The Silent Deadlock: Debugging SGLang on Blackwell GPUs

Introduction

In the high-stakes world of large language model deployment, few things are more disorienting than a silent failure. After successfully loading a 547-billion-parameter model in just 22 seconds—a dramatic improvement over the 25-minute load times of vLLM—the SGLang server simply... stopped. Zero CPU utilization. Zero GPU activity. No listening port. No error messages. Just a cluster of sleeping processes consuming 83% of system memory, frozen in place like a paused film.

Message <msg id=3155> captures the precise moment when the assistant pivots from operational deployment to systematic debugging. Faced with a deadlock that produces no tracebacks, no crash logs, and no obvious failure point, the assistant launches a diagnostic variant of the SGLang server with CUDA graph compilation disabled and debug-level logging enabled. This message is the turning point in a debugging odyssey—the moment when the assistant acknowledges that something fundamentally incompatible exists between SGLang and the SM120 (Blackwell) architecture, and begins the painstaking work of isolating the root cause.

The Context: From Triumph to Deadlock

The journey to this message spans multiple segments of intense engineering work. The assistant had successfully deployed Kimi-K2.5 INT4—a 1-trillion-parameter Mixture-of-Experts model—on 8x RTX PRO 6000 Blackwell GPUs. After extensive profiling that identified AllReduce as the dominant bottleneck at 51.5% of decode time (see [msg 3128] for the todo list), the assistant had invested heavily in speculative decoding as a software-only optimization path.

The EAGLE-3 training pipeline had been built from scratch: synthetic data generation, hidden state extraction at 3,165 tok/s producing 828 GB of training data, and a 5-epoch finetune completing in 2.6 hours. But when testing the trained drafter 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. This was not a training quality issue; the pre-trained AQ-MedAI baseline showed the same behavior. The problem was a fundamental incompatibility between vLLM's EAGLE-3 integration and MLA (Multi-head Latent Attention) hidden state extraction during decode.

The user directed the assistant to pivot to SGLang, which boasts 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 the server. The initial results were breathtaking: SGLang loaded the 547 GB model in 22 seconds versus vLLM's 25 minutes. But then came the silence.

The Diagnostic Trail

The messages leading up to <msg id=3155> reveal a meticulous debugging process. In <msg id=3137>, the assistant checks if the server is serving and finds nothing—curl to both /v1/models and /health returns empty responses. In <msg id=3138>, GPU memory shows 76 GB per GPU with 2-3% utilization—the model is loaded but idle. After waiting 5 minutes (<msg id=3139>), still no response. In <msg id=3141>, the assistant discovers 0% CPU and 0% GPU across all 8 scheduler processes. The process is alive but completely stalled.

The assistant checks for errors in <msg id=3140> and finds only harmless import warnings about missing GLM modules. No tracebacks, no exceptions, no crashes. In <msg id=3143>, the assistant examines process memory and finds each TP worker using ~425 GB virtual address space—90% of the system's 449 GB RAM. The processes are in "Sl" state (sleeping, multithreaded). But actual physical memory is only 33 GB used (<msg id=3144>), with 416 GB in buff/cache. The system is not out of memory; the processes are simply... waiting.

A critical hypothesis emerges: CUDA graph compilation may be hanging. The assistant kills the process and tries again without EAGLE-3 (<msg id=3147>) to isolate whether the speculative decoding integration is the culprit. But the same deadlock occurs—model loads in 22 seconds, then silence. In <msg id=3153>, the assistant observes all 8 scheduler processes at 0% CPU, 0% GPU, consuming 82-83% memory each. The deadlock is in the base SGLang server, not the EAGLE-3 integration.

The Reasoning Behind Message 3155

Message <msg id=3155> represents a deliberate diagnostic pivot. The assistant makes two key changes to the launch command:

First, it sets SGLANG_LOG_LEVEL=DEBUG as an environment variable and passes --log-level debug as a command-line flag. This is a belt-and-suspenders approach—the assistant cannot be certain which logging system SGLang uses (Python's logging module, a custom framework, or environment-variable-driven logging), so it sets both. The goal is to capture any diagnostic output that might reveal where the deadlock occurs.

