The Runtime Tuning Blitz: Applying 10 Kernel Fixes to an 8-GPU Blackwell Inference Server

Introduction

In the high-stakes world of large language model inference, every microsecond counts. When you're running an 8-GPU NVIDIA RTX PRO 6000 Blackwell system with dual AMD EPYC 9335 (Turin) processors, the difference between peak performance and mediocre throughput often lies not in the model architecture or the serving framework, but in the silent misconfigurations lurking beneath the operating system's surface. Message 1279 of this opencode session represents a pivotal moment: the application of ten runtime kernel and system-level fixes discovered through a massive parallel audit of the entire machine. This message is the execution phase — the moment where diagnosis transforms into action.

Context: The Road to Discovery

To understand why message 1279 exists, we must trace the chain of reasoning that led to it. The session had been wrestling with the GLM-5-NVFP4 model on SGLang, achieving throughput that, while respectable, fell dramatically short of theoretical maximums. A prior computation of the theoretical single-stream maximum (309 tok/s) versus the actual performance (10.36 tok/s) revealed a staggering 3.4% efficiency gap — meaning the system was utilizing only a tiny fraction of its theoretical capability.

This prompted a radical diagnostic strategy: the assistant launched 10 parallel exploration agents ([msg 1259]) to simultaneously investigate every facet of the system configuration. These agents examined UEFI/BIOS settings, LXC container configuration, NVIDIA driver versions, kernel runtime knobs, kernel version compatibility, CUDA memory configuration, PCIe MaxReadReq sizes, power and thermal configuration, NCCL/P2P bandwidth, memory/NUMA allocation, SGLang/Python runtime configuration, and NVLink/NVSwitch status. The result was a comprehensive audit report ([msg 1273]) that identified 16 issues ranked by impact.

The user's response ([msg 1274]) was concise and directive: document the findings in system-improve.md, apply the runtime-only changes, verify P2P with a micro-benchmark, and rerun the inference benchmark. The assistant first wrote the documentation file ([msg 1276]), then took a "before" snapshot of all parameters ([msg 1278]) to establish a baseline. Message 1279 is the culmination of this preparation — the actual application of all runtime fixes.

The Anatomy of the Fix Script

The message contains a single bash script executed over SSH on the Proxmox host at 10.1.2.6. The script is meticulously structured as ten numbered steps, each with clear echo statements for visibility and set -e for error handling. Let us examine each fix and the reasoning behind it.

Fix 1: Loading nvidia_peermem

The first fix attempts to load the nvidia_peermem kernel module, which enables GPUDirect Peer-to-Peer (P2P) memory access. The audit had identified this module as not loaded, and the agent's recommendation was to load it with modprobe. The assumption was that this would enable direct GPU-to-GPU communication without involving system memory, potentially improving allreduce performance.

However, this fix failed with the error "Invalid argument." The assistant's error handling (&& echo "OK" || echo "FAILED") gracefully captured this without aborting the entire script. As the assistant would discover in subsequent messages ([msg 1280], [msg 1283]), nvidia_peermem depends on ib_core — the InfiniBand core module — which is not present on this PCIe-only system. This was a correctable assumption: the audit agent had flagged the missing module as a P0 issue, but deeper investigation revealed it was irrelevant for this hardware configuration. The module exists for GPUDirect RDMA over InfiniBand, not for standard PCIe GPU-to-GPU communication.

Fix 2: PCIe MaxReadReq to 4096

This was perhaps the most impactful hardware-level fix. The audit revealed that all 8 GPUs had their MaxReadReq (Maximum Read Request Size) stuck at 512 bytes instead of the optimal 4096 bytes. This parameter controls how much data a PCIe device can request in a single read transaction. Larger values reduce the number of transactions needed for bulk data transfers, improving bandwidth utilization.

The fix uses setpci — a low-level PCI configuration tool — to write the value 5937 to the CAP_EXP+0x08 register for each GPU and its upstream root port. The value 5937 is a bitwise encoding: it sets both the MaxPayload (bits 0-4, value 7 = 256 bytes, which is the Blackwell hardware limit) and MaxReadReq (bits 5-7, value 3 = 4096 bytes). The assistant correctly targeted both the GPU endpoints and their corresponding root ports, ensuring the entire PCIe path was configured consistently.

The verification in the next message ([msg 1280]) confirmed success: MaxReadReq 4096 bytes on both checked GPUs. This single change could significantly reduce PCIe transaction overhead for model weight loading and tensor parallelism communication.

Fix 3: Disabling NUMA Balancing

kernel.numa_balancing=1 was enabled by default, which causes the kernel to automatically migrate memory pages between NUMA nodes to balance access. For GPU workloads where memory is deliberately allocated on the GPU's local NUMA node, this automatic balancing can cause unnecessary page migrations, degrading performance. The fix disables it with sysctl -w kernel.numa_balancing=0.

Fix 4: Disabling C2 Deep Sleep

CPU C-states represent different levels of power saving. C2 (or "state2" in cpuidle nomenclature) has a wake latency of ~100 microseconds — small for interactive workloads but significant for GPU kernel dispatch latency. When a GPU kernel completes and the CPU must launch the next kernel, deep CPU sleep states add measurable overhead.

