The Silent Deadlock: Debugging SGLang on SM120 After the EAGLE-3 Pivot

The Message

0% CPU and 0% GPU — the process is idle/stuck. Something went wrong silently. Let me check if maybe it crashed or the port is occupied:

>

``bash ssh root@10.1.230.174 'ss -tlnp | grep 8000; echo "---"; curl -v http://localhost:8000/ 2>&1 | head -10' ``

>

``` --- Host localhost:8000 was resolved. IPv6: ::1 * IPv4: 127.0.0.1 % Total % Received % Xferd Average Speed Time Time Time Current Dload Upload Total Spent Left Speed

>

0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0 Trying [::1]:8000... connect to ::1 port 8000 from ::1 port 34718 failed: Connection refused Trying 127.0.0.1:8000... connect to 127.0.0.1 port 8000 from 127.0.0.1 port 55840 failed... ```

Context: The Long Road to EAGLE-3

This message represents a moment of reckoning after an arduous multi-day engineering effort. To understand its significance, one must trace the path that led here. The team had been running a comprehensive profiling campaign on the Kimi-K2.5 INT4 model deployed across 8x Blackwell RTX PRO 6000 GPUs. The dominant bottleneck was AllReduce, consuming 51.5% of decode time. With PCIe-based communication limiting further gains from Expert Parallelism, the team turned to speculative decoding as a software-only optimization path.

The EAGLE-3 training pipeline had been built from scratch over several sessions. The team wrote synthetic data generation scripts, captured 10,000 inference samples from the Kimi-K2.5 model's actual reasoning outputs, extracted hidden states at 3,165 tok/s producing 828 GB of training data, and fine-tuned a drafter from the AQ-MedAI checkpoint in 2.6 hours. It was a complete, end-to-end pipeline — a significant engineering achievement.

But then came the crash. When testing the trained drafter with vLLM's EAGLE-3 integration, both the newly trained drafter and the pre-trained AQ-MedAI baseline achieved only ~15% acceptance rate, resulting in 0.66x throughput — worse than running without speculation at all. This was not a training quality problem; it was a fundamental integration issue with vLLM's handling of Multi-Head Latent Attention (MLA) during hidden state extraction in decode.

The user made a decisive call: pivot to SGLang, which has first-class EAGLE-3 support and is explicitly tested with Kimi-K2 drafters. The assistant built sgl-kernel for SM120 (a 48-minute compile), verified it worked, and launched the server. SGLang loaded the 547GB model in just 22 seconds — compared to vLLM's 25 minutes. It was a stunning improvement.

And then... silence.## The Silent Deadlock

The subject message captures the moment the assistant realizes something has gone terribly wrong. After waiting through the 22-second weight load, after waiting through what should have been CUDA graph warmup, after checking GPU utilization and finding it at 0% — the assistant arrives at a stark diagnosis: "0% CPU and 0% GPU — the process is idle/stuck. Something went wrong silently."

This is one of the most frustrating classes of bugs in distributed systems: the silent failure. The process hasn't crashed — there's no segfault, no traceback, no error message in the log. The Python process is still alive, consuming 226 GB of memory across the TP workers. But it's doing nothing. The server port (8000) isn't listening. The curl command gets "Connection refused." The process is in a state of suspended animation.

The assistant's debugging approach is methodical. First, it checks whether the port is occupied using ss -tlnp | grep 8000 — the standard Linux socket statistics command. The empty output confirms no process is listening on port 8000. Then it attempts an HTTP connection with curl -v, which provides verbose output showing the connection attempt to both IPv6 (::1) and IPv4 (127.0.0.1) addresses, both failing with "Connection refused." This rules out a simple network configuration issue or firewall problem — the server simply never started listening.

The Reasoning Behind the Checks

The assistant's thinking process is visible in the sequence of diagnostic commands. In the messages leading up to this one, the assistant had already checked:

  1. Process existence: ps aux | grep sglang confirmed the Python process was still running.
  2. CPU utilization: 0.0% — the process wasn't computing anything.
  3. GPU utilization: 0% across all 8 GPUs — no CUDA kernels were executing.
  4. GPU memory: 76 GB per GPU — the model weights were loaded, so weight loading succeeded. These checks narrowed the problem space considerably. The model loaded successfully (memory allocation confirms this), but something after weight loading — likely CUDA graph compilation, drafter model initialization, or the server startup sequence — had deadlocked. The assistant's next hypothesis, expressed in this message, is that the process "crashed or the port is occupied." The port check is a clever diagnostic: if the process had crashed after partially starting, there might be a lingering socket or the port might be held by another process. But ss -tlnp shows nothing on port 8000, and the connection attempt fails cleanly.

What Went Wrong: A Deeper Analysis

The SGLang server was launched with an ambitious configuration: tensor parallelism of 8 across all GPUs, EAGLE-3 speculative decoding with the AQ-MedAI drafter, and a memory fraction of 0.90. The log showed clean weight loading — 64 safetensor shards loaded in 34 seconds with no errors. But then the process went dark.

Several hypotheses could explain this deadlock:

CUDA Graph Compilation on SM120: The Blackwell architecture (compute capability 12.0) is new, and CUDA graph compilation — which SGLang performs during warmup — may hit an infinite loop, a driver deadlock, or an unsupported operation. The sgl-kernel was built with SM120 support, but the CUDA graph capture mechanism may not be fully compatible with Blackwell's architecture. CUDA graphs involve capturing kernel launches and then replaying them, and if any captured kernel uses an instruction or feature not properly supported, the capture can hang.

