The Systematic Kernel Audit: Uncovering Hidden Bottlenecks in an 8-GPU Blackwell ML Server

Introduction

In the high-stakes world of large-scale machine learning inference, the gap between theoretical peak performance and real-world throughput can be vast — and the reasons are often hidden in the deepest layers of the operating system. This article chronicles a comprehensive kernel runtime parameter audit conducted on a Proxmox host equipped with dual AMD EPYC Turin processors and eight NVIDIA RTX PRO 6000 Blackwell GPUs, as part of a larger effort to deploy the GLM-5-NVFP4 model using SGLang. The audit, spanning 14 messages in an opencode coding session, systematically examined over 40 kernel parameters, CPU frequency governors, PCIe configuration settings, GPU driver states, and memory management policies. What emerged was a portrait of a system running at kernel defaults — defaults that, while perfectly adequate for general-purpose computing, were silently throttling GPU compute performance by a staggering margin.

The session that produced this audit was born from a performance crisis. The team had computed the theoretical maximum single-stream throughput for the GLM-5-NVFP4 model at 309 tokens per second, only to discover that actual performance was a mere 10.36 tok/s — an efficiency gap of just 3.4%. This 30x shortfall demanded a root-cause investigation, and the kernel audit was the first systematic step toward understanding why.

The Architecture of the Audit

The audit was initiated by a user request at <msg id=0> that was remarkable for its thoroughness. The user provided a 20-point checklist of kernel runtime parameters to examine, spanning CPU frequency governance, C-states, scheduler tuning, virtual memory parameters, network buffers, hugepages, interrupt affinity, and PCIe configuration. But the checklist was only the starting point. The user's final instruction — "Research what optimal settings should be for AMD EPYC Turin + NVIDIA Blackwell GPU compute workloads. Compare with what's found. Report ALL suboptimal settings" — transformed the exercise from data collection into a genuine diagnostic investigation [6].

The assistant responded by executing the checks in careful, logically grouped batches across messages 1 through 13. This batching strategy was itself a design decision of consequence. Rather than dumping all parameters at once, the assistant organized the probes by subsystem: CPU frequency and power management in message 1, C-states and idle drivers in message 2, block device schedulers and VM parameters in message 3, scheduler tuning and network parameters in message 4, and so on through PCIe configuration, GPU interrupts, kernel command-line parameters, and GPU driver state [5][15][10].

This systematic decomposition reflects a mental model of kernel performance where parameters are organized by function rather than by the order they appear in a checklist. The progression from CPU-centric parameters to memory, I/O, PCIe, and finally GPU-specific settings mirrors the classic performance analysis workflow: understand the compute units first, then the memory subsystem, then the interconnect, and finally the accelerators.

The CPU Frequency Discovery

The first batch of checks, executed in message 1, revealed a critical finding about CPU frequency management. The scaling driver was acpi-cpufreq rather than amd_pstate — a significant deviation from optimal configuration for an AMD EPYC Turin processor. The amd_pstate driver is purpose-built to leverage the Collaborative Processor Performance Control (CPPC2) feature of modern AMD processors, providing finer-grained frequency control and faster response than the generic ACPI-based driver [5].

The frequency numbers themselves told a troubling story. The scaling maximum frequency was 3.0 GHz, while the hardware maximum was 4.42 GHz — a 32% gap that suggested either a BIOS-imposed power limit or a kernel-level cap preventing the CPU from reaching its full potential. For GPU compute workloads, where the CPU must quickly service GPU interrupts, launch kernels, and manage data transfers, every megahertz of CPU frequency matters. The performance governor was correctly set, but the underlying driver and frequency limits were leaving performance on the table [5].

C-States: The 100-Microsecond Penalty

Message 2 probed the system's C-state configuration, revealing a subtle but impactful bottleneck. The system had three idle states available: POLL (0μs latency), C1 (1μs latency), and C2 (100μs latency). All states were enabled, meaning the kernel's menu idle governor was free to choose any state — including C2 with its 100μs wake penalty [10].

For GPU compute workloads, this is a critical finding. When a GPU kernel completes or a DMA transfer finishes, the GPU raises an interrupt that must be serviced by a CPU. If that CPU is in C2, the 100μs wake latency adds directly to the interrupt handling time. For workloads that launch thousands of small GPU operations per second, the cumulative overhead can be substantial. The assistant correctly identified this as a MEDIUM-HIGH priority issue, recommending either disabling C2 entirely via processor.max_cstate=1 on the kernel command line or disabling it at runtime [10].

