The Moment of Verification: Reconfiguring GPU Topology for Hybrid LXC-VM Deployment

In the middle of a complex infrastructure reconfiguration spanning a Proxmox host with 8 NVIDIA RTX PRO 6000 Blackwell GPUs, the assistant produces a message that is deceptively brief yet marks a critical transition point. The message reads:

4 GPUs visible, all NUMA 0. Now update the SGLang service for TP=4:

Accompanied by an updated todo list showing five completed tasks and the implicit next step, this message represents the moment when hardware reconfiguration is verified complete and software reconfiguration begins. It is a pivot point — the fulcrum between dismantling the old topology and building the new one.

The Context: Why Split the GPUs?

To understand this message, one must understand the problem it solves. The Proxmox host (kpro6) was equipped with 8 identical RTX PRO 6000 Blackwell GPUs, each with 96 GB of VRAM, arranged across two NUMA nodes. GPUs 0–3 lived on NUMA node 0, and GPUs 4–7 on NUMA node 1. Originally, all 8 GPUs were bound to the nvidia driver and passed through to an LXC container (ID 129) running SGLang for LLM inference serving.

The user's requirement was to split this homogeneous pool: keep 4 GPUs for the LXC container (for continued SGLang inference serving), and dedicate the other 4 GPUs to a new VM with SEV-SNP (Secure Encrypted Virtualization - Secure Nested Paging) passthrough. SEV-SNP is an AMD technology for confidential computing, isolating VM memory from the hypervisor. This split would allow the same physical hardware to serve two截然 different purposes — low-latency LLM inference in the LXC, and confidential computing workloads in the VM.

The NUMA boundary was the natural split point. By keeping NUMA 0 GPUs on the nvidia driver for the LXC and moving NUMA 1 GPUs to vfio-pci for VM passthrough, the assistant ensured that each environment got a complete, coherent set of GPUs without crossing NUMA domains. This decision was made in message 6043 and executed through a sequence of precise operations: stopping the SGLang server, stopping the LXC container, unbinding four GPUs from the nvidia driver and rebinding them to vfio-pci, updating the LXC config to only mount the remaining four GPU device files, creating a new PCI mapping (pro6000-vm) for the VM-bound GPUs, and finally restarting the LXC container.

The Verification: "4 GPUs visible, all NUMA 0"

The first part of the message — "4 GPUs visible, all NUMA 0" — is a verification statement. It confirms that after all the reconfiguration steps, the result is correct. The nvidia-smi command inside the LXC (executed in message 6064) showed exactly 4 GPUs, all on NUMA node 0, with the expected PCI addresses (01:00.0, 11:00.0, 61:00.0, 71:00.0). The four GPUs that were moved to vfio-pci (81:00.0, 91:00.0, e1:00.0, f1:00.0) are no longer visible to the nvidia driver, confirming they are ready for VM passthrough.

This verification is crucial because any mistake in the reconfiguration — a GPU left behind, a wrong PCI address, a driver binding failure — would cause failures downstream. If the LXC saw 5 GPUs or 3 GPUs, or if the GPUs were on the wrong NUMA node, the SGLang service would either fail to start or perform suboptimally. The assistant's brief confirmation signals confidence that the hardware layer is correct.

The verification also implicitly confirms that the LXC's device mount configuration is correct. Earlier, the assistant had edited /etc/pve/lxc/129.conf to remove the mount entries for /dev/nvidia4, /dev/nvidia5, /dev/nvidia6, and /dev/nvidia7. The fact that only 4 GPUs appear inside the container means those edits took effect properly — the container no longer has access to the vfio-bound GPUs.

The Next Step: "Now update the SGLang service for TP=4"

The second part of the message — "Now update the SGLang service for TP=4" — declares the next action. This is a significant configuration change. Previously, the SGLang service was configured with --tp 8 (tensor parallelism across all 8 GPUs). With only 4 GPUs available, this must change to --tp 4.

Tensor parallelism in SGLang splits the model's layers across multiple GPUs, with each GPU holding a portion of every layer and communicating during inference via allreduce operations. Changing from TP=8 to TP=4 has several implications:

Model capacity: The model being deployed — Qwen3.5-122B-A10B in BF16 precision — requires approximately 234 GB of VRAM (122 billion parameters × 2 bytes per parameter, plus overhead for activations and KV cache). With 8 × 96 GB = 768 GB total VRAM, there was ample headroom. With 4 × 96 GB = 384 GB, the model still fits comfortably, but the headroom for KV cache is reduced from ~534 GB to ~150 GB, which limits the maximum number of concurrent requests that can be served.
Communication overhead: TP=4 involves fewer inter-GPU communication partners than TP=8, potentially reducing latency for individual requests. However, each GPU holds a larger portion of the model (roughly double), increasing per-GPU memory pressure and reducing the effective memory bandwidth available for compute.
Throughput characteristics: The optimal TP degree depends on the model size, GPU interconnect bandwidth, and workload characteristics. On PCIe-connected GPUs — these Blackwell GPUs lack NVLink and communicate over standard PCIe Gen5 — the communication overhead is particularly significant. Lower TP degrees can be more efficient because they reduce the volume of allreduce traffic across the PCIe fabric. The assistant's decision to simply change TP from 8 to 4 assumes that the model will still fit and perform well. This assumption would later be validated: the subsequent benchmarking in this segment showed impressive results — 108 tok/s single-stream and up to 2,800 tok/s at high concurrency (C=128).

