLang's architecture, memory patterns, and SM120 compatibility.

The deadlock itself, while frustrating, is a natural consequence of running bleeding-edge software on bleeding-edge hardware. SGLang's SM120 support is nascent; the sgl-kernel had to be custom-built; the Blackwell architecture (compute capability 120) is new enough that CUDA graph compilation paths, NCCL collective algorithms, and attention backends may all have untested interactions. The assistant's methodical approach — build, test, observe, pivot — is exactly the right strategy for this frontier.

In the broader arc of the conversation, this message marks the point where SGLang on SM120 goes from "promising" to "blocked." The 22-second model load time was exhilarating; the deadlock is sobering. But the diagnostic clarity achieved here means the next steps are well-defined: try disabling CUDA graphs, increase logging verbosity, or investigate NCCL configuration. The deadlock has been identified, even if its root cause remains to be found.