Debugging SGLang on Blackwell: A Diagnostic Crossroads

The Message

ssh root@10.1.230.174 'ps aux | grep -E "sglang|python3" | grep -v grep | head -20'
root       50299  0.0  0.2 55478556 1035232 ?    Sl   22:53   0:16 /root/ml-env/bin/python3 -m sglang.launch_server --model-path /shared/kimi-k2.5-int4 --trust-remote-code --tp-size 8 --mem-fraction-static 0.85 --host 0.0.0.0 --port 8000 --disable-cuda-graph --log-level debug
root       50434  0.0  0.0  18416 11500 ?        S    22:53   0:00 /root/ml-env/bin/python3 -c from multiprocessing.resource_tracker import main;main(77)
root       50435  0.0 90.4 934181816 426061196 ? Sl   22:53  18:36 sg...

ssh root@10.1.230.174 'wc -l /data/eagle3/synth_10k/sglang_base_debug.log 2>/dev/null; echo "---"; tail -80 /data/eagle3/synth_10k/sglang_base_debug.log 2>/dev/null'
238 /data/eagle3/synth_10k/sglang_base_debug.log
---
[2026-02-22 22:53:28 TP6] Ignore import error when loading sglang.srt.models.glm_ocr: No module named 'transformers.models.glm_ocr'
[2026-02-22 22:53:28 TP0] Ignore import error when loading sglang.srt.models.glmasr: cannot import name 'GlmAsrConfig' from 'transformers' (/root/ml-env/lib/python3.12/site-packages/transformers/__init__.py)
[2026-02-22 22:53:28 TP6] Ignore import error when loading sglang.srt.models.glm_ocr_nextn: No module named...

ssh root@10.1.230.174 'nvidia-smi --query-gpu=index,memory.used,memory.total,utilization.gpu --format=csv,noheader'
0, 76015 MiB, 97887 MiB, 0 %
1, 76015 MiB, 97887 MiB, 0 %
2, 76017 MiB, 97887 MiB, 0 %
3, 76017 MiB, 97887 MiB, 0 %
4, 76015 MiB, 97887 MiB, 0 %
5, 76015 MiB, 97887 MiB, 0 %
6, 76015 MiB, 97887 MiB, 0 %
7, 76015 MiB, 97887 MiB, 0 %

This single message, delivered at a critical juncture in an intensive machine learning infrastructure debugging session, captures a moment of diagnostic clarity. The assistant is checking on a SGLang server launch that was supposed to be the breakthrough in a weeks-long effort to deploy speculative decoding for a 1-trillion-parameter MoE model on cutting-edge Blackwell GPUs. Instead, the output tells a story of deadlock, silence, and stalled progress.

The Context: A Long Road to Speculative Decoding

To understand why this message exists, we must first understand the broader mission. The assistant and user are deploying Kimi-K2.5 INT4, a 1-trillion parameter Mixture-of-Experts language model, across 8 NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs (SM120 architecture, compute capability 12.0). These are brand-new GPUs—so new, in fact, that much of the software ecosystem does not yet support them properly.

The core challenge is throughput. The model is enormous (547 GB on disk, 64 safetensors shards), and the GPUs communicate only over PCIe Gen5 (no NVLink). Profiling revealed that AllReduce operations consume 51.5% of decode time—a fundamental PCIe bottleneck. The team's strategy to overcome this is EAGLE-3 speculative decoding, a technique where a small draft model generates multiple candidate tokens in parallel, and the large verifier model accepts or rejects them in a single forward pass. If the acceptance rate is high enough (60-80%), throughput can nearly double.

The team had already built a complete EAGLE-3 training pipeline: generated 10,000 synthetic training samples via Kimi-K2.5 inference, extracted 828 GB of hidden states, trained a custom draft model for 5 epochs, and attempted to integrate it with vLLM. That integration failed catastrophically—the acceptance rate was only ~15%, making speculative decoding 34% slower than baseline. The root cause was traced to bugs in vLLM's EAGLE-3 implementation for DeepSeek V3 / MLA architectures.

