The Stubborn 96,619 MiB: A Diagnostic Pivot in Deploying Kimi-K2.5-NVFP4

Message at a Glance

In a single bash command executed over SSH, the assistant checks the GPU memory usage and running processes on a remote machine hosting eight NVIDIA RTX PRO 6000 Blackwell GPUs. The command and its output are deceptively simple:

ssh root@10.1.230.174 'nvidia-smi --query-gpu=index,memory.used --format=csv,noheader; ps aux | grep -E "python|vllm" | grep -v grep | grep -v networkd | grep -v unattended'
0, 96619 MiB
1, 96619 MiB
2, 96619 MiB
3, 96619 MiB
4, 96619 MiB
5, 96619 MiB
6, 96619 MiB
7, 96619 MiB
root      212436  0.0  0.0  18412 11704 ?        S    22:01   0:00 /root/ml-env/bin/python3 -c from multiprocessing.resource_tracker import main;main(93)

Every GPU reports exactly 96,619 MiB of used memory — approximately 94.4 GiB out of 96 GiB total, meaning the 540 GB Kimi-K2.5-NVFP4 model weights remain fully loaded across all eight devices. Only a single Python process remains: a multiprocessing resource tracker (PID 212436), a ghost of the vLLM server that was supposed to have been killed. This message captures the moment when the assistant realizes that its cleanup attempt has failed and must dig deeper.

Context: The Deployment Arc

To understand why this message matters, we must trace the arc of the deployment. The assistant has been working for hours to bring up a production-grade inference server for nvidia/Kimi-K2.5-NVFP4, a 1-trillion-parameter Mixture-of-Experts model (based on the DeepSeek V3 architecture) quantized by NVIDIA using NVFP4. The hardware is formidable: eight RTX PRO 6000 Blackwell GPUs (each with 96 GB VRAM), connected via PCIe.

The journey to this moment included:

1. Downloading 540 GB of model weights across 119 safetensor shards.
2. Resolving an FP8 KV cache blocker — the model checkpoint shipped with FP8 KV cache configuration, but no MLA attention backend on SM120 (Blackwell) supports FP8 KV cache. The assistant removed kv_cache_quant_algo from the config files, forcing a fallback to fp16 KV cache.
3. Successfully loading the model and achieving ~60 tok/s single-request throughput.
4. Creating a systemd service (vllm-kimi-k25.service) to manage the server as a production daemon.
5. Debugging shell escaping issues in the service file's ExecStartPre directive, where $free needed to be $$free for systemd.
6. Attempting to start the service, which failed because leftover vLLM processes from the manual launch still occupied the GPUs. The message immediately preceding this one ([msg 2157]) was the assistant's attempt to clean the slate:

ssh root@10.1.230.174 'pkill -9 -f "python3.*vllm"; pkill -9 -f "run_kimi"; sleep 5; nvidia-smi --query-gpu=index,memory.used --format=csv,noheader; rm -f /dev/shm/psm_* /dev/shm/sem.mp-*'

The assistant assumed this would free the GPUs. Message 2158 is the verification of that assumption — and it reveals the assumption was wrong.

What the Message Reveals

The output delivers two pieces of information, and the tension between them tells the story.

First, the GPU memory numbers. All eight GPUs report 96,619 MiB used. This is the same value reported before the kill attempt (see [msg 2156]). The model weights have not been released. This is deeply problematic: the systemd service's ExecStartPre script waits for GPU memory to drop below 1,000 MiB before proceeding, so as long as the GPUs are full, the service cannot start. The assistant is stuck in a loop: kill processes, check memory, find it unchanged, try again.

Second, the process list. Only one Python process remains: PID 212436, running the multiprocessing resource tracker. This is a standard Python mechanism for managing shared memory segments across worker processes. When vLLM starts, it spawns multiple worker processes (one per GPU, plus engine cores) using Python's multiprocessing module. Each worker process communicates with the resource tracker, which coordinates the cleanup of shared memory segments. When the assistant ran pkill -9 -f "python3.*vllm", it killed the vLLM worker processes and the main server process, but it did not kill the resource tracker. The resource tracker survived because its command line (python3 -c from multiprocessing.resource_tracker import main;main(93)) does not match the pattern python3.*vllm.

Why the Resource Tracker Matters

The resource tracker is not just an idle process — it holds references to CUDA memory allocations. When vLLM workers are killed with SIGKILL (signal 9), they cannot run their cleanup handlers. The CUDA driver, seeing that the worker processes have vanished, should eventually release their memory. However, the resource tracker may still hold file descriptors or shared memory handles that keep the CUDA context alive. This is a well-known issue in GPU computing: abruptly killing CUDA processes can leave GPU memory in a stale state, especially when using NCCL and multiprocessing.

