The Moment of Truth: Diagnosing P2P Performance After a VM Migration

In the long and winding journey of deploying a large language model across eight NVIDIA RTX PRO 6000 Blackwell GPUs, there comes a moment when all the theoretical work must face reality. Message [msg 368] in this opencode session is precisely that moment. After painstakingly migrating a Proxmox virtual machine from the legacy i440FX chipset to Q35, enabling PCIe passthrough, adding pci=realloc to fix BAR allocation failures, and setting iommu=pt on the host kernel, the assistant finally runs the benchmark that will tell whether any of it mattered. The answer, delivered with clinical precision, is both disappointing and illuminating: the changes produced only marginal improvements, and the fundamental bottleneck remains untouched.

The Road to This Message

To understand the weight of message [msg 368], one must appreciate the journey that preceded it. The session began in Segment 0 with the full setup of an ML environment on Ubuntu 24.04 — NVIDIA driver installation, CUDA Toolkit 13.1, PyTorch, flash-attn, and ultimately the deployment of the GLM-5-NVFP4 model using SGLang. By Segment 3, the focus had narrowed to a single, maddening problem: cross-GPU communication was bottlenecked by the virtualization layer.

The GPUs were all detected as NS (Not Supported) for P2P (peer-to-peer) DMA in nvidia-smi topo -p2p r. The topology showed PHB (PCIe Host Bridge) connections between every pair of GPUs, meaning all cross-GPU traffic had to go through the host CPU and system memory rather than directly between GPUs. For a model that requires tight coordination across eight GPUs using tensor parallelism, this is a critical performance issue.

The team had already attempted multiple fixes. They migrated the VM from i440FX to Q35 with pcie=1 to get proper PCIe topology. They added pci=realloc to the guest kernel command line to fix BAR allocation failures that initially prevented 6 of 8 GPUs from being detected. They enabled amd_iommu=on iommu=pt on the Proxmox host. They even attempted to disable ACS (Access Control Services) in the BIOS to merge IOMMU groups, only to discover that each GPU sits on its own dedicated PCIe root complex on the ASUS ESC8000A-E13 motherboard — a hardware topology that fundamentally prevents VFIO from granting direct P2P DMA access, regardless of software configuration.

What the Message Actually Says

The message opens with a candid assessment:

Interesting — the bandwidth numbers are essentially the same as before the VM config changes: - 4MB: 33.67 GB/s (was ~32.6 GB/s) — marginal improvement - 12KB: 0.94 GB/s, 13.1µs (was 0.90 GB/s, 13.7µs) — marginal improvement

The assistant immediately identifies the core issue: "The ~13µs floor for small transfers is inherent to the SHM/staged-copy path (GPU→host memory→GPU)." This is a crucial diagnostic insight. The latency floor is not a configuration problem — it's a fundamental property of the data path when P2P DMA is unavailable.

The assistant then runs a more comprehensive bandwidth sweep across six transfer sizes, from 12KB to 16MB:

  12KB:    0.92 GB/s     13.4 µs/copy
  48KB:    3.55 GB/s     13.8 µs/copy
 192KB:    9.09 GB/s     21.6 µs/copy
 768KB:   19.01 GB/s     41.4 µs/copy
   4MB:   32.14 GB/s     97.9 µs/copy
  16MB:   41.41 GB/s    405.1 µs/copy

This data tells a clear story. For small transfers (12KB–48KB), latency is dominated by the fixed overhead of the SHM path (~13µs). As transfer sizes increase, bandwidth scales up but never reaches the theoretical maximum of PCIe Gen5 x16 (~64 GB/s per direction). The 16MB transfer achieves 41.41 GB/s — respectable but well short of what true P2P DMA could deliver.

The Reasoning and Motivation Behind the Message

The assistant's motivation in this message is diagnostic clarity. After investing significant effort in VM configuration changes — chipset migration, kernel parameter tuning, IOMMU configuration — the natural question is: did any of it help? The assistant answers this with a carefully designed benchmark that tests multiple transfer sizes to characterize the communication path.

The reasoning is methodical. Rather than simply declaring success or failure based on a single number, the assistant probes the system across a range of sizes to understand the shape of the performance curve. The small-transfer latency (~13µs) reveals the fixed overhead of the SHM path. The bandwidth scaling with size reveals the achievable throughput. Together, these numbers tell a complete story about what the system can and cannot do.

The assistant also makes a critical decision about what to test next. After the initial two-point benchmark (12KB and 4MB), the assistant runs a six-point sweep. This is not random — it's designed to find the inflection point where latency stops being dominated by fixed overhead and starts being dominated by bandwidth. The data shows this transition happening around 192KB, where latency jumps from ~13µs to ~21µs, indicating the transfer is now large enough that the copy time itself matters.

Assumptions and Their Consequences

The message rests on several implicit assumptions. The first is that the benchmark methodology is sound. The assistant uses torch.randn to generate data, copy_ for the transfer, and torch.cuda.synchronize() to ensure completion. This is a reasonable approach, but it measures only the simplest possible operation — a blocking copy between two GPUs. Real model inference involves more complex communication patterns (all-reduce, all-gather, etc.) that may behave differently.

The second assumption is that the comparison to "before" numbers is valid. The assistant references previous benchmarks showing ~32.6 GB/s for 4MB and ~0.90 GB/s for 12KB. However, the message doesn't verify that the earlier benchmarks were run under identical conditions — same GPU load, same memory state, same PCIe link state. Small variations in system state can affect benchmark results.

The third assumption is that the SHM path is the only available fallback. In reality, NCCL has multiple transport options: P2P (direct GPU DMA), SHM (shared memory via host), and NVLink (direct GPU interconnect, not available on these RTX PRO 6000 cards). The assistant correctly identifies the SHM path but doesn't explore whether NCCL's configuration could be tuned to improve it.

