The Zero-Utilization Deadlock: Debugging SGLang on Blackwell SM120

Introduction

In the high-stakes world of large language model deployment, few moments are as simultaneously promising and frustrating as watching a server load a 547-billion-parameter model in 22 seconds—only to then sit completely idle. This is precisely the scenario captured in message 3141 of an extended coding session, where an AI assistant is debugging a SGLang server running the Kimi-K2.5 INT4 model across 8 NVIDIA Blackwell RTX PRO 6000 GPUs. The message represents a critical diagnostic pivot point: the assistant has just completed a monumental effort to build and train an EAGLE-3 speculative decoding drafter, pivoted from vLLM to SGLang due to fundamental integration issues, and now faces a new and puzzling failure mode.

The Message: A Diagnostic Snapshot

The subject message reads in full:

No real errors, just harmless import warnings. The log ended at weight loading. The process may be stuck or log output heavily buffered. Let me check with strace or just check if the process is alive and CPU-active:

`` [bash] ssh root@10.1.230.174 'ps aux | grep sglang | grep -v grep | awk "{print \$3, \$11}" | head -5; echo "---"; nvidia-smi --query-gpu=index,utilization.gpu --format=csv,noheader' 0.0 /root/ml-env/bin/python3 0.0 sglang::scheduler_TP0 0.0 sglang::scheduler_TP1 0.0 sglang::scheduler_TP2 0.0 sglang::scheduler_TP3 --- 0, 0 % 1, 0 % 2, 0 % 3, 0 % 4, 0 % 5, 0 % 6, 0 % 7, 0 % ``

At first glance, this is a simple diagnostic command. But the data it returns tells a devastating story: the SGLang server processes are alive—the main Python process and four scheduler TP (tensor parallelism) workers are visible—but they are consuming zero CPU and zero GPU utilization across all eight Blackwell GPUs. The server is not serving requests, not listening on its port, and not making progress. It is, to all appearances, deadlocked.

Context and Motivation: Why This Message Was Written

To understand the significance of this message, one must appreciate the journey that led to it. The session began with a complete ML environment setup on Ubuntu 24.04, including NVIDIA driver installation, CUDA Toolkit 13.1, and PyTorch configuration. The team then spent enormous effort deploying and benchmarking multiple 1-trillion-parameter models on 8 Blackwell GPUs, including NVFP4 Kimi-K2.5, MiniMax-M2.5 FP8, and native INT4 Kimi-K2.5. Performance profiling revealed that AllReduce operations dominated decode time at 51.5%, leading to an investigation of speculative decoding as a software-only optimization path.

The assistant then built a complete EAGLE-3 training pipeline from scratch, generating 10,000 synthetic training examples by capturing Kimi-K2.5's actual reasoning outputs, extracting hidden states at 3,165 tokens per second, and finetuning a drafter model over 5 epochs. This was a monumental engineering achievement spanning hundreds of messages.

But when the trained drafter was tested with vLLM's EAGLE-3 integration, the results were devastating: only ~15% acceptance rate, yielding 0.66x throughput—worse than running without speculation at all. The assistant correctly diagnosed this as a fundamental vLLM integration issue with Multi-Head Latent Attention (MLA) hidden state extraction during decode, not a training quality problem. Both the newly trained drafter and the pre-trained AQ-MedAI baseline exhibited the same poor performance.

The user then directed a 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 architecture (a 48-minute compilation), verified it worked, and launched the server. The model loaded in just 22 seconds—a dramatic improvement over vLLM's 25-minute load time. But then silence. The log stopped after weight loading. No errors, no crashes, no serving.

The Diagnostic Reasoning Process

The assistant's thinking in this message reveals a systematic debugging approach. First, it examined the server log for errors, finding only harmless import warnings about missing GLM OCR and ASR model modules. These warnings are expected when loading a Kimi model on a SGLang build that supports multiple model architectures—they don't indicate a problem.

The assistant then considered two hypotheses: (1) the process is stuck somewhere after weight loading, or (2) the log output is heavily buffered and the server is actually making progress but not flushing its output. The mention of "strace" as an alternative diagnostic tool shows the assistant considering deeper system-level investigation.

The chosen diagnostic—checking process CPU usage and GPU utilization—is elegant and informative. The ps aux output reveals something important: the scheduler TP workers are named sglang::scheduler_TP0 through sglang::scheduler_TP3, indicating that SGLang's tensor parallelism is working (4 scheduler processes for 8 GPUs, likely with 2 GPUs per process). But all show 0.0% CPU. The nvidia-smi output confirms zero GPU utilization across all 8 GPUs.