The PCIe MaxReadReq Bottleneck

One of the most technically interesting findings emerged from message 7, which probed the PCIe configuration of all eight GPUs. The command checked MaxReadReq (Maximum Read Request Size), link speed/width, and MaxPayload for each GPU, using lspci -vvv to extract the relevant PCIe capability registers [13].

The output was uniform across all eight GPUs: MaxReadReq was 512 bytes, MaxPayload was 256 bytes, and the link was running at 32 GT/s x16 (PCIe Gen5). The link speed was optimal, but the MaxReadReq was severely suboptimal. The PCIe specification allows MaxReadReq up to 4096 bytes, and for GPU compute workloads — where large DMA transfers between GPU memory and system memory are the norm — a larger read request size directly translates to higher effective bandwidth and lower latency [13].

The assistant's command construction revealed deep PCIe domain knowledge. The use of lspci -vvv (triple verbose) was essential because MaxReadReq and MaxPayload only appear in the "Capabilities" section of the PCIe configuration space, which requires maximum verbosity. The assistant also checked all eight GPUs independently, ensuring that any per-slot variation would be detected. The uniformity of the finding — all eight GPUs at 512 bytes — pointed to a system-wide BIOS default rather than a per-slot anomaly [13].

Beyond the Checklist: Proactive Investigation

A hallmark of this audit was the assistant's willingness to go beyond the user's prescribed checklist. Throughout messages 8 through 13, the assistant added probes that the user had not requested but that any experienced systems engineer would recognize as essential [9].

Message 8 checked the kernel command line, IOMMU status, ACS override, GPU persistence mode, power limits, and clocks. The kernel command line revealed amd_iommu=on iommu=pt — a good configuration for GPU compute, as pass-through mode reduces IOMMU translation overhead. The ACS (Access Control Services) check was particularly relevant for multi-GPU systems, where ACS can interfere with peer-to-peer GPU communication [9].

Message 10 probed PCIe Access Control Services, Active State Power Management (ASPM), and Relaxed Ordering. The assistant found that ASPM was disabled — optimal for compute workloads — and that Relaxed Ordering was enabled, which allows PCIe write transactions to bypass pending reads, reducing DMA latency [12].

Message 11 extended the investigation into kernel compile-time configuration, checking parameters that are invisible to runtime inspection tools: the kernel tick frequency (HZ), tickless operation (NO_HZ), preemption model, and RCU callback offloading. These parameters are baked into the kernel at build time and can be equally decisive for performance as any runtime sysctl [2].

Message 12 pivoted from system-level checks to GPU-specific configuration, querying compute mode, ECC status, clock policy, Fabric Manager status, and peer memory modules. The most consequential finding was that ECC was enabled on all eight GPUs. ECC consumes approximately 12.5% of memory bandwidth and reduces usable memory capacity — a significant performance tax for inference workloads where transient bit errors rarely affect output quality [4].

Message 13 served as the final data-gathering pass, checking PCIe Advanced Error Reporting (AER), ZFS ARC statistics, file descriptor limits, and IO scheduler parameters. The PCIe AER check confirmed that the fabric was healthy (no correctable or uncorrectable errors), while the file descriptor check revealed a soft limit of 1024 — a classic hidden bottleneck for ML frameworks that open many file handles [14].

The GPU Interrupt Landscape

Messages 6 and 9 focused on GPU interrupt handling, a domain that is often overlooked but can be a first-order performance concern for latency-sensitive workloads. The assistant probed /proc/interrupts for NVIDIA GPU interrupts, checking their distribution across CPU cores and their NUMA affinity [3][1].

The output revealed a nuanced picture. GPU interrupts were distributed across both NUMA nodes, with some IRQs restricted to specific node's CPU ranges. However, the effective affinities showed interrupts landing on scattered cores rather than being concentrated on the cores closest to each GPU's PCIe slot. The irqbalance daemon was running, which could dynamically move interrupts away from optimal cores [3].

The assistant also checked the NMI watchdog (kernel.nmi_watchdog), which can introduce periodic interrupt noise that perturbs tightly synchronized GPU operations. The energy performance bias and AMD pstate driver presence were also verified, confirming that the AMD-specific frequency driver was not loaded [1].

The Comprehensive Report

All of these findings culminated in message 14, a comprehensive audit report that synthesized data from the 13 preceding tool-call messages into a structured document with three sections: Optimal Settings (25 items), Suboptimal Settings (15 items), and a Priority Summary mapping fixes to P0-P3 priority levels [8].