Input Knowledge Required

To fully understand this message, the reader needs substantial context from earlier in the session. One must know:

Output Knowledge Created

This message creates several important pieces of knowledge:

  1. A quantitative characterization of the cross-GPU communication path: The six-point bandwidth sweep provides a complete picture of what the system can achieve without P2P DMA. This is valuable for setting expectations about model performance.
  2. Confirmation that the VM configuration changes were insufficient: The marginal improvement (~1 GB/s on the 4MB test) demonstrates that chipset migration, PCIe reallocation, and IOMMU passthrough did not address the fundamental bottleneck.
  3. Identification of the latency floor: The ~13µs minimum latency for small transfers is a critical parameter for understanding model performance. For a model using tensor parallelism, small messages are exchanged frequently, and this latency adds up.
  4. A baseline for future optimization: Any future changes (different NCCL transport, different VM configuration, different hardware) can be compared against these numbers.

The Thinking Process Visible in the Reasoning

The assistant's reasoning is laid bare in the structure of the message. The first paragraph establishes the comparison to previous benchmarks, immediately framing the results in context. The second paragraph interprets the numbers, identifying the SHM path as the cause of the latency floor. The third paragraph describes the next action — a more comprehensive benchmark — and the final block presents the results.

This structure reveals a disciplined analytical process: observe, interpret, probe deeper, conclude. The assistant does not jump to conclusions after the first two data points. Instead, it recognizes that the 12KB and 4MB tests alone cannot fully characterize the communication path, and designs a six-point sweep to capture the full performance curve.

The choice of sizes is itself revealing. The assistant tests 12KB, 48KB, 192KB, 768KB, 4MB, and 16MB — roughly doubling each time with a few strategic jumps. This logarithmic sampling is a classic benchmarking technique that captures behavior across orders of magnitude without wasting time on redundant measurements.

The assistant also demonstrates awareness of the practical implications. The ~13µs floor for small transfers is not just an abstract number — it directly affects the performance of tensor-parallel inference, where small activation tensors are exchanged between GPUs at every layer. The assistant implicitly recognizes this by testing down to 12KB, which is a realistic size for individual tensor slices in a transformer model.

Mistakes and Incorrect Assumptions

While the message is well-reasoned, there are several potential issues worth examining.

The assumption that the "before" numbers are comparable: The assistant compares the new results to benchmarks run before the VM configuration changes. However, the earlier benchmarks were run in a different context — possibly with different GPU clock states, different memory utilization, or different system load. The assistant does not control for these variables, which could easily account for the ~1 GB/s difference in the 4MB test.

The assumption that P2P would dramatically improve performance: The assistant seems to treat the ~33 GB/s result as a disappointment, implicitly expecting that proper P2P would deliver much higher bandwidth. But even on bare metal, P2P between GPUs on separate root complexes goes through the CPU's Infinity Fabric, which has its own latency and bandwidth characteristics. The assistant does not establish what the theoretical maximum for this hardware configuration would be.

The narrow focus on bandwidth rather than latency: For model inference, latency is often more critical than bandwidth. The assistant measures both but focuses the interpretation on bandwidth numbers. The ~13µs floor for small transfers is actually the more important finding for the deployment use case.

The implicit assumption that NCCL will use the same path: The benchmark uses torch.Tensor.copy_(), which is a simple GPU-to-GPU copy. NCCL's all-reduce and other collective operations may use different algorithms and achieve different performance characteristics. The assistant does not test NCCL directly.

The Broader Significance

Message [msg 368] represents a turning point in the session. After weeks of configuration work — driver installation, CUDA toolkit management, flash-attn compilation, VM migration, PCIe topology optimization — the assistant finally confronts the fundamental constraint of the hardware. The message is, in essence, an acceptance of reality.

The ~13µs latency floor is not a bug to be fixed. It is a physical consequence of the hardware topology: eight GPUs on eight separate PCIe root complexes, connected through the CPU's coherence fabric, in a virtualized environment where VFIO must mediate every cross-device DMA operation. No amount of kernel parameter tuning or chipset migration can change this.

This realization has profound implications for the deployment strategy. If P2P cannot be enabled, the model's tensor parallelism must be designed to minimize cross-GPU communication, or the team must accept the performance penalty. The assistant's next steps — hinted at by the "NCCL-level tuning" comment — suggest a pragmatic shift from trying to fix the hardware to optimizing the software around the constraint.

The message also serves as a cautionary tale about virtualization for GPU workloads. While Proxmox with VFIO passthrough can successfully present GPUs to a VM, the performance characteristics are fundamentally different from bare metal. The PCIe topology that the VM sees is an abstraction, and the NVIDIA driver's P2P detection correctly identifies that true peer-to-peer DMA is not available. For workloads that depend on tight GPU-to-GPU communication, bare metal or NVLink-equipped hardware remains essential.

Conclusion

Message [msg 368] is a masterclass in diagnostic benchmarking. It asks a clear question — did the VM configuration changes improve cross-GPU communication? — and answers it with carefully collected data. The answer is not what anyone hoped for, but it is honest, precise, and actionable. The assistant's disciplined approach — comparing to baselines, probing multiple transfer sizes, identifying the latency floor, and avoiding premature conclusions — provides a model for how to evaluate system performance under real constraints.

The message also captures the emotional arc of the session: the hope that each configuration change would unlock P2P, the gradual realization that the hardware topology is the fundamental constraint, and the pragmatic shift toward working within that constraint. It is a moment of clarity that redefines the entire project's trajectory.