The Todo List as a Cognitive Tool

The todo list accompanying the message reveals the assistant's systematic approach to complex infrastructure tasks. Each step is tracked with a status (completed or pending) and priority. At this moment, five tasks are marked complete:

Stop the SGLang server in LXC 129
Stop LXC 129
Bind NUMA 1 GPUs to vfio-pci
Update LXC 129 config to only mount nvidia0-3
Create PCI mapping for VM passthrough The sixth task — updating the SGLang service for TP=4 — is the implicit next step, declared in the message's prose rather than explicitly in the todo list. This todo list serves multiple purposes. It provides state management across dozens of operations spanning multiple machines (Proxmox host, LXC container, and potentially VMs), preventing the assistant from losing track of what's been done. It creates a recovery point — if an operation fails, the assistant can resume from the last completed step rather than restarting from scratch. And it serves as communication to the user, implicitly showing progress and building confidence that the plan is being executed methodically. The fact that the todo list is updated in nearly every message of this sequence shows how integral it is to the assistant's workflow. It is not a decoration — it is the scaffolding that holds the complex multi-step plan together.

Assumptions and Hidden Risks

At this point in the session, several assumptions are in play, some of which will prove incorrect:

The model fits on 4 GPUs. The Qwen3.5-122B BF16 model requires ~234 GB. With 4 × 96 GB = 384 GB available, this leaves ~150 GB for KV cache and activations. This is sufficient for moderate concurrency but could limit high-throughput serving. The assumption holds, but barely — the model leaves less than 40% of total VRAM for cache.

The LXC sees the GPUs correctly. The verification confirms device visibility, but the deeper question is whether CUDA and NCCL can properly discover and communicate across the 4 GPUs. This depends on the nvidia driver version and the CUDA runtime inside the container matching the host kernel driver.

No driver conflicts. The container's nvidia userspace components must match the host kernel module. A mismatch between the container's CUDA 12.8 userspace libraries and the host's nvidia 590 kernel driver would later surface as a problem requiring remediation.

P2P DMA works. The GPUs are connected via PCIe, and GPU-to-GPU P2P DMA is essential for tensor parallelism. The assistant assumes this works — but later in the same chunk, a critical issue emerges. The SEV-SNP configuration on the host enables full IOMMU translation (amd_iommu=on), which breaks P2P DMA. Every P2P transfer produces corrupted data, causing NCCL to hang during init_torch_distributed. The fix — adding NCCL_P2P_DISABLE=1 to force NCCL to use SHM (shared memory) transport — avoids the hardware issue but may impact performance for cross-GPU communication.

The most significant hidden risk at this moment is the P2P DMA corruption. The assistant doesn't yet know that NCCL will hang. The verification that 4 GPUs are visible is necessary but not sufficient — it confirms device enumeration but not inter-device communication. The real test will come when the SGLang server attempts to initialize distributed PyTorch across the 4 GPUs.

The Broader Significance

This message, for all its brevity, represents a critical infrastructure decision: the moment when a homogeneous pool of 8 identical GPUs is deliberately fractured into two heterogeneous environments serving different purposes. This is not a failure recovery or a workaround — it is a deliberate architectural choice to maximize hardware utilization across different workload types.

The decision to split by NUMA node rather than by some other criterion (alternating GPUs, or by PCIe topology) reflects a deep understanding of NUMA effects on GPU performance. Keeping all GPUs within a single NUMA domain for each workload minimizes cross-NUMA memory access penalties, which can significantly impact both training and inference throughput. On AMD EPYC platforms (which this Proxmox host likely uses, given the AMD IOMMU and SEV-SNP references), cross-NUMA memory access can add substantial latency.

Moreover, the message exemplifies the assistant's methodical, plan-driven approach to infrastructure management. Rather than making changes ad-hoc and hoping for the best, each step is planned, executed, verified, and tracked. The todo list is the visible manifestation of this discipline. The brief verification statement — "4 GPUs visible, all NUMA 0" — is the culmination of a multi-step sequence that could have gone wrong at any point.

Conclusion

Message 6065 is a quiet moment of confirmation in a session full of complex operations. "4 GPUs visible, all NUMA 0. Now update the SGLang service for TP=4" — eleven words that encapsulate the successful completion of a hardware reconfiguration and the clear direction for the next phase. Behind those words lies a sequence of precise operations: unbinding PCI devices, updating LXC configs, creating PCI mappings, and verifying correctness. Ahead lies the software reconfiguration that will bring the new topology to life.

The message also embodies a truth about complex infrastructure work: the most important messages are often the shortest ones. They mark the moments when uncertainty resolves into certainty, when the plan survives contact with reality, and when the next step becomes clear. This message is one such moment — a pivot point between what was and what will be. The verification is complete; the reconfiguration begins.