The report's structure revealed a clear reasoning process: verify the baseline, identify deviations from best practices, assess the severity of each deviation, and provide actionable remediation steps. The three P0 (critical) items were: switching to amd_pstate=active, loading nvidia_peermem, and investigating NVLink/Fabric Manager. The P1 items included disabling NUMA balancing, reducing dirty ratios, and disabling deep C-states. The P2 items covered increasing MaxReadReq, raising file descriptor limits, and disabling the NMI watchdog. The P3 items were lower-impact optimizations like disabling scheduler autogrouping and adjusting zone reclaim mode [8].

The report also documented what was already optimal: the CPU governor was set to performance, PCIe link speed was 32 GT/s x16 (Gen5), GPU power limits were at the maximum 600W, no swap was configured, and the IOMMU was in pass-through mode. These positive findings were important for establishing confidence in the system's foundation and focusing attention on the genuine issues [8].

Assumptions and Blind Spots

For all its thoroughness, the audit operated under several assumptions that deserve examination. The most significant was the assumption that the Proxmox host kernel (6.8.12-9-pve) supported amd_pstate — a reasonable assumption for a modern kernel, but one that went unverified until the kernel upgrade later in the session [8].

The PCIe MaxReadReq check only examined the GPU endpoint, not the upstream root port. The values shown by lspci on the GPU are the device's capabilities, but the actual negotiated values may be lower if the root complex does not support the same maximums. The assistant assumed the displayed values were the active configuration, which is usually correct but not guaranteed [13].

The block device scheduler check in message 3 captured loop devices and NBD devices but did not clearly identify physical storage. The system uses ZFS as its root filesystem, which has its own I/O scheduling that operates below the block layer. The kernel block device scheduler may have limited effect on ZFS-backed storage, a nuance the assistant did not fully explore [15].

The interrupt analysis checked counts at a moment when the GPUs were idle, showing zeros across all CPU cores. The assistant did not acknowledge this caveat, and the output could be misinterpreted as indicating a problem when it simply meant no GPU workload was running [3].

The Thinking Process

Throughout the audit, the assistant's thinking process was encoded not in explicit reasoning blocks but in the structure and sequencing of the commands themselves. The layered approach — starting with CPU parameters, then memory, then I/O, then PCIe, then GPU-specific settings — reflected a mental model of system performance where each layer depends on the one below it.

The decision to batch related checks into single SSH commands minimized network round-trips and ensured consistent snapshots of system state. The use of 2>/dev/null throughout indicated awareness that some kernel interfaces might not exist on this particular kernel version. The head limits on output prevented overwhelming the conversation while assuming the most relevant information appears in the first few lines [9].

Most notably, the assistant demonstrated a pattern of proactive investigation that went beyond the user's checklist. The checks for ZFS ARC, PCIe AER, file descriptor limits, kernel compile-time configuration, and GPU ECC status were all additions the assistant made independently. This reflects an understanding that the user's 20-point list was a starting point, not an exhaustive specification, and that a complete audit requires thinking about what else could be wrong [14][2][4].

Conclusion

The kernel runtime parameter audit documented in this chunk is a masterclass in systematic performance diagnostics. Over 14 messages, the assistant transformed a 20-point checklist into a comprehensive investigation of over 40 kernel parameters, PCIe configuration settings, GPU driver states, and memory management policies. The audit identified 15 suboptimal settings, three of which were classified as critical, and produced a prioritized remediation plan that would guide the next phase of system optimization.

But the audit's true value lies not in the specific findings — important as they are — but in the methodology it represents. The assistant demonstrated that effective performance debugging requires not just following a checklist but understanding the full stack of mechanisms that can affect workload performance. It showed that the most impactful findings often live at the boundaries of the original investigation scope — the boundary between kernel-level and GPU-level configuration, between runtime sysctls and compile-time kernel parameters, between what the user asked for and what the system needs.

For anyone managing GPU compute infrastructure, this audit serves as both a template and a reminder: the difference between theoretical and actual performance often lives in the kernel parameters you didn't know to check, the PCIe registers you didn't think to read, and the GPU settings you assumed were correct. The systematic, hypothesis-driven approach demonstrated here — gather data, compare against best practices, prioritize by impact, and always look beyond the checklist — is a model worth emulating.## The Memory Subsystem: Hugepages and NUMA Topology

Message 5 of the audit probed the kernel's memory management configuration, focusing on transparent hugepages, hugepage allocation, and NUMA topology [7]. The findings were revealing but nuanced.

