The Virtualization Ceiling: How an 8-GPU LLM Deployment Collided with the Physics of PCIe Topology

Introduction

In the high-stakes world of large language model inference, the difference between a successful deployment and a frustrating bottleneck often lies not in the model architecture or the GPU compute capability, but in the invisible plumbing that connects the hardware together. When deploying the GLM-5-NVFP4 model—a 744-billion-parameter Mixture-of-Experts language model—across eight NVIDIA RTX PRO 6000 Blackwell GPUs inside a Proxmox virtual machine, the team encountered precisely this kind of hidden bottleneck. The session documented in this chunk represents a pivotal turning point: the moment when the investigation shifted from software configuration to hardware topology, from "can we enable this feature?" to "can we work around a fundamental physical constraint?"

This article synthesizes the work across messages 316 through 330, tracing the arc from discovery through diagnosis to the formulation of a structured remediation plan. It examines how the assistant and user collaboratively peeled back the layers of virtualization—from NCCL tuning to IOMMU configuration to PCIe topology mapping—and ultimately confronted a hardware reality that no software change could overcome.

The Discovery: A Bottleneck Revealed

The chunk opens with message 316, a comprehensive status report that stands as one of the most significant artifacts in the entire session [1]. This document, spanning over 3,000 words, consolidates everything learned across hours of debugging, benchmarking, and profiling. It documents the hardware configuration (eight RTX PRO 6000 Blackwell GPUs with SM120 architecture, an AMD EPYC 9335 CPU, 432GB of RAM), the software environment (CUDA 12.8 for PyTorch compatibility, NVIDIA driver 590.48.01, SGLang compiled from source), and the model details (GLM-5-NVFP4 with DeepSeek Sparse Attention forcing NSA attention backends).

But the critical discovery is buried in the "CRITICAL" subsection: the machine is running inside a Proxmox KVM virtual machine, and peer-to-peer DMA between GPUs is completely disabled. Every GPU pair shows NS—"Not Supported"—for P2P read operations. Cross-GPU transfers that should happen at PCIe Gen5 x16 speeds (64 GB/s theoretical) are instead bouncing through host memory with a latency floor of 13.7 microseconds per small transfer. For a model with 78 layers of all-reduce operations, those microseconds compound into milliseconds of overhead per decode step.

The benchmark results tell the story: peak throughput of approximately 485 total tokens per second at 64 concurrent requests, with single-stream throughput stuck at a mere 11 tok/s. The targets—1,000+ total tok/s and 100+ tok/s single-stream—remain distant. GPU profiling shows 100% utilization but only 55% power draw, the classic signature of a latency-bound workload where the GPUs spend most of their time waiting for data from their peers rather than computing.

The Pivot: From NCCL Tuning to Host-Side Investigation

The user's response in message 317 is deceptively simple: "Continue if you have next steps, or stop and ask for clarification if you are unsure how to proceed." This two-sentence message, analyzed in detail in [2], defines the rhythm of the session. The user grants the assistant autonomy while leaving the door open for clarification.

The assistant's response in message 318 begins with a state check—confirming the server is running and idle—and then proposes a multi-pronged strategy [3][4]. The initial plan focuses on NCCL environment variable tuning (NCCL_P2P_DISABLE, NCCL_SHM_USE_CUDA_MEMCPY, NCCL_ALGO variants), TP4+PP2 configuration to reduce the number of GPUs in each all-reduce, and host-side Proxmox changes. The assistant explicitly asks about Proxmox host access, recognizing that this is the most impactful path if feasible [5][6].

The user confirms root access to the Proxmox host (named kpro6) in message 322, providing initial diagnostic output [7]. The kernel command line lacks IOMMU enablement parameters, but eight AMD IOMMU units are detected in the hardware. The assistant recognizes this as a promising sign: the hardware supports IOMMU, it just needs to be properly configured [8].

The Data That Changed Everything

What follows is a sequence of messages that documents a fundamental shift in understanding. The assistant attempts to SSH directly to the Proxmox host from its environment but fails—the hostname kpro6 is not resolvable from the assistant's network [9][10]. The user clarifies with a two-word message: "no ssh" [11][12]. This forces the assistant to adopt a delegation workflow: it provides commands for the user to run on the host and asks for the output to be pasted back [13].