Assumptions and Potential Misinterpretations

The assistant's initial assessment—"No real errors, just harmless import warnings"—is technically correct but potentially misleading. While there are no Python tracebacks or crash logs, the situation is clearly an error state: a server that loads but never serves is broken. The assistant's framing suggests it hasn't yet fully accepted the severity of the problem.

The hypothesis that log output is "heavily buffered" is a reasonable consideration—Python's print buffering can cause log lines to appear long after they're generated, especially in multiprocess environments. However, the zero CPU utilization contradicts this: if the server were actively compiling CUDA graphs or initializing, it would show non-zero CPU activity. The zero CPU utilization strongly suggests the process is blocked on something—likely a CUDA synchronization primitive, a deadlock in NCCL (NVIDIA Collective Communications Library), or a hang during GPU kernel initialization.

Another implicit assumption is that the SM120 build of sgl-kernel is fully compatible. The assistant had previously verified that import sgl_kernel worked after loading torch, and that SGLang could be imported. But runtime compatibility is a different matter—the CUDA kernels compiled for SM120 may have subtle issues that only manifest during actual model execution, not during import.

Input Knowledge Required

To fully understand this message, one needs knowledge of several domains:

SGLang Architecture: SGLang uses a distributed serving architecture with tensor parallelism (TP). Each TP rank runs a scheduler process that manages GPU memory and executes model forward passes. The naming convention sglang::scheduler_TP0 through sglang::scheduler_TP3 indicates 4 TP worker processes, which for 8 GPUs implies 2 GPUs per process (using NVLink or PCIe interconnects).

CUDA Graph Compilation: Modern inference engines like SGLang and vLLM use CUDA graph compilation to optimize execution. After model weights are loaded, the engine typically runs a warmup phase that compiles CUDA graphs for common operation sequences. This can take minutes and may appear as a hang if something goes wrong during compilation.

SM120 Architecture: The NVIDIA Blackwell architecture (compute capability 12.0) is new and may have compatibility issues with software built for SM100 (Hopper). The sgl-kernel build system maps SM120 to the sm100 directory, and while the CMake configuration includes sm_120a code generation, the runtime loading and execution may still have issues.

NCCL and Collective Operations: On multi-GPU systems, NCCL handles communication between GPUs. Deadlocks in NCCL initialization or allreduce operations are common failure modes, especially on new hardware architectures where NCCL may not have optimal tuning.

Output Knowledge Created

This message produces several pieces of critical diagnostic information:

Processes are alive but stalled: The SGLang server processes haven't crashed or exited. They're in a blocked state, waiting on something.
No GPU work is happening: Zero GPU utilization across all 8 GPUs means the model isn't running any forward passes, not even warmup or initialization kernels.
The deadlock occurs after weight loading: Since the log shows 100% weight loading completion (64/64 shards), the hang occurs in the post-loading phase—likely CUDA graph compilation, drafter model initialization, or NCCL collective setup.
The EAGLE-3 drafter may be implicated: The server was launched with --speculative-algorithm EAGLE3 and --speculative-draft-model-path /data/eagle3/aq-medai-k2-drafter. If the drafter model loading or integration with the base model causes a deadlock, this would explain the hang.
The TP worker count is 4, not 8: This is interesting—with --tp-size 8, one might expect 8 scheduler processes. The fact that only 4 are visible suggests SGLang may be using 2 GPUs per process, or some workers may have failed to start.

The Broader Significance

This message captures a moment of transition between triumph and setback. The assistant has just achieved what many would consider impossible: loading a 547-billion-parameter model in 22 seconds on 8 GPUs. But the victory is hollow if the server doesn't actually serve. The zero-utilization deadlock represents a class of problems that are notoriously difficult to debug—silent failures where nothing crashes, nothing errors, and nothing progresses.

The debugging approach shown here—checking logs, then process status, then GPU utilization—is methodical and correct. The next steps would likely involve enabling verbose logging, disabling CUDA graph compilation, testing without the EAGLE-3 drafter, and potentially running with a single GPU to isolate the issue. The assistant's mention of "strace" as an alternative suggests readiness to dive into system-level debugging if needed.

For the reader, this message illustrates a fundamental truth about large-scale ML system deployment: the hardest problems are often not about getting code to run, but about getting it to run correctly at scale. A model that loads but doesn't serve is functionally equivalent to a model that crashes—but far more confusing to debug. The silence of zero utilization is, in its own way, louder than any error message.