The transparent hugepage mechanism was set to madvise mode, meaning the kernel would not automatically coalesce 4KB pages into 2MB hugepages. Instead, applications must explicitly request hugepage-backed memory by calling madvise() with the MADV_HUGEPAGE flag. For GPU compute workloads running in containers, where SGLang and PyTorch allocate large tensors, this is a potential missed optimization. If the application does not explicitly opt in, it receives standard 4KB pages, increasing TLB miss rates and page table overhead [7].

The hugepage allocation status was even more stark: zero hugepages were pre-allocated. The Hugetlb counter was zero, meaning the kernel had not reserved any 2MB pages at boot or runtime. While NVIDIA's GPU driver manages its own device memory, host-side hugepages benefit CPU-side data paths — tensor preprocessing, token embedding lookups, and CPU-GPU synchronization — and their absence represents a tunable parameter that could yield improvements for certain workloads [7].

The NUMA topology check confirmed that the system had two NUMA nodes, as expected for a dual-socket AMD EPYC Turin platform. This topology was essential context for understanding GPU placement: with 8 GPUs, one would expect 4 GPUs on each NUMA node's PCIe root complex. The assistant would later verify this mapping in message 6, confirming that the GPUs were split across nodes [7].

The GPU-Specific Deep Dive

Messages 12 and 13 represented a critical pivot from system-level configuration to GPU-specific settings. The assistant queried compute mode, ECC status, clock policy, Fabric Manager, peer memory modules, and P2P topology — checks that the user's original checklist had not explicitly requested but that were essential for a complete picture [4].

The ECC finding was arguably the single most impactful discovery of the entire audit. All eight GPUs had ECC enabled, which consumes approximately 12.5% of memory bandwidth and reduces usable memory capacity. For inference workloads where transient bit errors rarely affect output quality, ECC is typically disabled to maximize throughput. The assistant correctly identified this as a significant performance tax, though it was not classified as a P0 item in the final report because the impact depends on the specific workload's sensitivity to memory errors [4].

The Fabric Manager check addressed NVLink infrastructure. On multi-GPU systems with NVLink bridges, Fabric Manager coordinates the NVLink fabric topology and must be running for GPUs to communicate at full NVLink bandwidth. Without it, inter-GPU communication falls back to PCIe, which is dramatically slower. The assistant's check of systemctl status nvidia-fabricmanager was a critical diagnostic for any multi-GPU inference deployment [4].

The nvidia-smi topo -m check revealed the physical connectivity between GPUs — which GPUs share a PCIe root complex, which are connected via NVLink, and the NUMA affinity of each GPU. This topology information is essential for understanding inter-GPU communication costs and for optimally placing model shards across GPUs during distributed inference [4].

The Kernel Upgrade and Its Aftermath

While the kernel audit itself did not include the kernel upgrade — that occurred later in the session — the findings directly motivated it. The discovery that the system was running kernel 6.8.12-9-pve with acpi-cpufreq instead of amd_pstate, that C-states were too deep, that NUMA balancing was enabled, and that PCIe MaxReadReq was stuck at 512 bytes all pointed to the need for a kernel upgrade with specific boot parameters.

The subsequent upgrade to kernel 6.14.11 with amd_pstate=active and processor.max_cstate=1 addressed several of the P0 and P1 findings simultaneously. However, the upgrade introduced its own challenges: CUDA failed inside the LXC container after the reboot due to stale NVIDIA device major numbers in the cgroup configuration. This required updating the device numbers to match the new kernel's device map — a post-reboot fix that the assistant handled methodically.

The kernel audit thus served a dual purpose. It identified the specific configuration changes needed, and it established a baseline against which the new kernel's configuration could be compared. After the upgrade, the assistant could re-run key checks to verify that the changes had taken effect and that no new issues had been introduced.

From System Audit to Bottleneck Analysis

The kernel audit was not an end in itself but a means to an end. The ultimate goal was to understand why the GLM-5-NVFP4 model was achieving only 10.36 tok/s against a theoretical maximum of 309 tok/s. The audit ruled out systemic misconfiguration as the primary cause — the system was not perfectly tuned, but the 15 suboptimal settings identified could not explain a 30x performance gap.

After the kernel upgrade and configuration fixes, the team's attention shifted to the software stack itself. A diagnostic tool was built to measure the latency of individual decode components. This revealed that simulated BF16 GEMMs and AllReduces accounted for only 8.9ms of the 95ms decode time, pointing the finger squarely at the FP4 GEMM kernel overhead, MoE routing, and attention as the primary culprits [chunk summary].

