The Silent Deadlock: Debugging SGLang on SM120 Blackwell GPUs

Introduction

In the high-stakes world of large language model deployment, few things are more disorienting than a process that simply stops without error. Message 3145 captures one such moment in an extended debugging session: the assistant has just loaded a 547-billion-parameter model (Kimi-K2.5 INT4) across 8 Blackwell GPUs using SGLang, only to discover that the server never started serving. The process is alive but idle—zero CPU utilization, zero GPU utilization, no listening port, and no error messages. This article examines that single message as a case study in systematic debugging under uncertainty.

The Context: A Long Road to Speculative Decoding

To understand message 3145, one must appreciate the journey that led to it. The assistant had spent the better part of a day building an EAGLE-3 speculative decoding pipeline for Kimi-K2.5, a 1-trillion-parameter Mixture-of-Experts model. The pipeline included synthetic data generation (10,000 samples), hidden state extraction, and fine-tuning a drafter model. The goal was to accelerate inference using speculative decoding—a technique where a smaller "draft" model proposes tokens that a larger "target" model verifies in parallel.

However, when the assistant tested the trained drafter with vLLM's EAGLE-3 integration, the results were devastating: only ~15% acceptance rate, yielding 0.66x throughput—worse than no speculation at all. 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 pivot required building sgl-kernel for SM120 (the Blackwell GPU architecture), which took 48 minutes. After installation, SGLang loaded the 547GB model in an astonishing 22 seconds—compared to 25 minutes in vLLM. But then the server went silent.

The Subject Message: A Diagnostic Snapshot

Message 3145 is the assistant's response to discovering that the SGLang server is not serving requests despite appearing to have loaded successfully. The full message reads:

33 GB used RAM, 416 GB in buff/cache, 76 GB per GPU. The system isn't OOM, it's just sleeping. The real problem is that the server never started. Let me kill and try without EAGLE-3 first to check if base SGLang works on SM120:

[bash] ssh root@10.1.230.174 'kill -9 $(ps aux | grep sglang | grep -v grep | awk "{print \$2}") 2>/dev/null; sleep 3; kill -9 $(fuser /dev/nvidia 2>/dev/null | tr " " "\n" | sort -u | grep -v "^$") 2>/dev/null; sleep 2; rm -f /dev/shm/; nvidia-smi --query-gpu=memory.used --format=csv,noheader | head -1' 76017 MiB

At first glance, this looks like a simple "kill and retry" command. But the reasoning embedded in the text reveals a sophisticated diagnostic process. The assistant has just checked the system state (in the preceding messages) and found that the SGLang processes are sleeping with 0% CPU and 0% GPU utilization, and that curl to localhost:8000 returns "Connection refused." The log file ends at weight loading with no errors. The process is alive but doing nothing.

The Reasoning: Why This Message Was Written

The assistant's opening line—"33 GB used RAM, 416 GB in buff/cache, 76 GB per GPU. The system isn't OOM, it's just sleeping"—is a direct response to a hypothesis that was implicitly being tested. In the preceding messages, the assistant had noticed that each TP (tensor parallelism) worker process was showing ~425 GB of virtual memory, leading to a suspicion of out-of-memory (OOM) conditions. But the free -h output shows only 33 GB of actual RAM usage, with 416 GB in buffer/cache. This is a critical distinction: the large virtual memory allocations are shared memory mappings (typical for GPU tensor parallelism workers that use CUDA IPC or NCCL), not physical RAM consumption.

The assistant correctly concludes that the system is not OOM—it's "just sleeping." This reframes the problem. If the system has plenty of memory and the GPUs are loaded (76 GB each), why isn't the server responding? The phrase "The real problem is that the server never started" is the key insight. Weight loading completed, but the server initialization—the part that sets up the HTTP listener, CUDA graphs, and request handling loop—never executed or got stuck.

The Decision: Isolate by Removing EAGLE-3

The assistant's decision to "try without EAGLE-3 first" is a textbook debugging maneuver: isolate the variable. The SGLang command that was launched included --speculative-algorithm EAGLE3 and --speculative-draft-model-path /data/eagle3/aq-medai-k2-drafter. If the deadlock is caused by the EAGLE-3 integration—perhaps the drafter model loading, the hidden state extraction, or the speculative verification logic—then running base SGLang without speculative decoding would bypass that entire code path.

This decision is grounded in the observation that the log file ended cleanly after weight loading. Weight loading for the base model (Kimi-K2.5 INT4) completed successfully, but there is no log output about the drafter model loading or the CUDA graph warmup. The assistant cannot tell whether the deadlock occurs during drafter loading, CUDA graph compilation, or some other initialization step—but removing EAGLE-3 eliminates the most likely new variable.

