The 22-Second Mirage: Diagnosing an SM120 Deadlock in SGLang

In the high-stakes world of large-scale ML inference, few moments are as exhilarating as watching a 547-billion-parameter model load in 22 seconds. After enduring 25-minute load times with vLLM, the assistant in this opencode session had just achieved a dramatic speedup with SGLang on an 8-GPU Blackwell (SM120) system. But the excitement was short-lived. Message 3150 captures the precise moment when that initial triumph curdled into suspicion—and ultimately into the discovery of a fundamental compatibility deadlock between SGLang and the SM120 architecture.

The Message in Full

The subject message is deceptively brief. It consists of a single line of commentary from the assistant—"Loaded in 22 seconds! Now waiting for warmup:"—followed by a bash command that sleeps for 300 seconds before probing the server:

sleep 300 && ssh root@10.1.230.174 'curl -s http://localhost:8000/v1/models 2>/dev/null | head -5; echo "---"; curl -s http://localhost:8000/health 2>/dev/null; echo "---"; grep "server\|Server\|startup\|Application\|ready\|Ready\|listening\|Listening" /data/eagle3/synth_10k/sglang_base_test.log | tail -5'

The results are ominous. Both curl commands return empty output—the server is not listening on port 8000. The log grep returns only a single line: an informational message from server_args.py dumping the server configuration, including the critical detail speculative_algorithm=None. There is no "ready" message, no "listening" message, no indication that the server ever completed its startup sequence.

Why This Message Was Written

This message sits at a pivotal juncture in a much longer debugging narrative. The assistant had been working for hours—across multiple segments and dozens of messages—to deploy the Kimi-K2.5 INT4 model with speculative decoding on 8x NVIDIA Blackwell GPUs. The journey had already seen several dramatic pivots:

vLLM with EAGLE-3 had been thoroughly tested and found to achieve only ~15% acceptance rate, yielding 0.66x throughput—worse than no speculation at all. This was diagnosed as a fundamental integration issue with MLA (Multi-Head Latent Attention) hidden state extraction during decode.
SGLang was identified as the alternative, since it has first-class EAGLE-3 support and is explicitly tested with Kimi-K2 drafters. The assistant built sgl-kernel for SM120 (a 48-minute compilation) and verified it worked.
The first SGLang attempt with EAGLE-3 (msg 3131–3146) loaded the model but then silently stalled—zero CPU utilization, zero GPU utilization, no listening port. The processes appeared to be in a deadlock state.
The base SGLang test (msg 3147) was launched without EAGLE-3 to isolate whether the problem was specific to speculative decoding or a more general SM120 compatibility issue. Message 3150 is the first check on that base test. The assistant's commentary—"Loaded in 22 seconds! Now waiting for warmup:"—reveals the working hypothesis at this moment: that the model weights had loaded successfully, and the server was simply taking time to compile CUDA graphs, warm up, or perform other initialization tasks. The 300-second sleep was a generous allowance for that process.

The Reasoning and Assumptions

The assistant's thinking, visible through the sequence of actions, reveals several layers of reasoning:

Assumption 1: Fast loading implies fast readiness. The model loaded 64 safetensors shards in 22 seconds—roughly 68× faster than vLLM's 25 minutes. It was natural to assume that SGLang's initialization pipeline would be correspondingly efficient. The assistant expected that after 5 minutes of warmup, the server would be ready to serve requests.

Assumption 2: The EAGLE-3 deadlock was EAGLE-3-specific. When the first SGLang attempt (with the AQ-MedAI drafter) stalled, the assistant's debugging focused on the speculative decoding path. The decision to test without EAGLE-3 was a classic debugging technique: remove variables to isolate the root cause. The assumption was that if the base server worked, the problem was in the drafter loading or integration.

Assumption 3: CUDA graph compilation is the likely bottleneck. The assistant had seen this pattern before—models load quickly, then spend significant time compiling CUDA graphs before serving. The 300-second wait was calibrated to this expectation.

All three assumptions would prove incorrect.

