The Moment of Proof: Confirming P2P GPU Access in an LXC Container

In any complex engineering effort, there comes a moment when weeks of struggle crystallize into a single, unambiguous measurement. For this opencode session — a sprawling, multi-segment journey to deploy the GLM-5-NVFP4 large language model across 8 NVIDIA RTX PRO 6000 Blackwell GPUs — that moment arrived in [msg 568]. The message is deceptively simple: a Python script that copies tensors between GPUs and prints bandwidth numbers. But those numbers — 53.76 GB/s same-NUMA, 40.24 GB/s cross-NUMA — represent the culmination of an odyssey spanning driver debugging, virtualization topology analysis, kernel parameter hacking, and container migration. This article unpacks that single message, examining why it was written, what decisions it reflects, and what it meant for the broader project.

The Message in Full

The assistant wrote:

Topology is confirmed NODE/SYS (bare-metal) — this is the key improvement over the KVM VM which only had PHB. Persistence mode is enabled.

Now let me do a quick P2P bandwidth check, then move on to launching sglang.

```bash ssh root@10.1.230.174 "/root/ml-env/bin/python3 -c ' import torch import time

# Test P2P access for i in range(8): for j in range(8): if i != j: can = torch.cuda.can_device_access_peer(i, j) if not can: print(f\" GPU {i} -> GPU {j}: NO P2P\")

print(\"P2P access check done (no output = all pairs have P2P)\")

# Quick bandwidth test: GPU 0 -> GPU 1 (same NUMA) size = 256 1024 1024 # 256 MB a = torch.randn(size // 4, device=\"cuda:0\") b = torch.empty(size // 4, device=\"cuda:1\")

# warmup b.copy_(a) torch.cuda.synchronize()

iters = 20 start = time.perf_counter() for _ in range(iters): b.copy_(a) torch.cuda.synchronize() elapsed = time.perf_counter() - start bw = (size * iters) / elapsed / 1e9 print(f\"GPU0->GPU1 (same NUMA): {bw:.2f} GB/s\")

# Cross-NUMA: GPU 0 -> GPU 4 c = torch.empty(size // 4, device=\"cuda:4\") c.copy_(a) torch.cuda.synchronize()

start = time.perf_counter() for _ in range(iters): c.copy_(a) torch.cuda.synchronize() elapsed = time.perf_counter() - start bw = (size * iters) / elapsed / 1e9 print(f\"GPU0->GPU4 (cross NUMA): {bw:.2f} GB/s\") ' 2>&1" ```

P2P access check done (no output = all pairs have P2P) GPU0->GPU1 (same NUMA): 53.76 GB/s GPU0->GPU4 (cross NUMA): 40.24 GB/s

Why This Message Was Written: The Context of Desperation

To understand why the assistant wrote this message, one must understand the wall it had just climbed. The project had been blocked for days by a fundamental problem: the GPUs could not communicate directly with each other. In the KVM virtual machine used earlier in the session (<seg id=3>), nvidia-smi topo -m showed a topology of PHB (PCIe Host Bridge) between all GPU pairs. This meant every GPU-to-GPU data transfer had to traverse the host memory through the VFIO (Virtual Function I/O) layer, adding massive latency and capping throughput far below what the hardware's PCIe Gen5 fabric could deliver.

The team had tried everything. They attempted to enable P2P DMA by modifying Proxmox host kernel parameters, migrating the VM to a Q35 chipset, fixing BAR allocation, and disabling ACS (Access Control Services) — all documented in <seg id=3>. But the hardware topology was fundamentally against them: each GPU sat on its own PCIe root complex, and VFIO could not bridge across IOMMU groups for P2P. That effort ended in defeat.

The pivot to an LXC container (<seg id=4>) was a hail-mary: run directly on the Proxmox host kernel, bypassing VFIO entirely. But this brought its own nightmare — CUDA initialization failed with error code 3 (CUDA_ERROR_NOT_INITIALIZED). The breakthrough came in [msg 560]-[msg 563], when the assistant discovered a GitHub issue (#797) about the NVIDIA open kernel module's Heterogeneous Memory Management (HMM) feature being incompatible with certain kernels. Setting uvm_disable_hmm=1 as a module parameter for nvidia_uvm instantly resolved the CUDA blocker.

So by the time [msg 568] is written, the assistant has just achieved CUDA working inside the LXC container with all 8 GPUs detected ([msg 565]). But "CUDA works" is not the same as "P2P works." The entire point of migrating to LXC was to achieve direct GPU-to-GPU communication. This message is the verification step — the moment of truth where the assistant checks whether the multi-day gamble paid off.## Decisions Embedded in the Code

The message contains several implicit decisions that reveal the assistant's reasoning process.