Assumptions Embedded in the Message

Several assumptions underlie the assistant's reasoning:

The SM120 GPU architecture is not fundamentally incompatible with SGLang. The assistant has already built sgl-kernel for SM120 and verified that import sgl_kernel works. But the kernel library only provides low-level operations; the SGLang server itself may have SM120-specific issues in its CUDA graph compilation or attention backend.
The deadlock is in server initialization, not in weight loading. The log shows weight loading completing at 100%, with no subsequent log output. The assistant assumes the deadlock occurs after weight loading, during the initialization phase. This is a reasonable inference, but it's also possible that weight loading is not truly complete—perhaps a finalization step is stuck.
EAGLE-3 is the likely culprit. The assistant hypothesizes that removing speculative decoding will resolve the issue. This is based on the fact that base SGLang (without EAGLE-3) is a more mature, well-tested code path. The EAGLE-3 integration in SGLang is newer and may have SM120-specific bugs.
The kill-and-cleanup sequence is sufficient. The assistant kills processes, kills processes holding NVIDIA device files, removes shared memory files (/dev/shm/*), and then checks GPU memory. This assumes that a clean restart is possible without rebooting the machine.

Potential Mistakes and Incorrect Assumptions

While the assistant's reasoning is sound, there are potential pitfalls:

The deadlock may not be in EAGLE-3 at all. If base SGLang also fails on SM120, the problem is deeper—perhaps in the CUDA graph compilation, the attention backend, or the NCCL initialization for 8-GPU tensor parallelism. The assistant's debugging strategy would then need to pivot again.
The "sleeping" process state may be misleading. A process in state Sl (sleeping, multithreaded) with 0% CPU could be waiting on a blocking system call, such as a CUDA kernel launch that never returns, a NCCL collective operation that deadlocks, or a file descriptor that never becomes readable. The assistant does not investigate the process's wait channel (/proc/PID/wchan) or stack trace, which could reveal exactly where it's stuck.
The cleanup may not be thorough enough. Killing processes and removing /dev/shm/* may leave residual CUDA contexts or NCCL state. A more thorough approach might involve resetting the NVIDIA driver with nvidia-smi -r or rebooting.
The assumption that 76 GB per GPU means "loaded successfully" may be premature. The model is 547 GB in INT4, which with 8 GPUs means ~68 GB per GPU. The 76 GB usage includes overhead (CUDA contexts, NCCL buffers, etc.). But it's possible that only the weight tensors were loaded, and the model's execution graph or KV cache was never initialized.

Input Knowledge Required

To fully understand this message, a reader needs knowledge of:

Tensor parallelism (TP): The model is split across 8 GPUs, with each process handling one GPU. The TP0 through TP7 labels in the log indicate 8 parallel workers.
CUDA IPC and shared memory: GPU tensor parallelism workers use shared memory for NCCL communication, which explains the large virtual memory allocations without corresponding physical RAM usage.
SGLang server architecture: The server goes through phases: model loading, CUDA graph warmup, and HTTP listener startup. The deadlock occurs between loading and serving.
Speculative decoding with EAGLE-3: A technique where a drafter model proposes tokens and the base model verifies them. SGLang has native support for this, but the integration with MLA (Multi-head Latent Attention) attention may have issues.
SM120 (Blackwell) GPU architecture: The newest NVIDIA architecture, which may have compatibility issues with software compiled for SM100 (Hopper).
Process states in Linux: The Sl state (sleeping, multithreaded) and what it implies about the process's activity.

Output Knowledge Created

This message creates several pieces of actionable knowledge:

A confirmed diagnosis: The SGLang server with EAGLE-3 on SM120 deadlocks silently after weight loading. This is a new finding—previous work had established that SGLang works on Hopper (SM90/SM100) GPUs, but SM120 compatibility is unproven.
A debugging strategy: Isolate the problem by removing speculative decoding. If base SGLang works, the bug is in EAGLE-3 integration. If base SGLang also fails, the bug is in the core SGLang server on SM120.
A cleanup procedure: The kill-and-cleanup sequence (kill processes, release NVIDIA device files, remove shared memory) provides a template for restarting failed server attempts.
A baseline measurement: The system has 33 GB used RAM, 416 GB buff/cache, and 76 GB per GPU after loading. This serves as a reference for future attempts.

The Thinking Process Revealed

The assistant's thinking process, visible in the message's structure, follows a clear diagnostic arc:

Observe: The server is not responding (curl fails), but the process is alive (ps shows it running).
Measure: Check system resources (free, nvidia-smi) to rule out OOM.
Interpret: 33 GB used RAM means no OOM. The process is sleeping, not crashed.
Reframe: The problem is not resource exhaustion but a deadlock in initialization.
Hypothesize: EAGLE-3 integration is the most likely cause (newest code, most complex).
Design experiment: Remove EAGLE-3, run base SGLang, observe behavior.
Execute: Kill processes, clean up, prepare for next attempt. This is textbook scientific debugging: observe, measure, hypothesize, test. The assistant resists the temptation to chase symptoms (like the large virtual memory numbers) and instead focuses on the core question: what changed between "weight loading complete" and "server should be serving"?

Conclusion

Message 3145 is a small but illuminating snapshot of a debugging process in real time. It captures the moment when a promising path (SGLang loading in 22 seconds) hits an unexpected obstacle (silent deadlock), and the assistant pivots to a systematic isolation strategy. The message reveals not just what the assistant did, but how it thought—ruling out OOM, reframing the problem, designing an experiment, and executing it cleanly. Whether base SGLang succeeds or fails on SM120, the debugging approach itself is a model of disciplined troubleshooting under uncertainty.