What the Results Actually Revealed

The empty curl responses and the absence of any "ready" or "listening" log messages told a different story. The server had not simply stalled during warmup—it had never entered the serving loop at all. The log ended at the server_args dump, which occurs during the initial configuration phase, before the main event loop even starts.

The subsequent messages in the conversation (msg 3151–3154) confirm the deadlock: all 8 scheduler processes show 0% CPU and 0% GPU utilization, each consuming ~83% of system memory (shared across processes). The processes are in a sleeping state (Sl), not actively computing. This is not a slow warmup—it is a complete halt.

Input Knowledge Required

To fully understand this message, the reader needs:

Knowledge of the SGLang server lifecycle: weight loading → model initialization → CUDA graph compilation → serving loop. The assistant is working through this sequence and knows what log messages to expect at each stage.
Understanding of SM120 (Blackwell) architecture: The NVIDIA Blackwell GPU (compute capability 12.0) is a new architecture that requires specific kernel compilation. The assistant had already discovered that sgl-kernel's load_utils.py mapped SM120 to the sm100 directory, and had verified that the built binary actually contained SM120 code via CMake flags.
Awareness of the previous EAGLE-3 deadlock: The base test was explicitly a debugging step after the EAGLE-3 attempt stalled. Without this context, the message seems like a routine startup check.
Familiarity with distributed inference patterns: The use of tensor parallelism (--tp-size 8) across 8 GPUs, the mem-fraction-static parameter, and the scheduler process architecture are all domain-specific concepts.

Output Knowledge Created

This message produces several critical pieces of knowledge:

The base SGLang server deadlocks on SM120, independent of EAGLE-3. This rules out the hypothesis that the problem was specific to speculative decoding. The deadlock is a fundamental compatibility issue between SGLang and the Blackwell architecture.
The deadlock occurs after weight loading but before the serving loop. The model weights are successfully loaded (22 seconds, 64/64 shards), but the server never transitions to the ready state. This narrows the search space to the initialization phase between weight loading and serving.
The deadlock is silent. There are no error messages, no tracebacks, no warnings. The processes simply stop making progress. This makes debugging significantly harder—the assistant must resort to external signals (curl, GPU utilization, process state) rather than log analysis.
SGLang's fast loading is not the full story. The 22-second load time, while impressive, is misleading. It suggests that the weight loading pipeline is well-optimized for SM120, but some subsequent initialization step is fundamentally broken.

The Debugging Strategy Revealed

The assistant's thinking process is visible in the structure of the debugging campaign. The progression follows a clear pattern:

Test the full configuration (EAGLE-3 + base model) → deadlock
Simplify by removing EAGLE-3 → still deadlock
Simplify further by disabling CUDA graphs and enabling verbose logging (msg 3154–3155) → next step This is methodical root-cause isolation. Each step removes a variable while keeping the infrastructure constant. The assistant is building a minimum failing test case. The choice of a 300-second sleep is also revealing. It reflects an expectation of how long CUDA graph compilation should take on this hardware. When that expectation fails, the assistant must recalibrate and consider more fundamental issues—NCCL deadlocks, attention backend incompatibilities, or SM120-specific initialization bugs.

The Broader Significance

This message, though brief, captures a universal experience in ML engineering: the gap between "it loaded" and "it works." Loading model weights is the easy part. The hard part is everything that comes after—CUDA graph compilation, NCCL initialization, attention backend setup, and the countless implicit assumptions that code makes about GPU architecture.

For the SM120 (Blackwell) platform, which was still very new at the time of this session, such compatibility issues are expected. SGLang's codebase, like most ML inference frameworks, was primarily developed and tested on Hopper (SM90) and earlier architectures. The SM120 path was likely exercised only in CI, if at all, and edge cases like 8-GPU tensor parallelism with a 547B parameter model would naturally expose latent bugs.

The assistant's next steps—enabling SGLANG_LOG_LEVEL=DEBUG and --disable-cuda-graph—represent a shift from "is it working?" to "why isn't it working?" The debugging has entered a new phase, one that would ultimately require deep investigation into SGLang's internals on Blackwell hardware.