The Silence After Loading: Diagnosing a Deadlock on Blackwell GPUs

In the high-stakes world of large language model deployment, few moments are as tense as the seconds after a 547-billion-parameter model finishes loading onto eight GPUs. The weights are in place, the memory is allocated, and the server should be ready to serve requests. But sometimes, instead of the expected listening port and streaming responses, there is only silence. Message [msg 3138] captures exactly this moment — a brief, almost mundane diagnostic check that sits at the pivot point between hope and frustration, revealing the first concrete evidence of a deep compatibility problem between SGLang and NVIDIA's newest Blackwell (SM120) architecture.

The Road to This Moment

To understand the significance of this message, one must appreciate the journey that led to it. The team had spent weeks building a complete EAGLE-3 speculative decoding pipeline for Kimi-K2.5, a massive Mixture-of-Experts model. They had trained a custom drafter, generated synthetic data, and integrated everything into vLLM — only to discover that vLLM's EAGLE-3 integration with Multi-Head Latent Attention (MLA) yielded an abysmal ~15% acceptance rate, resulting in worse throughput than no speculation at all (0.66x). This was not a training quality problem; it was a fundamental integration issue with how vLLM extracts hidden states during MLA decode.

The user directed a hard pivot to SGLang, which has first-class EAGLE-3 support and is explicitly tested with Kimi-K2 drafters. The assistant spent nearly an hour building sgl-kernel from source for SM120 support, navigating a cascade of build issues — missing scikit-build-core, CMake version incompatibilities, missing libnuma-dev, and OOM crashes that required reducing parallel compilation jobs to 20. After the build succeeded, the first test was breathtaking: SGLang loaded the 547GB model in just 34 seconds, compared to vLLM's agonizing 25 minutes. It was a triumph.

But then the server went quiet.

The Diagnostic Turn

Message [msg 3138] is the assistant's first response to this silence. The previous message ([msg 3137]) had attempted to query the server's endpoints — curl http://localhost:8000/v1/models and /health — and received nothing but empty responses. The server was not listening. The assistant's reasoning, captured in the opening line, reveals its working hypothesis:

"Not serving yet. It's likely still in CUDA graph compilation or drafter loading."

This is a reasonable assumption. After loading model weights, a production inference server must perform several additional steps before it can accept requests: CUDA graph compilation (which optimizes and caches execution graphs for the specific model architecture), drafter model loading (for speculative decoding), and kernel warmup. These phases can take minutes and typically show significant GPU activity as the CUDA compiler and runtime libraries work to prepare the execution environment.

To test this hypothesis, the assistant runs a simple but revealing diagnostic:

nvidia-smi --query-gpu=index,memory.used,utilization.gpu --format=csv,noheader

The output is unambiguous:

0, 76017 MiB, 2 %
1, 76017 MiB, 3 %
2, 76017 MiB, 2 %
3, 76017 MiB, 3 %
4, 76017 MiB, 2 %
5, 76017 MiB, 2 %
6, 76017 MiB, 2 %
7, 76017 MiB, 3 %

What the Numbers Reveal

Each of the eight RTX PRO 6000 Blackwell GPUs shows exactly 76,017 MiB of memory used — approximately 76 GB out of what is likely 96 GB total per GPU. This confirms that the model weights have been successfully loaded onto all GPUs, distributed across the 8-way tensor parallelism configuration. The memory allocation is uniform across all devices, which is expected for a well-distributed model.

But the GPU utilization tells a different story. All eight GPUs show 2-3% utilization — effectively idle. If CUDA graph compilation were underway, the GPUs would be busy compiling and optimizing kernels, typically showing 50-100% utilization during this phase. If the drafter model were being loaded and initialized, there would be measurable compute activity. The near-idle GPUs strongly suggest that neither of these processes is happening.

This is the critical insight that message [msg 3138] provides: the model is loaded but nothing is running on the GPUs. The process is not busy — it is stuck. The assistant's hypothesis of "still in CUDA graph compilation or drafter loading" is gently falsified by the data it just collected. The GPUs are not compiling graphs; they are waiting.