The pivot to SGLang was the natural next step. SGLang has first-class EAGLE-3 support, and the pre-trained AQ-MedAI/Kimi-K2-Instruct-eagle3 drafter was explicitly tested with it. The team rebuilt sgl-kernel from source for SM120 (a 48-minute ordeal), verified that SGLang could load the 547 GB model in just 22-34 seconds (versus 25 minutes in vLLM), and then hit a wall: the server loaded the model and then went completely silent.## What the Message Reveals: Three Diagnostic Signals

The assistant issues three commands in parallel, each probing a different dimension of the system's state. Together, they form a complete diagnostic picture.

Signal 1: Process State — The Server is "Running" but Not Working

The first command checks running processes. The output shows PID 50299, the SGLang server launched with --disable-cuda-graph --log-level debug. Its state is Sl—sleeping, multithreaded. The RSS (Resident Set Size) is 1,035,232 KB, or about 1 GB. This is tiny compared to the 547 GB model. Where did the model go?

The answer lies in the scheduler processes. The output is truncated (the sg... at the end), but from earlier messages we know that SGLang spawns 8 scheduler processes (one per GPU), each consuming ~425 GB of virtual memory via shared memory mappings. The parent process (PID 50299) itself uses almost no memory—it's just a coordinator. The real work happens in the children.

The Sl state is critical. It means the process is sleeping, and it's multithreaded. But crucially, it's not doing any work. CPU usage is 0.0%. The server launched at 22:53 and has been alive for only 16 seconds of CPU time (the 0:16 in the process listing). But it has been running for much longer in wall-clock time—it was launched well before this message was sent.

Signal 2: The Log — Silence After Model Loading

The second command reads the debug log. Only 238 lines. The tail shows only import errors for unrelated model architectures (glm_ocr, glmasr, glm_ocr_nextn). These are harmless—SGLang tries to import every supported model architecture and logs warnings when optional dependencies are missing. The absence of any log entries after these import warnings is the real story.

The model finished loading. The log should show CUDA graph compilation, NCCL initialization, server startup, and the "ready" message. None of that appeared. The server loaded the model weights into GPU memory (all 8 GPUs show ~76 GB used out of ~97 GB), and then... nothing. No errors, no crashes, no progress. Just silence.

Signal 3: GPU Utilization — Zero, Across All Eight GPUs

The third command checks GPU memory and utilization. Every GPU shows 0% utilization. The memory usage (~76 GB per GPU) confirms the model is loaded—76 GB × 8 = 608 GB, which is consistent with a 547 GB model plus overhead for KV cache allocation and framework buffers. But the compute units are completely idle.

This is the most damning evidence. If the server were compiling CUDA graphs, initializing NCCL, or warming up, the GPUs would show activity. They don't. The system is deadlocked before any GPU work begins.

The Reasoning Behind the Commands

The assistant's choice of these three specific commands reveals a sophisticated diagnostic strategy. Rather than guessing at the root cause, the assistant systematically rules out possibilities.

The process check answers: "Is the server even running?" The answer is yes—the PID exists, the scheduler processes are alive. This rules out a crash or OOM kill.

The log check answers: "Did the server encounter an error?" The answer is no—the log ends without errors, just import warnings. This rules out a Python exception or configuration error.

The GPU check answers: "Is the server doing any GPU work?" The answer is no—0% utilization across all GPUs. This rules out the possibility that the server is simply slow and making progress. It's stuck.

Together, these three signals point to a very specific class of problem: a deadlock or hang that occurs after model loading but before any GPU computation begins. The most likely candidates are NCCL initialization (setting up communication between the 8 TP workers) or CUDA graph compilation (which SGLang does during startup and which was explicitly disabled with --disable-cuda-graph).

Assumptions and Their Validity

The assistant makes several assumptions in this message, most of which are well-founded but worth examining.

