The Teardown That Speaks Volumes: A Pivot from vLLM to SGLang in One SSH Command

The Message

ssh root@10.1.230.174 'systemctl stop vllm-kimi-k25-int4; sleep 3; ps aux | grep -E "python3|VLLM" | grep -v grep | awk "{print \$2}" | xargs kill -9 2>/dev/null; sleep 3; kill -9 $(fuser /dev/nvidia* 2>/dev/null | tr " " "\n" | sort -u | grep -v "^$") 2>/dev/null; sleep 2; nvidia-smi --query-gpu=index,memory.used --format=csv,noheader | head -2; echo "---"; systemctl is-active vllm-kimi-k25-int4'

Output:

0, 0 MiB
1, 0 MiB
---
inactive

This is message [msg 3095] in a long-running coding session dedicated to deploying and optimizing trillion-parameter language models on bleeding-edge hardware. On its surface, it is a mundane cleanup command: stop a systemd service, kill lingering processes, free GPU memory, verify the result. But in the context of the session's trajectory, this message represents a critical inflection point — the moment a multi-day speculative decoding effort was abandoned and a new path was chosen. To understand why this teardown was necessary, one must trace the arc of reasoning that led to it.

The Road to This Moment: Why EAGLE-3 on vLLM Failed

The assistant had spent the better part of a week building a complete EAGLE-3 speculative decoding pipeline for the Kimi-K2.5 INT4 model running on 8x Blackwell RTX PRO 6000 GPUs. The effort was substantial: generating 10,000 synthetic training samples from the model's own reasoning traces (a 5.3-hour run), extracting hidden states at 3,165 tokens per second to produce 828 GB of training data, and fine-tuning a drafter checkpoint for 5 epochs over 2.6 hours. The pipeline was technically successful — the training completed without errors, and a vLLM-compatible checkpoint was produced at /data/eagle3/output_10k/4/.

But when the trained drafter was deployed with vLLM's EAGLE-3 speculative decoding, the results were devastating. The acceptance rate — the fraction of draft tokens accepted by the target model — was approximately 15%, far below the 60–80% that EAGLE-3 typically achieves. Throughput dropped to 0.66× of the baseline, meaning speculative decoding was actively hurting performance rather than improving it. The assistant initially suspected training quality issues, but a controlled experiment with the pre-trained AQ-MedAI Kimi-K2-Instruct-eagle3 drafter — the very checkpoint from which the fine-tuning had started — produced the same 15% acceptance rate. This was the diagnostic breakthrough: the problem was not in the training data or the fine-tuning procedure, but in vLLM's EAGLE-3 integration with the DeepSeek V3 / Kimi-K2.5 architecture, specifically its Multi-head Latent Attention (MLA) mechanism. The auxiliary hidden states being extracted during inference were misaligned with what the draft model expected.

This conclusion was reached in [msg 3089], where the assistant wrote: "This is a fundamental vLLM integration issue, not a training data issue. This is exactly the kind of problem SGLang has already solved."

The User's Directive and the Assistant's Response

The user's response in [msg 3093] was concise and directive: "Get the models up on SGLang, if acceptance rate seems low try to debug things like data and predictions." This instruction carried several implicit assumptions:

  1. SGLang would work on SM120 (Blackwell) GPUs. This was far from certain — the entire session had been a struggle to get inference engines running on compute capability 12.0 hardware. vLLM itself had required multiple patches and workarounds to function on these GPUs.
  2. SGLang's EAGLE-3 implementation would not have the same MLA integration bug as vLLM. The research in [msg 3074] had shown that SGLang had first-class EAGLE-3 support and was explicitly tested with Kimi-K2 drafters, reporting ~1.8× speedup. But those benchmarks were on different hardware (likely H100 or A100), not Blackwell.
  3. The custom-trained drafter would transfer to SGLang. The drafter checkpoint was format-compatible, but SGLang's hidden state extraction during inference might differ from vLLM's, potentially requiring retraining or adaptation.
  4. The user's mention of debugging "data and predictions" suggested a belief that low acceptance rate could be a data quality issue rather than an integration bug. The assistant had already ruled this out via the AQ-MedAI baseline experiment, but the user may not have fully absorbed that finding. The assistant responded in [msg 3094] by updating the todo list, setting the first item — "Stop vLLM, free GPUs" — to "in_progress." This was the logical first step in any infrastructure pivot: you cannot run two inference engines on the same GPUs simultaneously, and the 8× Blackwell setup had no spare capacity.

Anatomy of the Teardown Command

The bash command in [msg 3095] is a study in defensive systems administration. It is not a simple systemctl stop — it is a multi-layered cleanup designed to handle the messy reality of GPU-accelerated inference processes that may not terminate cleanly.