This progression — from system audit to kernel upgrade to targeted diagnostic tooling — illustrates the layered approach required for complex performance debugging. The kernel audit eliminated one class of potential causes (system configuration), allowing the team to focus on the next layer (GPU kernel efficiency). Without the audit, the team might have spent weeks optimizing the wrong thing.

Lessons for GPU Compute Infrastructure

The kernel audit documented in this chunk offers several lessons for anyone managing GPU compute infrastructure:

First, never assume defaults are optimal. Every parameter checked in this audit — from CPU frequency governor to PCIe MaxReadReq to GPU ECC status — was at its kernel or BIOS default. Defaults are designed for general-purpose computing, not for dedicated GPU compute workloads. The gap between default and optimal configuration can be 10-30% or more in throughput.

Second, go beyond the checklist. The user's 20-point checklist was thorough, but the most impactful findings — ECC enabled, amd_pstate not loaded, ZFS ARC potentially consuming memory — were not on the list. Effective system auditing requires domain knowledge about what matters for the specific workload, not just mechanical checklist execution.

Third, understand the full stack. Performance bottlenecks can manifest at any layer: CPU frequency management, PCIe protocol configuration, GPU driver state, memory management policy, or kernel compile-time parameters. A complete audit must span all of these layers, from the deepest kernel configuration to the highest-level GPU settings.

Fourth, separate data collection from analysis. The assistant's approach of gathering all data first, then synthesizing findings in a comprehensive report, is a model worth emulating. Premature analysis can lead to confirmation bias; systematic data collection ensures that all findings are considered on their merits.

Fifth, document the baseline. The audit captured the system state at a specific point in time, creating a reference for future comparisons. When performance degrades after a change, administrators can compare against this baseline to identify what changed. This documentation is invaluable for long-term system management.

References

[1] "Probing the Hidden Path: GPU Interrupt Affinity Analysis in an 8-GPU Blackwell System" — Analysis of message 9's GPU IRQ affinity probe.

[2] "Beyond the Checklist: Probing Kernel Configuration for GPU Compute Performance" — Analysis of message 11's kernel compile-time configuration checks.

[3] "Probing the GPU Interrupt Landscape: A Deep Dive into Kernel Parameter Auditing for Blackwell Compute" — Analysis of message 6's GPU interrupt and irqbalance checks.

[4] "Drilling Down to the GPU: A Systematic Audit of Compute-Relevant Settings" — Analysis of message 12's GPU-specific configuration checks.

[5] "The First Probe: Auditing Kernel Runtime Parameters for GPU Compute Performance" — Analysis of message 1's CPU frequency and power management checks.

[6] "The Kernel Runtime Parameter Audit: A Systematic Deep Dive into GPU Compute Performance Tuning" — Analysis of message 0's audit request and methodology.

[7] "Probing the Kernel's Memory Subsystem: Hugepages and NUMA Topology in an 8-GPU Compute Audit" — Analysis of message 5's memory subsystem checks.

[8] "The Kernel Audit That Almost Wasn't: Dissecting a Proxmox GPU Compute Tuning Report" — Analysis of message 14's comprehensive audit report.

[9] "The Deepening Audit: Uncovering Kernel-Level Bottlenecks in an 8-GPU ML Workstation" — Analysis of message 8's kernel command line and IOMMU checks.

[10] "Peeling Back the Kernel: A Deep Dive into C-State Analysis for GPU Compute Performance" — Analysis of message 2's C-state and idle driver checks.

[11] "The Silent Audit: Probing Linux Kernel Runtime Parameters for GPU Compute Performance" — Analysis of message 4's scheduler and network parameter checks.

[12] "Peering into the PCIe Subsystem: A Methodical Audit of GPU Compute Infrastructure" — Analysis of message 10's PCIe ACS, ASPM, and Relaxed Ordering checks.

[13] "The PCIe MaxReadReq Audit: A Single Command That Revealed a Hidden Bottleneck" — Analysis of message 7's PCIe configuration checks.

[14] "The Final Probe: A Deep Dive into Message 13 of a GPU Compute Kernel Audit" — Analysis of message 13's final data-gathering checks.

[15] "The Methodical Auditor: A Deep Dive Into Kernel Parameter Discovery for GPU Compute Performance" — Analysis of message 3's block device and VM parameter checks.