Second, it adds --disable-cuda-graph. This is the most significant change. CUDA graph compilation is an optimization that captures GPU kernel launch sequences into reusable graphs, reducing launch overhead. But on a new architecture like SM120 (Blackwell), the CUDA graph capture mechanism may have bugs or incompatibilities. By disabling it, the assistant tests the hypothesis that CUDA graph compilation is the deadlock point.

The assistant also redirects output to a new log file (sglang_base_debug.log) rather than overwriting the previous test log, preserving the evidence trail.

Assumptions and Knowledge Required

To understand this message, one must grasp several layers of technical context:

The SM120 architecture: Blackwell GPUs use compute capability 120, which is new enough that software support is still maturing. The assistant had already discovered that sgl-kernel's load_utils.py maps SM120 to the sm100 directory (see <msg id=3123>), and that the precompiled SM100 binaries needed rebuilding for SM120 compatibility.

CUDA graph compilation: This is an optimization where CUDA captures a sequence of kernel launches into a graph that can be replayed with minimal CPU overhead. On new architectures, the graph capture process can trigger driver bugs, memory allocation issues, or synchronization deadlocks.

Tensor Parallelism (TP): The --tp-size 8 flag splits the model across 8 GPUs. Each TP worker runs as a separate process with NCCL-based communication. A deadlock in one worker's initialization (e.g., during CUDA graph capture) can stall all workers as they wait for collective operations.

SGLang's architecture: Unlike vLLM's one-process-per-GPU model, SGLang uses a scheduler process per TP rank plus a main process. The assistant's observation of 8 scheduler processes plus the main Python process confirms this architecture is running correctly up to a point.

The assistant makes a key assumption: that the deadlock occurs during initialization/post-loading warmup, not during weight loading itself. This is supported by the evidence—weight loading completes successfully (100% safetensors shards loaded), and then the process goes silent. The assumption is reasonable and guides the diagnostic strategy.

The Thinking Process

The assistant's reasoning, visible across the preceding messages, follows a clear diagnostic arc:

  1. Observation: Model loads fast, then server never becomes available.
  2. Hypothesis 1: EAGLE-3 integration is causing the hang. Test: launch without EAGLE-3. Result: same hang.
  3. Hypothesis 2: CUDA graph compilation is hanging. Test: launch with --disable-cuda-graph. This is message <msg id=3155>.
  4. Hypothesis 3: Insufficient logging obscures the failure point. Test: enable debug logging at both environment and command-line levels. The assistant does not jump to conclusions. It systematically eliminates variables: first removing speculative decoding, then disabling CUDA graphs. The message represents the second step in this elimination process.

Potential Mistakes and Incorrect Assumptions

The assistant's primary assumption—that CUDA graph compilation is the deadlock point—may be incorrect. The deadlock could stem from:

Input and Output Knowledge

Input knowledge required to understand this message includes: the SM120 architecture's newness and potential incompatibilities, SGLang's server architecture and command-line flags, CUDA graph compilation mechanics, the history of EAGLE-3 integration attempts, and the specific hardware configuration (8x Blackwell GPUs, PCIe-connected, 449 GB RAM).

Output knowledge created by this message is the debug log file at /data/eagle3/synth_10k/sglang_base_debug.log, which will contain verbose output from the SGLang server initialization process. This log is the key diagnostic artifact that will either confirm the CUDA graph hypothesis or point toward the true root cause. The message also establishes a reproducible diagnostic procedure that can be refined in subsequent iterations.

Conclusion

Message <msg id=3155> is a masterclass in systematic debugging under uncertainty. Faced with a silent deadlock that produces no errors, no crashes, and no diagnostic output, the assistant formulates a clear hypothesis—CUDA graph compilation is hanging on SM120—and designs a targeted experiment to test it. The message captures the transition from operational deployment to root-cause analysis, a pivot that requires intellectual discipline: resisting the temptation to blame the EAGLE-3 integration, the model weights, or the hardware, and instead focusing on what can be controlled and measured.

The debugging journey is far from over. If --disable-cuda-graph resolves the deadlock, the assistant will need to either fix CUDA graph compilation for SM120 or work without it. If the deadlock persists, the investigation must continue into NCCL, Triton, or the CUDA driver itself. But this message marks the critical first step: a well-reasoned diagnostic probe into the unknown territory of Blackwell GPU software compatibility.