The Thinking Process Visible in the Message

The message reveals a methodical diagnostic approach. The assistant does not jump to conclusions or immediately kill the process. Instead, it:

States its current hypothesis ("It's likely still in CUDA graph compilation or drafter loading") — making explicit the expected behavior.
Selects a diagnostic tool (nvidia-smi with memory and utilization queries) that can directly test the hypothesis.
Collects data from all 8 GPUs — ensuring the diagnosis covers the entire distributed system, not just one device.
Presents the raw data without interpretation — letting the numbers speak for themselves before drawing conclusions. This pattern — hypothesize, instrument, measure, interpret — is the hallmark of systematic debugging. The message is the "measure" step, and the interpretation will come in subsequent messages as the assistant processes what it has seen.

Assumptions Embedded in the Message

The message makes several assumptions worth examining:

Assumption 1: CUDA graph compilation is a normal part of startup. This is true for SGLang and vLLM, which compile and cache CUDA graphs during initialization to reduce per-request latency. However, the duration and GPU activity level depend on the model architecture and the number of different graph configurations needed.

Assumption 2: The process is still alive and making progress. The assistant assumes the process is "still in" a phase, implying forward progress. The subsequent messages ([msg 3139] through [msg 3145]) will reveal this assumption is incorrect — the process is actually deadlocked, with 0% CPU and 0% GPU utilization, and no listening port.

Assumption 3: GPU utilization is a reliable indicator of progress. This is generally true for CUDA graph compilation, which is GPU-intensive. However, it's worth noting that some initialization phases might be CPU-bound (e.g., loading the drafter model's weights into CPU memory before transferring to GPU), which would show low GPU utilization. The assistant's hypothesis of "drafter loading" could still be consistent with low GPU utilization if the drafter is small and its weights are being processed on the CPU.

Assumption 4: The model loaded successfully. The uniform 76 GB memory allocation across all GPUs strongly supports this, but memory allocation alone doesn't guarantee that all tensors are correctly placed and that the model's forward pass is functional. A silent corruption or misconfiguration during weight loading could leave the model in a broken state that manifests as a hang rather than an error message.

Input Knowledge Required

To fully understand this message, one needs:

Knowledge of the SGLang/vLLM server startup sequence: weight loading → CUDA graph compilation → drafter loading → warmup → serving. The assistant's hypothesis places the server in the second or third phase.
Understanding of nvidia-smi output: what memory.used and utilization.gpu represent, and how to interpret them in the context of a distributed inference server.
Context from the previous messages: the pivot from vLLM to SGLang, the successful sgl-kernel build for SM120, and the lightning-fast 34-second weight loading.
Knowledge of Blackwell (SM120) architecture: the fact that SGLang's SM120 support is new and potentially unstable, and that the sgl-kernel build targeted SM120 but the load_utils.py maps SM120 to the sm100 directory, creating a potential mismatch.

Output Knowledge Created

This message produces several valuable pieces of knowledge:

The model is loaded on all 8 GPUs with uniform memory allocation (76,017 MiB each), confirming that weight distribution across tensor parallelism is working correctly.
GPU utilization is near-zero (2-3%), which is inconsistent with active CUDA graph compilation or drafter initialization.
The server is not yet listening on port 8000, confirming the process has not completed its startup sequence.
The assistant's initial hypothesis is likely incorrect — the process is probably stuck rather than still making progress. This diagnostic data sets the stage for the deeper investigation that follows in subsequent messages ([msg 3139]–[msg 3145]), where the assistant will discover that the process is completely idle (0% CPU, 0% GPU), that each TP worker has an enormous virtual memory allocation (~425 GB), and that the server is ultimately deadlocked — likely due to an SM120 compatibility issue in SGLang's initialization path.

The Broader Significance

Message [msg 3138] is significant not for what it accomplishes — it's a simple diagnostic check that doesn't resolve the problem — but for what it represents. It is the moment when the team's hopes for a quick SGLang deployment collide with reality. The 34-second weight loading was a tantalizing glimpse of what SGLang could offer, but the deadlock that follows reveals that SM120 support is not yet production-ready. The message marks the beginning of a debugging campaign that will consume the remainder of the session, involving strace analysis, process tree inspection, memory mapping investigation, and ultimately a pivot to verbose logging and CUDA graph disabling.

In the broader narrative of the opencode session, this message is a turning point. The EAGLE-3 training pipeline had been completed successfully, the vLLM integration had failed, and SGLang seemed like the perfect alternative — until this diagnostic check revealed that even basic model serving on SM120 was broken. The silence after loading was not the calm before the storm; it was the storm itself, and this message was the first lightning bolt to illuminate it.