Message 329 is the user's response: a raw paste of diagnostic command outputs that collectively paint a devastating picture of the hardware topology [14]. The key findings are:

  1. Eight GPUs, eight IOMMU groups: Each GPU is in its own IOMMU group (10, 28, 42, 61, 72, 90, 101, 117), with no two GPUs sharing a group.
  2. Eight separate PCIe root complexes: The lspci -tv output reveals that each GPU connects to its own dedicated root complex on the AMD EPYC Turin platform. The pattern is consistent: each root complex contains a "Turin PCIe Dummy Host Bridge" and a single PCIe slot connecting directly to one GPU.
  3. No PCIe switches: There are no shared PCIe switches anywhere in the topology. Each GPU has a dedicated lane to its own root complex, with no hardware mechanism for direct GPU-to-GPU communication at the PCIe level.
  4. VFIO is properly configured: The vfio_pci, vfio_iommu_type1, and iommufd modules are all loaded and functioning. The passthrough setup is correct—it's the hardware that's the limitation.
  5. VM configuration uses i440FX: The VM (ID 128, named "llm-one") uses the legacy i440FX chipset with eight hostpci entries using mapping=pro6000, but critically missing the pcie=1 flag that would present the GPUs as proper PCIe devices. This data is the moment of truth. The assistant's working hypothesis—that ACS override patches could merge IOMMU groups and enable P2P—is conclusively refuted. ACS (Access Control Services) is a feature of PCIe switches, and there are no switches in this topology. Each GPU is on its own root complex, and no software configuration can bridge that physical separation.

The Three-Phase Remediation Plan

Message 330 represents the assistant's synthesis of this diagnostic data into a structured, actionable plan [15]. The analysis is organized across three layers of the system stack:

Layer 1: The IOMMU Translation Tax

The assistant identifies that while IOMMU is active on the host, the kernel command line lacks the iommu=pt (passthrough) parameter. Without this, the IOMMU operates in translation mode, meaning every DMA transaction—every GPU memory read or write—must go through IOMMU translation tables rather than being passed through directly. This adds latency to every GPU memory transaction.

The fix is straightforward: add amd_iommu=on iommu=pt to the Proxmox host's kernel command line by editing /etc/kernel/cmdline and running proxmox-boot-tool refresh. This is Phase 2 of the plan and requires a host reboot.

Layer 2: The VM Configuration Mismatch

The assistant identifies three specific issues with how the GPUs are presented to the VM:

  1. Missing pcie=1 flag: Without this flag, the GPUs are presented as conventional PCI devices behind the i440FX bus, not as PCIe devices. This disables PCIe-native features including ACS and ARI, and critically prevents the NVIDIA driver from seeing proper PCIe capabilities.
  2. i440FX machine type: The legacy chipset does not model PCIe natively. All devices appear on a flat PCI bus.
  3. Missing x-vga=off: A best-practice omission for compute GPUs. Phase 1 of the plan addresses the most immediate issue: adding pcie=1 to each of the eight hostpci entries in the VM configuration. This is a low-risk change that requires only a VM restart, not a host reboot. The assistant hypothesizes that this alone could improve P2P status from NS to something better, as the NVIDIA driver would be able to negotiate PCIe capabilities properly.

Layer 3: The Physical Topology Constraint

The deepest layer is the one that cannot be changed: each GPU sits on its own PCIe root complex of the AMD EPYC Turin processor. This topology is standard for high-end AMD EPYC server platforms like the ASUS ESC8000A-E13 motherboard. It maximizes per-GPU bandwidth by giving each GPU its own dedicated PCIe lane to the CPU, but it fundamentally prevents the kind of low-latency P2P communication that NVLink-enabled systems or systems with PCIe switches can achieve.

The assistant notes this matter-of-factly: "This is typical for 8-GPU non-NVLink server configs." There is no Phase 4 for this layer—it is a physical constraint that software alone cannot overcome.

The Broader Implications for Virtualized GPU Inference

The work in this chunk illustrates a profound truth about modern AI infrastructure: virtualization introduces friction that is invisible until you push throughput to the limit. The Proxmox VM was running the model correctly, producing valid outputs, and appearing healthy—yet it was leaving 50% or more of the hardware's potential performance on the table due to P2P overhead.

This is not a bug in Proxmox, KVM, or the NVIDIA driver. It is a fundamental consequence of the IOMMU's isolation model. The IOMMU exists to protect the host from malicious or buggy guest drivers by translating and validating every DMA transaction. When P2P is enabled, the IOMMU must allow one GPU to directly access another GPU's memory—which requires the IOMMU to trust that both devices are under the control of the same guest. In a topology where each GPU is in its own IOMMU group (because each is on its own root complex), the IOMMU cannot grant this trust without compromising isolation.

For ML practitioners deploying large models across multiple GPUs, the choice between bare metal and virtualization is not just about convenience or resource sharing—it is a fundamental architectural decision. A Proxmox host with GPUs on separate root complexes can provide excellent per-GPU performance for independent workloads but will bottleneck any workload requiring frequent cross-GPU communication.