The assistant's next action ([msg 2159]) confirms this diagnosis: it kills the resource tracker explicitly with kill -9 212436, then waits 10 seconds. But even that does not immediately free the memory — the GPUs remain at 96,619 MiB. The assistant must then wait longer or investigate further.

Assumptions and Their Failure

This message exposes several assumptions the assistant made:

1. That pkill -9 would be sufficient. The assistant assumed that killing all processes matching python3.*vllm would release GPU memory. It did not account for the resource tracker, which uses a different process name pattern.
2. That GPU memory would free quickly. The sleep 5 in the previous command assumed five seconds would be enough for the CUDA driver to clean up. In practice, CUDA memory cleanup after a forced kill can take tens of seconds or require explicit intervention.
3. That the resource tracker would be cleaned up automatically. The assistant did not anticipate that a background Python process with no visible connection to vLLM could hold GPU memory hostage.
4. That the systemd service could be started cleanly after a manual launch. The assistant expected that killing the manual process and starting the service would be a simple transition. Instead, the stale memory made the ExecStartPre check fail repeatedly.

Input Knowledge Required

To understand this message, the reader needs to know:

• How vLLM uses multiprocessing. vLLM spawns separate worker processes for tensor parallelism, each holding a shard of the model weights. These workers communicate via NCCL and share GPU memory.
• The role of Python's multiprocessing resource tracker. This is a background process that manages shared memory segments. It is created automatically when multiprocessing is used and persists until all worker processes have exited cleanly.
• How CUDA memory management works. GPU memory is allocated per process context. When a process is killed, its CUDA context is destroyed, but cleanup is not instantaneous and can be blocked by orphaned processes holding file descriptors.
• The systemd service lifecycle. The ExecStartPre directive runs a command before starting the main service. If it fails (e.g., because GPU memory is still in use), the service does not start.
• The hardware constraints. Each GPU has 96 GB of VRAM, and the model weights consume ~94.4 GiB per GPU. There is almost no headroom, so any leftover memory blocks the service.

Output Knowledge Created

This message creates critical diagnostic knowledge:

• The exact GPU memory state after a kill attempt: all eight GPUs at 96,619 MiB, unchanged.
• The identity of the surviving process: PID 212436, the multiprocessing resource tracker.
• The failure mode of the cleanup strategy: pkill -9 with pattern matching does not catch all Python processes, and the resource tracker is a hidden dependency.
• The need for a more aggressive cleanup procedure: subsequent messages show the assistant evolving its approach, eventually adding explicit resource tracker kills and longer wait times.

The Thinking Process

The assistant's reasoning is visible in the structure of the command itself. It combines two diagnostics into a single SSH call: GPU memory via nvidia-smi and process listing via ps. This is efficient — one connection, two data points — and reveals the assistant's mental model: it expects to see either (a) zero GPU memory and no processes (clean slate) or (b) some GPU memory and some processes (need to kill more). What it actually sees is (c): full GPU memory and one zombie process. This is an unexpected state that forces the assistant to refine its understanding.

The choice of ps filters is also revealing. The assistant explicitly excludes grep, networkd, and unattended from the results — these are common system processes that might match the python pattern but are unrelated to vLLM. This shows the assistant has experience with noisy process listings on Ubuntu systems and knows how to get a clean view.

The fact that the assistant runs this as a single bash command (not two separate commands) suggests it wants an atomic snapshot: the GPU state and process state at the same instant. If it ran them separately, the state could change between calls.

Broader Significance

This message is a microcosm of a larger challenge in deploying large language models on multi-GPU systems: the gap between "the process is killed" and "the GPU is free." In production deployments, this gap is often bridged by careful service lifecycle management — using ExecStartPre checks, graceful shutdown signals (SIGTERM before SIGKILL), and timeout-based retry loops. The assistant is learning this lesson in real time, debugging not the model or the inference code but the operational plumbing of systemd service management.

The 96,619 MiB number is itself significant. It is not a round number — 96,619 MiB is 94.4 GiB, leaving only ~1.6 GiB free per GPU. This tight margin means any leftover allocation, no matter how small, can block the service. The resource tracker itself uses only 18 MB of system memory (RSS: 11,704 KB), but it holds the CUDA context open, preventing memory release.

In the broader narrative of this coding session, message 2158 marks the transition from "the model works" to "the service works." The assistant has already proven that Kimi-K2.5-NVFP4 can run at 60 tok/s with coherent output. Now it must make that deployment reliable and restartable — a task that requires understanding not just the model architecture but the operating system's process model, CUDA's memory management, and systemd's service semantics. This message is the diagnostic that reveals the gap between those layers.