Decision 1: Verify P2P before launching the model server. The assistant explicitly states "now let me do a quick P2P bandwidth check, then move on to launching sglang." This ordering is deliberate. If P2P were broken (as it was in the KVM VM), launching sglang with tensor parallelism across 8 GPUs would likely fail or perform abysmally. The assistant is applying a principle of progressive validation: confirm the foundation before building on it. A failed P2P test would have sent the investigation back to the virtualization layer, saving hours of debugging a server that couldn't possibly work well.

Decision 2: Test both same-NUMA and cross-NUMA bandwidth. The topology output (shown in [msg 567]) revealed that GPUs 0-3 are on NUMA node 0 and GPUs 4-7 on NUMA node 1, with SYS connectivity between the groups. The assistant tests GPU0→GPU1 (same NUMA, expected to be fastest) and GPU0→GPU4 (cross NUMA, expected to be slower due to the NUMA boundary). This shows an understanding that PCIe topology and NUMA domains jointly determine P2P performance. The 13.52 GB/s gap between the two measurements (53.76 vs 40.24) confirms that the NUMA boundary adds overhead, likely from traversing the CPU interconnect (e.g., AMD Infinity Fabric or Intel UPI) between sockets.

Decision 3: Use 256 MB buffers and 20 iterations. The buffer size is large enough to saturate the PCIe link and measure sustained bandwidth rather than cache effects. Twenty iterations provide statistical averaging. The warmup copy (b.copy_(a) before the timed loop) ensures any lazy allocation or page migration happens outside the measurement window. These are standard practices in GPU bandwidth benchmarking, and their presence indicates the assistant has experience with CUDA performance measurement.

Decision 4: Check all 56 GPU pairs for P2P capability. The nested loop for i in range(8): for j in range(8): if i != j: checks every directed pair. The script prints only failures ("NO P2P"), so silence means success. This is a clean, minimal-output design — if everything works, you get one line of confirmation. The assistant is optimizing for readability of results, anticipating that the output will be read by a human (or itself in the next round).

Assumptions Made

The message rests on several assumptions, most of which are justified by prior work:

The LXC container provides bare-metal GPU access. This was the core hypothesis driving the migration from KVM to LXC. The assistant assumes that because the container shares the host kernel and has direct device access (via bind-mounts of /dev/nvidia*), the GPU topology will match the host's. The nvidia-smi topo -m output in [msg 567] confirmed NODE and SYS links rather than PHB, validating this assumption.
torch.cuda.can_device_access_peer() accurately reflects P2P capability. This CUDA runtime API queries the driver's topology information. The assistant trusts it as authoritative.
The bandwidth test is representative of real workload performance. The simple b.copy_(a) measures raw PCIe DMA bandwidth. For tensor parallelism in model inference, this is a reasonable proxy — the all-reduce and all-gather collectives used by sglang's TP implementation are bandwidth-bound operations that directly benefit from P2P DMA.
Persistence mode is correctly enabled. The assistant ran nvidia-smi -pm 1 on the host ([msg 567]) before the container test. It assumes this setting persists into the container and benefits GPU performance (by keeping the GPU initialized and reducing driver overhead on each CUDA API call).

Mistakes and Incorrect Assumptions

The message itself is clean and contains no obvious errors. However, a subtle issue lurks in the bandwidth measurement methodology. The test uses torch.randn(size // 4, device="cuda:0") which creates a float32 tensor. The size calculation size // 4 converts 256 MB to 64 million float32 elements. But b.copy_(a) copies the tensor element-wise — the actual data transferred is 256 MB per iteration. The bandwidth formula (size * iters) / elapsed / 1e9 correctly computes GB/s.

However, the test measures unidirectional P2P bandwidth (GPU0 sends to GPU1). In real model training or inference, communication patterns are often bidirectional or involve collective operations. The 53.76 GB/s result is impressive but represents an upper bound — real collectives like all-reduce typically achieve 60-80% of raw P2P bandwidth due to protocol overhead and synchronization.

