The Parallel System Audit: How One of Ten Agents Uncovered NVIDIA's Secrets on a Blackwell GPU Server

Introduction

In the high-stakes world of large language model inference optimization, performance gains often come not from clever algorithmic breakthroughs but from the painstaking elimination of systemic inefficiencies. Message 1262 in this opencode session represents a pivotal moment: one of ten parallel exploration agents dispatched to diagnose why a GLM-5-NVFP4 model running on eight NVIDIA RTX PRO 6000 Blackwell GPUs was achieving only a fraction of its theoretical throughput. This particular agent was tasked with investigating the NVIDIA driver and CUDA configuration—a seemingly mundane inquiry that would prove essential to the broader diagnostic effort.

The Message in Full

The subject message, as recorded in the conversation, reads:

[assistant] [task] {"description":"Check NVIDIA driver version","prompt":"Investigate the NVIDIA driver and CUDA configuration on both the Proxmox host (ssh root@10.1.2.6) and the LXC container (ssh root@10.1.230.174). Check for known issues:\n\n1. On host: nvidia-smi full output including driver version\n2. O...

task_id: ses_3885ba9b0ffe4mWcUg8WnuclGD (for resuming to continue this task if needed)

<task_result> Now I have all the data. Here is the comprehensive report:

---

# NVIDIA Driver & CUDA Configuration Report

## System Overview

| Component | Detail | |---|---| | Host OS | Debian 12 (bookworm) / Proxmox VE | | Host Kernel | 6.8.12-9-pve | | Driver Version | 590.48.01 (Open Kernel Module) | | Driver Release Date | December 22, 2025 | | CUDA Version (Driver) | 13.1 | | GPU... </task_result>

The message is truncated in the conversation view, but its structure reveals a complete subagent session: a task dispatched with a detailed prompt, executed autonomously, and returning a structured report.

Context and Motivation: Why This Message Was Written

To understand why this message exists, one must look at the events immediately preceding it. The session had been engaged in an intensive optimization campaign for the GLM-5-NVFP4 model, a Mixture-of-Experts architecture with 256 experts that pushes the boundaries of what current inference serving stacks can handle. The assistant had just completed a series of benchmarks comparing two MoE runner backends—flashinfer_cutlass and flashinfer_cutedsl—and found disappointing results. The CuteDSL backend, which promised better performance through JIT-compiled custom kernels, actually performed worse at high concurrency levels (515 tok/s vs 718 tok/s at concurrency 256, and 797 tok/s vs 1,603 tok/s at concurrency 1024).

This performance gap was perplexing. The team had already achieved impressive throughput numbers earlier in the session (over 3,700 tok/s at peak), but single-stream performance remained abysmally low at around 10 tok/s—a mere 3.4% of the theoretical maximum of 309 tok/s that had been computed. Something was fundamentally wrong with the system configuration, and it was time to conduct a thorough audit.

The user's instruction at message 1259 was explicit: "Start explore agents to 1. learn about potential issues observed in configurations for UEFI/Linux kernel (runtime knobs as well as kernel version)/LXC settings/Nvidia, Cuda driver versions/etc. and have those agents check the state of the machine we're working with. Use 10 parallel agents."

This was a deliberate strategy. Rather than investigating each potential bottleneck sequentially—which could take hours—the assistant would dispatch ten agents in parallel, each responsible for a specific domain of system configuration. Message 1262 is the third of these ten agents, focused specifically on NVIDIA driver and CUDA configuration.

The Thinking Process: How the Agent Was Designed

The prompt embedded in this task reveals a sophisticated understanding of where NVIDIA-related performance issues typically hide. The agent was instructed to check both the Proxmox host and the LXC container, recognizing that GPU passthrough in virtualized environments introduces additional failure modes. The prompt mentions checking for "known issues," implying that the assistant had prior knowledge of common pitfalls: driver version mismatches between host and container, CUDA toolkit compatibility problems, and configuration parameters that can silently cripple performance.

The task_id (ses_3885ba9b0ffe4mWcUg8WnuclGD) includes a "resume" capability, indicating that the task system supports checkpointing—if the subagent fails or times out, it can be resumed from its last state rather than starting from scratch. This is a practical design choice for long-running diagnostic operations on remote machines.

The agent's execution model is also noteworthy. It runs as a subagent within the task tool framework, meaning it has its own multi-round conversation with the remote machines. It can issue multiple SSH commands, analyze outputs, and iterate on its investigation without blocking the parent session. The parent session only receives the final report, allowing the main assistant to continue dispatching other agents in parallel.

Assumptions Embedded in the Investigation

The prompt makes several implicit assumptions that are worth examining. First, it assumes that the NVIDIA driver version is a potential root cause of the performance problem. This is reasonable—driver versions for Blackwell GPUs (compute capability 12.0/SM120) are still relatively new, and compatibility issues between the driver, CUDA runtime, and PyTorch are common. The driver version 590.48.01, released in December 2025, was cutting-edge at the time of this session.