Assumption 1: The server is hung, not just slow. Given that the log shows no progress after model loading, and GPU utilization is zero, this is a reasonable conclusion. However, it's worth noting that the log is only 238 lines—the server might be doing CPU-side initialization that doesn't produce log output. The --log-level debug flag should catch everything, but SGLang might have its own logging quirks.

Assumption 2: The --disable-cuda-graph flag would help diagnose the issue. This assumption was made in the previous message (msg 3155) when the assistant launched this debug attempt. The reasoning was that CUDA graph compilation might be the source of the hang. By disabling it, the assistant hoped to isolate the problem. However, the hang persists even with CUDA graphs disabled, suggesting the deadlock is elsewhere—likely in NCCL initialization or the attention backend.

Assumption 3: The import errors for glm_ocr and glmasr are harmless. This is correct. These are warnings about optional model architectures that SGLang's model registry tries to import. They don't affect the Kimi-K2.5 deployment. The assistant correctly ignores them.

Assumption 4: The model is fully loaded and resident in GPU memory. The nvidia-smi output confirms this—76 GB per GPU, consistent with the 547 GB model plus overhead. This is a correct assumption.

The Knowledge Required to Interpret This Message

This message is dense with implicit knowledge. To fully understand it, one needs:

Knowledge of SGLang's architecture. SGLang uses a distributed serving architecture with a coordinator process and multiple scheduler workers (one per GPU in tensor-parallel mode). The coordinator launches the workers and manages the lifecycle. The workers load model shards into their respective GPUs. Understanding this architecture is essential to interpreting the process listing.

Knowledge of GPU memory management. The 76 GB per GPU figure must be contextualized against the 97 GB total VRAM per GPU. The ~21 GB of free memory per GPU is reserved for KV cache, activations, and temporary buffers. The --mem-fraction-static 0.85 flag tells SGLang to reserve 85% of available memory for the model, which is consistent with the observed allocation.

Knowledge of NCCL and distributed initialization. The 8 TP workers need to establish NCCL communication channels. On systems without NVLink, this happens over PCIe. NCCL initialization involves creating communication rings, allocating buffers, and establishing peer-to-peer connections. A hang during this phase is a known issue on new GPU architectures.

Knowledge of the SM120/Blackwell ecosystem. The NVIDIA RTX PRO 6000 Blackwell (SM120) is a very new architecture. Software support is immature. sgl-kernel had to be rebuilt from source because pre-built binaries don't support SM120. CUDA graph compilation, flashinfer, and other GPU compute libraries may have SM120-specific bugs.## The Thinking Process: A Methodical Debugging Approach

The assistant's reasoning, visible across the sequence of messages leading up to this one, follows a clear diagnostic pattern. The initial assumption was that SGLang had "hung" or "deadlocked" on SM120 (msg 3148-3154). The assistant tried waiting, checking the log, checking GPU utilization, and eventually killing the process and relaunching with debug flags.

But notice what the assistant does not do in this message. It does not immediately kill the process again. It does not try a different configuration. It simply observes. This is a deliberate choice—the assistant is gathering data before making the next move. The three commands are designed to answer three questions simultaneously: Is the process alive? Did it log anything useful? Is it using the GPUs?

The answer to all three is "sort of." The process is alive but sleeping. The log shows no errors but also no progress. The GPUs have the model loaded but are doing nothing. This is the profile of a blocking synchronization point—a place where all 8 TP workers are waiting for something that never completes.

The most likely candidate is NCCL initialization. In SGLang's startup sequence, after loading model weights, the workers must establish NCCL communication. This involves collective operations like ncclCommInitAll or ncclCommInitRank. If one worker fails to initialize (due to a driver bug, a PCIe timeout, or an SM120 incompatibility), all workers block indefinitely. The 0% GPU utilization is consistent with this—NCCL initialization is primarily CPU-side work (setting up communication channels) with minimal GPU involvement.