A more significant limitation is that the test uses synchronous copies with explicit torch.cuda.synchronize() calls. This measures sustained bandwidth but doesn't capture the latency aspect. In the earlier KVM VM, the bottleneck was not just bandwidth but latency (~13 µs floor due to VFIO). The LXC environment likely reduces this dramatically, but the message doesn't measure it. The assistant implicitly assumes that if bandwidth is good, latency is also acceptable — which is generally true for PCIe P2P, but not guaranteed.

Input Knowledge Required

To fully understand this message, the reader needs:

The topology matrix from nvidia-smi topo -m ([msg 567]): The NODE (same PCIe switch/NUMA node) and SYS (different NUMA node) labels are the language of GPU interconnect topology. Without this context, the bandwidth numbers are just numbers.
The history of the KVM VM's PHB topology: The comparison "key improvement over the KVM VM which only had PHB" only lands if you know that PHB means all GPU traffic went through the host bridge — essentially software-mediated, slow, and high-latency.
The HMM fix ([msg 560]-[msg 563]): CUDA didn't work at all in the LXC container until uvm_disable_hmm=1 was set. The P2P test only happens because that blocker was resolved.
The hardware configuration: 8 NVIDIA RTX PRO 6000 Blackwell GPUs, connected via PCIe Gen5, split across two NUMA domains. The expected peak bandwidth for PCIe Gen5 x16 is approximately 64 GB/s per direction (theoretically 128 GB/s bidirectional). The measured 53.76 GB/s is about 84% of the theoretical unidirectional limit, which is excellent.

Output Knowledge Created

This message produces several critical pieces of knowledge:

P2P is fully functional across all 8 GPUs. This is the single most important result. Every pair can access every other pair directly via PCIe DMA, without host memory involvement.
Same-NUMA bandwidth is 53.76 GB/s. This is within striking distance of PCIe Gen5's theoretical limit, confirming that the hardware fabric is performing well.
Cross-NUMA bandwidth is 40.24 GB/s. The ~25% penalty for crossing the NUMA boundary is expected and acceptable. For tensor parallelism, the communication pattern typically involves nearest-neighbor exchanges within the same NUMA domain where possible.
The LXC approach works. This validates the entire strategy of migrating from KVM to LXC. The gamble paid off.
The project can proceed to the next phase: launching sglang. The assistant's stated next step — "move on to launching sglang" — is now feasible. The P2P verification is the green light for deploying the GLM-5-NVFP4 model with tensor parallelism across all 8 GPUs.

The Thinking Process Visible in the Message

The assistant's reasoning is compact but visible. The opening line — "Topology is confirmed NODE/SYS (bare-metal) — this is the key improvement over the KVM VM which only had PHB" — shows the assistant comparing against the known baseline. The word "key" signals that this is the decisive metric for the entire LXC migration.

The phrase "now let me do a quick P2P bandwidth check, then move on to launching sglang" reveals the assistant's mental priority queue. The P2P test is a gate: if it fails, the next step is not sglang but further debugging. The "quick" qualifier suggests the assistant is eager to move forward but disciplined enough to verify first.

The bandwidth test parameters (256 MB, 20 iterations, warmup) show deliberate experimental design. The assistant didn't just copy a random tensor and time it — it chose a meaningful buffer size, included a warmup phase, and averaged over multiple iterations. This reflects an understanding that GPU timing measurements are noisy and need careful methodology.

The cross-NUMA test (GPU0→GPU4) is particularly telling. The assistant could have tested only the best case (same NUMA) and declared victory. But by testing the worst case too, the assistant demonstrates intellectual thoroughness — it wants to know the full performance envelope, not just the highlight reel.

Conclusion

Message [msg 568] is a quiet triumph in a long and difficult engineering session. It contains no dramatic breakthroughs, no clever hacks, no new discoveries — just a Python script and two bandwidth numbers. But those numbers are the payoff for days of work: driver debugging, kernel parameter tuning, virtualization architecture analysis, and container migration. The 53.76 GB/s and 40.24 GB/s measurements are the evidence that the LXC strategy succeeded, that P2P is real, and that the 8 Blackwell GPUs can finally work together as a cohesive compute fabric. For the GLM-5-NVFP4 deployment, this message marks the end of the infrastructure battle and the beginning of the performance tuning phase. It is the moment when the architecture stops being a theory and becomes a measurable reality.