The fix iterates over all /sys/devices/system/cpu/cpu*/cpuidle/state2/disable files and writes 1 to disable C2. The script counts successful operations and reports "Disabled C2 on $C2_COUNT CPUs." This is a clever approach because it works regardless of how many CPUs exist (128 threads on this dual EPYC system).

Fix 5-10: The Remaining Sysctl Tweaks

The remaining fixes are standard Linux kernel tuning for high-performance compute workloads:

Assumptions and Their Validity

The assistant made several assumptions in crafting this script:

1. All sysctl parameters exist and are writable. This is generally safe — these are standard Linux kernel parameters present in virtually all modern kernels. The assumption held true.

2. The C2 cpuidle state exists on all CPUs. The script uses 2>/dev/null to silently skip CPUs where state2 doesn't exist. This is robust handling.

3. setpci with value 5937 correctly sets MaxReadReq to 4096. This is technically correct: bits 5-7 of the PCI Express Device Control register encode the MaxReadReq size, and 0x3 (binary 011) corresponds to 4096 bytes. However, this depends on the register layout being standard, which it is for all PCIe devices.

4. nvidia_peermem would load successfully. This was the only incorrect assumption. The module exists in the filesystem (confirmed in [msg 1280] with modinfo) but fails because its dependency ib_core is not loaded. The assistant's error handling prevented this from blocking other fixes.

5. All changes are safe for a production system. Disabling NUMA balancing, C2 sleep, and NMI watchdog are well-understood tuning practices for HPC and GPU compute. None risk system stability.

The Thinking Process Visible in the Message

The message reveals a methodical, production-oriented mindset. The assistant:

  1. Verified the baseline first (in [msg 1278]), confirming all parameters were at their defaults before making changes. This is critical for reproducibility and debugging.
  2. Used set -e to abort on any unexpected failure, but also used || echo "FAILED" for the one fix (nvidia_peermem) that was expected to potentially fail. This shows nuanced error handling — abort on true errors, report but continue on expected failures.
  3. Grouped all changes into a single SSH session rather than making 10 separate connections. This is both efficient and reduces the window for partial application.
  4. Provided immediate feedback by echoing each step with "OK" or "FAILED" labels. The output shows all 8 GPUs and root ports were configured successfully.
  5. Followed up immediately (in [msg 1280]) with verification, checking that each parameter actually changed. This closed the loop between intention and outcome.

Input Knowledge Required

To fully understand this message, one needs:

Output Knowledge Created

The message produced several important outcomes:

  1. 9 of 10 fixes applied successfully, transforming the system from a default-configured state to a tuned state optimized for GPU inference.
  2. PCIe MaxReadReq increased from 512 to 4096 bytes on all 8 GPUs and root ports, potentially improving inter-GPU communication bandwidth.
  3. NUMA balancing disabled, preventing unnecessary page migrations.
  4. CPU C2 deep sleep disabled, reducing GPU kernel dispatch latency.
  5. Network buffers increased 80x (from 212KB to 16MB), preventing NCCL socket bottlenecks.
  6. Profiling enabled via perf_event_paranoid reduction.
  7. A documented negative result: nvidia_peermem cannot load on this system, confirming that GPUDirect RDMA is not available and allreduce will remain CPU-mediated.

The Broader Significance

Message 1279 represents a critical inflection point in the optimization journey. The assistant had spent significant effort diagnosing the system — launching 10 agents, compiling their findings, documenting the action plan. But diagnosis without treatment is merely observation. This message is where the assistant acts on its findings, transforming knowledge into system state changes.

The approach also demonstrates a mature understanding of systems engineering: apply all runtime changes first (no reboot required), verify them, benchmark the impact, and only then consider reboot-requiring changes (kernel upgrade, amd_pstate CPU governor). This incremental, reversible approach minimizes risk while maximizing learning.

The failure of nvidia_peermem is itself a valuable data point. It confirmed that the system lacks InfiniBand support and that GPU-to-GPU communication will rely on standard PCIe transactions through the CPU's root complex. This informed subsequent decisions about allreduce strategy and tensor parallelism configuration.

Conclusion

Message 1279 is a masterclass in systematic system tuning. It takes a comprehensive audit report and translates it into precise, executable actions. The assistant demonstrates deep knowledge of Linux kernel tuning, PCIe configuration, and GPU compute requirements, while maintaining production-grade error handling and verification practices. The one incorrect assumption (about nvidia_peermem) was handled gracefully and turned into a learning opportunity rather than a failure.

For anyone tuning an inference server — whether for Blackwell GPUs, AMD EPYC processors, or any high-performance compute workload — this message serves as a template: audit thoroughly, document findings, apply changes methodically, verify everything, and always have a fallback for expected failures. The 9 successful fixes applied in this single SSH session likely contributed meaningfully to the system's ability to reach its ultimate throughput targets, closing the gap between theoretical potential and practical reality.