Another possibility is the attention backend initialization. SGLang defaults to the Triton attention backend. The log shows "Attention backend not specified. Use triton backend by default" from the earlier base test (msg 3150). If Triton's JIT compilation hangs or crashes on SM120, the server would deadlock. The --disable-cuda-graph flag was meant to eliminate CUDA graph compilation as a variable, but it doesn't affect the attention backend.

The Broader Narrative: A Pivot Point

This message sits at a critical inflection point in the larger story. The team has invested enormous effort in the EAGLE-3 path: training a custom draft model, patching vLLM, rebuilding sgl-kernel for SM120, and now debugging SGLang's startup hang. Each attempt has hit a wall.

The vLLM path failed because of hidden state alignment bugs in the EAGLE-3 integration for DeepSeek V3 / MLA architectures. The SGLang path is now failing because the server won't even start serving—it loads the model and then freezes. The AQ-MedAI pre-trained drafter, which was explicitly tested with SGLang and should give ~1.8x throughput, is sitting unused because the base server won't initialize.

What makes this moment particularly tense is the asymmetry between SGLang's weight loading speed and its startup reliability. SGLang loads the 547 GB model in 22-34 seconds—an incredible feat compared to vLLM's 25 minutes. But then it deadlocks. The fast loading is a testament to SGLang's efficient model loading pipeline (likely using direct tensor parallelism with NCCL broadcast from rank 0). The deadlock is a reminder that loading weights is only the first step—initializing the serving infrastructure is where the real complexity lies.

Output Knowledge Created

This message produces several pieces of actionable knowledge:

The SGLang server is alive but not progressing. PID 50299 exists, the scheduler processes are present, but no work is being done. This confirms the hang is not a crash or OOM.
The debug log is unhelpful. Only 238 lines, ending with harmless import warnings. No error messages, no stack traces, no indication of where the hang occurs. This tells the assistant that the hang is in a code path that either doesn't log or logs at a level above DEBUG.
GPU memory is fully allocated and idle. All 8 GPUs have ~76 GB allocated and 0% utilization. The model is loaded and resident, but no computation is happening.
The --disable-cuda-graph flag did not resolve the hang. This was the hypothesis from the previous message—that CUDA graph compilation was causing the deadlock. The negative result rules out that hypothesis and narrows the search space.
The hang occurs after model loading but before serving. This narrows the window to NCCL initialization, attention backend setup, or scheduler initialization.

What Comes Next

The assistant's next moves (visible in subsequent messages) follow logically from this diagnosis. The NCCL initialization hypothesis leads to trying different NCCL configurations: NCCL_PROTO=LL, NCCL_ALGO=Ring, NCCL_P2P_LEVEL=SYS, and other environment variables that change how NCCL establishes communication channels. The attention backend hypothesis leads to trying --attention-backend flashinfer instead of the default Triton backend.

But the deeper story is that this message marks the end of the "quick fix" phase. The team has tried the obvious solutions—rebuilding for SM120, disabling CUDA graphs, enabling debug logging—and the problem persists. The next steps require deeper investigation: patching SGLang's NCCL initialization code, debugging the attention backend, or potentially contributing fixes to the SGLang project itself.

Conclusion

This message is a masterclass in diagnostic minimalism. Three commands, each carefully chosen to probe a different dimension of the system, together tell a complete story. The assistant doesn't speculate about the root cause—it simply gathers data and lets the evidence speak.

The evidence speaks clearly: SGLang loads the model but cannot initialize the serving infrastructure on SM120. The 8 Blackwell GPUs sit idle, their 76 GB of model weights loaded and waiting, while the server sleeps. The import warnings for glm_ocr and glmasr scroll by harmlessly, a reminder that the software ecosystem is still catching up to the hardware.

For anyone working with cutting-edge GPU hardware, this message is a familiar scene. The hardware is fast, the software is promising, but the integration layer—where the inference engine meets the new GPU architecture—is where progress stalls. The assistant's methodical, data-driven approach to diagnosing the hang is a model for how to navigate these uncharted waters.