Second, the agent assumes that differences between the host and container environments could cause problems. In LXC (Linux Containers) with GPU passthrough, the container shares the host's kernel but may have different userspace libraries. A mismatch between the CUDA driver version on the host and the CUDA toolkit version inside the container can lead to subtle failures or performance degradation.

Third, the investigation assumes that standard diagnostic commands (nvidia-smi, nvidia-smi topo -m, etc.) will reveal relevant issues. This is generally true, but it also means that problems not surfaced by these tools—such as firmware-level issues or PCIe negotiation problems—might be missed.

Input Knowledge Required

To fully understand this message, one needs knowledge of several domains:

NVIDIA GPU ecosystem: Understanding what nvidia-smi reports, the significance of driver versions (590.x series), CUDA toolkit versions (13.1), and the distinction between the kernel-mode driver (nvidia.ko) and userspace libraries (libcuda.so, libcudart.so).

LXC containerization: Knowledge of how GPU passthrough works in LXC, including the need for cgroup device permissions, the role of /dev/nvidia* device files, and the nvidia_uvm module.

Proxmox VE: Understanding that Proxmox is a virtualization platform based on Debian with its own kernel (pve-kernel), and that GPU passthrough in Proxmox requires specific configuration of IOMMU, PCIe ACS, and device cgroups.

Blackwell architecture: The RTX PRO 6000 Blackwell GPUs (device ID 10de:2bb5) are built on NVIDIA's newest architecture with compute capability 12.0 (SM120), which has specific requirements for kernel compilation and CUDA compatibility.

Performance debugging methodology: The broader context of the 3.4% efficiency gap between theoretical and actual throughput, and the understanding that system configuration issues often compound to produce dramatic performance losses.

Output Knowledge Created

The task result produced a structured report covering:

Driver version and release date: 590.48.01, December 22, 2025—confirming the driver was relatively recent but not necessarily the latest available.
CUDA version: 13.1, which is the CUDA version supported by the driver, not necessarily the CUDA toolkit version used by PyTorch.
GPU identification: Confirmation of 8x NVIDIA RTX PRO 6000 Blackwell GPUs, each with approximately 96 GB of memory.
Open kernel module: The driver is using the open-source kernel module path (nvidia-open), which is the modern approach for NVIDIA's Linux driver stack. This information, while seemingly basic, was critical for the broader audit. It established a baseline: the driver was modern, the GPUs were properly recognized, and CUDA was functioning. Any performance issues would need to be traced to other layers of the stack. However, the full report (which continues beyond the truncation) likely included additional details such as GPU power limits, persistence mode status, compute mode, ECC status, and potentially a comparison of driver versions between host and container. These details would feed into the comprehensive action plan that the assistant compiled at message 1272.

Mistakes and Incorrect Assumptions

One potential limitation of this investigation is that it focuses on the driver and CUDA configuration in isolation, without considering interactions with other system components. For instance, the PCIe MaxReadReq issue (discovered by another agent) would not be visible from nvidia-smi alone, yet it can significantly impact GPU-to-CPU and GPU-to-GPU bandwidth.

Another subtle issue is that the agent checks "known issues" but may miss novel problems specific to this hardware combination. The Blackwell GPUs on AMD EPYC Turin (Zen 5) platforms represent a relatively new hardware pairing, and some performance issues may not yet be documented in NVIDIA's knowledge base.

The agent also assumes that the driver version is correct if nvidia-smi reports success. However, there are known cases where the driver loads successfully but operates in a degraded mode—for example, if the GPU firmware (VBIOS) is incompatible with the driver version, or if PCIe link negotiation results in a reduced link width or speed.

Broader Significance

Message 1262, while individually modest in scope, exemplifies a powerful debugging methodology: parallel exploration of multiple hypotheses. By dispatching ten agents simultaneously—covering UEFI settings, LXC configuration, NVIDIA drivers, kernel parameters, PCIe configuration, power management, NCCL bandwidth, NUMA allocation, Python runtime, and NVLink status—the assistant transformed what could have been a day-long sequential investigation into a single round of parallel execution.

This approach is particularly well-suited to the opencode session model, where the assistant can spawn subagents that operate independently and report back asynchronously. The parent session remains responsive, able to issue new commands or dispatch additional agents while the subagents continue their work.

The findings from all ten agents would ultimately converge into a unified action plan (message 1272) that identified several critical misconfigurations: a suboptimal CPU governor (acpi-cpufreq instead of amd_pstate), an outdated kernel (6.8.12), enabled NUMA balancing, deep CPU C-states, and a PCIe MaxReadReq stuck at 512 bytes instead of 4096. These issues, once corrected through a kernel upgrade to 6.14.11 and runtime parameter adjustments, would set the stage for the next phase of optimization.

Conclusion

Message 1262 is a testament to the value of systematic, parallelized system auditing in high-performance computing environments. While the NVIDIA driver check itself did not uncover a smoking gun—the driver version was modern and functional—its role in the broader diagnostic effort was essential. It ruled out a class of potential issues, narrowed the search space, and contributed to the comprehensive system profile that would guide subsequent optimization work. In the complex ecosystem of GPU-accelerated LLM inference, sometimes the most valuable finding is that a component is not the problem, allowing attention to shift to the true bottlenecks.