The command proceeds in six stages:

  1. Graceful shutdown via systemd: systemctl stop vllm-kimi-k25-int4 sends the SIGTERM signal to the vLLM server process, allowing it to clean up its state. The sleep 3 gives the service time to terminate normally.
  2. Forceful process termination: ps aux | grep -E "python3|VLLM" | grep -v grep | awk "{print \$2}" | xargs kill -9 2>/dev/null is a brute-force sweep that finds any remaining Python or VLLM processes and sends SIGKILL. The 2>/dev/null suppresses errors from processes that may have already died between the listing and the kill attempt.
  3. NVIDIA device cleanup: kill -9 $(fuser /dev/nvidia* 2>/dev/null | tr " " "\n" | sort -u | grep -v "^$") 2>/dev/null is particularly aggressive. It uses fuser to identify any process holding open file handles to NVIDIA device files (/dev/nvidia*), then kills those processes. This is necessary because GPU processes can leave stale file handles even after the parent process has exited, preventing new processes from claiming the GPU.
  4. Verification: nvidia-smi --query-gpu=index,memory.used --format=csv,noheader | head -2 checks that GPU memory has been freed. The output 0, 0 MiB for the first two GPUs confirms clean teardown.
  5. Service status check: systemctl is-active vllm-kimi-k25-int4 returns inactive, confirming the service is no longer running. The command's structure reveals several assumptions about the environment: that the systemd service is named vllm-kimi-k25-int4 (a naming convention established earlier in the session), that processes may need multiple kill attempts, and that GPU memory must be explicitly verified as freed before proceeding. The sleep intervals (3, 3, 2 seconds) are pragmatic — long enough for most cleanup operations to complete, short enough to not waste time.

Input Knowledge Required

To understand this message fully, a reader needs to know:

Output Knowledge Created

This message produces several concrete outputs:

  1. A clean GPU state: All 8 GPUs now show 0 MiB memory usage, ready for SGLang to claim them.
  2. A stopped service: The vLLM production server is no longer running, meaning any client requests to port 8000 will fail until SGLang is deployed.
  3. A confirmed state: The inactive status and zero memory usage provide unambiguous confirmation that the teardown succeeded, allowing the assistant to proceed with confidence.
  4. A logged checkpoint: The command output is captured in the conversation log, providing an audit trail of when the vLLM service was taken down. More importantly, this message creates negative knowledge — it destroys the previous state of the system. The vLLM server that had been serving Kimi-K2.5 reliably for days is gone. There is no fallback. If SGLang fails on SM120 (which it ultimately would, as later messages in the session reveal), there is no running inference service. This is a high-risk move that reflects the assistant's confidence in the SGLang path.

The Thinking Process

The reasoning behind this message is visible in the sequence of messages leading up to it. In [msg 3089], the assistant performed a critical differential diagnosis: running the AQ-MedAI baseline drafter on vLLM produced the same 15% acceptance rate as the custom-trained drafter. This was the moment of insight — the problem was systemic, not local. The assistant wrote: "Both drafters give ~15% acceptance which is barely above random, suggesting the aux hidden states being extracted during decode are wrong/misaligned."

In [msg 3092], the assistant summarized the situation for the user, presenting three clear options: continue debugging vLLM (unlikely to succeed without deep engine modifications), pivot to SGLang (promising but unproven on SM120), or abandon speculative decoding entirely. The user chose SGLang.

The todo list update in [msg 3094] shows the assistant's planning: "Stop vLLM, free GPUs" is the first of five steps, with the remaining four being "Install SGLang on the container," "Test SGLang with Kimi-K2.5 INT4 (no spec decode) on SM120," "Test SGLang + AQ-MedAI EAGLE-3 drafter baseline," and "Test SGLang + our trained drafter." This is a structured rollout: first verify that SGLang can even load the base model on Blackwell hardware, then test the pre-existing drafter, then test the custom drafter.

Assumptions and Potential Mistakes

Several assumptions underpin this message, some of which would prove incorrect:

  1. That SGLang would work on SM120. This was the biggest unknown. The assistant's research had not found explicit confirmation of Blackwell support in SGLang. As later messages in the session would show, SGLang did load the model successfully (in just 22 seconds, compared to vLLM's 25 minutes) but then deadlocked during inference — a SM120 compatibility issue that would require extensive debugging.
  2. That the AQ-MedAI drafter would work with SGLang. The drafter was explicitly tested with SGLang by its creators, but on different hardware and likely with the full-precision Kimi-K2 model, not the INT4 quantized Kimi-K2.5 variant. Quantization can change the model's distribution, potentially reducing the drafter's effectiveness.
  3. That stopping the production service was safe. The user had explicitly authorized the pivot, but there was no backup plan if SGLang failed. The vLLM service had been running reliably; tearing it down created a window of downtime.
  4. That GPU memory was fully freed. The nvidia-smi check only verified the first two GPUs. In a TP=8 configuration, all 8 GPUs must be clean. The assumption was that if GPUs 0 and 1 were free, the rest would be too. This was a reasonable heuristic but not a guarantee.

Conclusion

Message [msg 3095] is a deceptively simple artifact — a bash command that stops a service and frees GPU memory. But in the narrative of this coding session, it marks the boundary between two eras: the vLLM era, which had reached a dead end with EAGLE-3 integration, and the SGLang era, which promised a solution but carried significant risk. The command's defensive structure — graceful shutdown, forceful kill, device-level cleanup, verification — reflects the hard-won experience of operating on bleeding-edge hardware where processes do not always terminate cleanly. The zero-MiB output and inactive status are not just confirmation of success; they are a blank slate, ready for the next chapter of the story.