Drafter Model Initialization Deadlock: The EAGLE-3 drafter model must be loaded and initialized alongside the base model. If the drafter's weight loading or its integration with the base model's attention mechanism (especially with MLA) encounters a synchronization issue across the 8 TP workers, a collective operation (like NCCL all-reduce or broadcast) could hang indefinitely. This would manifest as exactly what we see: processes alive, memory allocated, but zero computation.

NCCL/Communication Deadlock: With 8 GPUs connected via PCIe, NCCL uses NVLink for GPU-to-GPU communication. If the NCCL initialization during warmup encounters a topology discovery issue or a peer-to-peer connection failure, the entire initialization can block. The earlier profiling had identified PCIe AllReduce as a bottleneck, and NCCL tuning experiments had been conducted — but tuning was done for vLLM, not SGLang's communication patterns.

Python-Level Deadlock: The SGLang server spawns multiple worker processes for tensor parallelism. If there's a threading or multiprocessing issue — a lock that's never released, a queue that's never drained, or a barrier that's never crossed — the Python process can appear alive (from the OS perspective) while being completely frozen at the application level.## Assumptions Made and Their Consequences

The assistant made several assumptions in this message that are worth examining:

  1. That the process is truly stuck, not just slow: The assistant assumed that 0% GPU utilization after several minutes of waiting indicated a deadlock rather than slow initialization. This was a reasonable assumption — CUDA graph compilation typically shows some GPU activity, even if at low utilization. Zero GPU utilization for an extended period is a strong signal of a hang.
  2. That the server should be listening on port 8000: The assistant assumed that a successful weight load would be followed by server startup. In SGLang's architecture, the HTTP server starts listening only after all initialization is complete — including CUDA graph warmup, drafter loading, and NCCL initialization. The "Connection refused" error confirmed that the server never reached the listening state.
  3. That the process hadn't crashed: The assistant checked ps aux and found the process alive. This ruled out a segfault or OOM kill, but it didn't rule out a Python-level deadlock where the process is alive but frozen. The distinction matters for debugging strategy: a crashed process would leave core dumps or error logs; a deadlocked process requires different diagnostic tools (like GDB attach, strace, or Python thread dump).
  4. That the log would contain error information: The assistant had checked the log for errors and found only harmless import warnings. This assumption — that errors would be logged — is generally valid, but deadlocks often produce no log output because the code path that would log the error is itself blocked.

Input Knowledge Required

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

Output Knowledge Created

This message creates several pieces of actionable knowledge:

  1. A confirmed bug: SGLang with EAGLE-3 on SM120 deadlocks after weight loading. This is a reproducible issue that can be reported to the SGLang developers.
  2. A diagnostic procedure: The sequence of checks (process alive → CPU 0% → GPU 0% → port not listening → connection refused) establishes a pattern for diagnosing silent deadlocks in distributed ML serving systems.
  3. A narrowed problem space: The deadlock occurs after weight loading but before the server starts listening, which narrows the search to CUDA graph compilation, drafter initialization, or NCCL warmup.
  4. A benchmark for future attempts: SGLang loads the 547GB model in 22 seconds on SM120 — a data point that confirms SGLang's weight loading is dramatically faster than vLLM's (25 minutes) on this architecture.

The Broader Significance

This message sits at a critical inflection point in the larger narrative. The team had invested enormous effort in the EAGLE-3 pipeline — writing training code, generating synthetic data, extracting hidden states, fine-tuning drafters — only to hit a wall with vLLM's 15% acceptance rate. The pivot to SGLang represented hope: a path forward with first-class EAGLE-3 support. The 22-second weight load was exhilarating. And then the deadlock.

The silence of the deadlocked process mirrors a deeper silence in the engineering process: the gap between what should work in theory and what actually works in practice. SGLang's documentation says it supports EAGLE-3 with Kimi-K2 drafters. The sgl-kernel compiled successfully for SM120. The model loaded in record time. But somewhere in the initialization sequence, on the actual Blackwell hardware, something breaks silently.

This is the reality of working at the frontier of ML infrastructure. The software stack is a tower of dependencies — PyTorch, CUDA, NCCL, SGLang, sgl-kernel, transformer libraries, custom kernels — each with its own compatibility matrix. When a new GPU architecture like Blackwell SM120 arrives, every layer of the stack must be validated. The 22-second load time proved that the basic weight loading path works. But the deadlock proved that the warmup and initialization path does not.

The debugging continues from this point. The assistant will need to try different approaches: disabling CUDA graphs, enabling verbose logging, running with a single GPU, testing without EAGLE-3, or even building a debug build of SGLang. Each attempt will narrow the problem further. But this message captures the moment of first discovery — the moment when the silence of the deadlocked process speaks louder than any error message could.

Conclusion

The subject message is a masterclass in systematic debugging under uncertainty. Faced with a process that is alive but unresponsive, consuming memory but not computing, the assistant methodically eliminates possibilities: is it crashed? (No — ps shows it running.) Is it computing? (No — 0% CPU and GPU.) Is it serving? (No — port 8000 isn't listening.) Is the port occupied by another process? (No — ss shows nothing.)

Each check is a hypothesis test, and each negative result narrows the space of possible explanations. The final diagnosis — "Something went wrong silently" — is both a conclusion and a call to action. The silence itself becomes the clue. In distributed systems, the absence of an error is not the same as the absence of a problem. Sometimes the most informative signal is the one that never arrives.