The Path Forward

The chunk concludes with the assistant having formulated a clear plan but facing a fundamental constraint that no amount of configuration can fully resolve. The three-phase plan—pcie=1 first, then iommu=pt, then Q35 migration if needed—represents the best possible approach within the hardware's limitations. But the underlying reality remains: eight GPUs on eight root complexes with no shared PCIe switch means that P2P DMA will never be possible through conventional means.

The session's next steps, as documented in the chunk summary, involve exploring hacky workarounds: insecure kernel parameters like vfio_iommu_type1.allow_unsafe_interrupts, the nv_peer_mem module for NVLink-like behavior over PCIe, and alternative parallelism strategies that reduce cross-GPU communication. These are the options that remain when the clean solution—proper hardware topology—is unavailable.

Conclusion

Messages 316 through 330 of this opencode session document a journey from discovery to diagnosis to the hard acceptance of a physical constraint. The team identified that their Proxmox VM was bottlenecked by the absence of P2P DMA, investigated the host's IOMMU configuration and PCIe topology, and ultimately discovered that each GPU resides on its own dedicated root complex—a topology that no software change can bridge.

The work in this chunk is a masterclass in systems-level debugging under virtualization. It demonstrates the importance of understanding the full hardware topology, not just the VM-level view. It shows how to reason about multi-layered problems: start with what you know, identify what's missing, connect missing pieces to observed symptoms, and propose changes in order of risk. And it illustrates the crucial moment when an engineering team must accept that some constraints are physical, not configurable—and pivot from "how do we fix this?" to "how do we work around this?"

For anyone deploying large language models on virtualized GPU infrastructure, this chunk offers both a cautionary tale and a practical methodology. The caution: virtualization can introduce bottlenecks that are invisible to standard profiling tools, and the absence of P2P DMA can fundamentally limit throughput for communication-intensive models. The methodology: systematic investigation across layers—from NCCL settings to VM configuration to host kernel parameters to physical PCIe topology—can reveal the true nature of the bottleneck and guide the search for solutions.## References

[1] "The Moment of Reckoning: How a Comprehensive Status Report Exposed the Virtualization Bottleneck in an 8-GPU LLM Deployment" — Analysis of message 316, the comprehensive status report that documented the VM/P2P discovery.

[2] "The Signal in the Silence: How a Two-Sentence User Message Defines the Rhythm of an AI-Assisted Engineering Session" — Analysis of message 317, the user's "continue or clarify" response.

[3] "The Pause Before Action: How a Simple State Check Revealed the Core Bottleneck in an 8-GPU Inference Deployment" — Analysis of message 318, the assistant's state check and initial plan formulation.

[4] "The Status Check: A Pivotal Moment in Diagnosing Virtualization Bottlenecks for Large-Scale GPU Inference" — Analysis of message 319, the server health verification.

[5] "The Strategic Crossroads: Diagnosing a Virtualization Bottleneck in an 8-GPU LLM Inference Stack" — Analysis of message 320, the strategic planning message.

[6] "The Proxmox Host Question: A Pivotal Moment in GPU Inference Debugging" — Analysis of message 321, the assistant's inquiry about host access.

[7] "The Moment of Truth: Diagnosing IOMMU Configuration on a Proxmox Host" — Analysis of message 322, the user's initial host diagnostic output.

[8] "The Turning Point: Root Access Confirmed and the IOMMU Investigation Begins" — Analysis of message 323, the assistant's recognition of the opportunity.

[9] "When SSH Fails: A Pivotal Moment in the P2P DMA Investigation" — Analysis of message 324, the failed SSH attempt.

[10] "The Network Barrier: A Pivotal Moment in GPU P2P Debugging" — Analysis of message 325, the network connectivity limitation.

[11] "The Silent Message: When an AI Assistant Has Nothing to Say" — Analysis of message 326, the empty assistant response.

[12] "The Two-Word Pivot: How \"no ssh\" Redefined a Debugging Workflow" — Analysis of message 327, the user's clarifying response.

[13] "The Pivot Point: When an AI Assistant Loses Direct Access and Must Delegate" — Analysis of message 328, the assistant's adaptation to the delegation workflow.

[14] "The Topology That Cannot Be Bridged: A Proxmox VM's PCIe Architecture Reveals the Fundamental Limits of GPU P2P" — Analysis of message 329, the user's diagnostic data that revealed the hardware topology constraint.

[15] "The Architecture of Latency: Diagnosing GPU P2P Bottlenecks in a Proxmox VM" — Analysis of message 330, the assistant's three-phase remediation plan.