Reconnaissance Before Deployment: Mapping the DGX Spark Battlefield

In the high-stakes world of multi-node LLM inference deployment, success often hinges not on the elegance of the final configuration, but on the thoroughness of the initial reconnaissance. Message [msg 6556] captures this critical phase in vivid detail: an AI assistant, tasked with deploying the Qwen3.5-122B-A10B-FP8 model across two NVIDIA DGX Spark systems, pauses to methodically inventory every relevant aspect of both machines before committing to a deployment strategy. The message is a masterclass in systematic infrastructure assessment — a sequence of parallel SSH probes and web fetches designed to build a complete mental model of the hardware, software, and networking landscape.

The Mission Context

To understand why this message matters, we must first appreciate what led to it. The user's directive in [msg 6553] was deceptively simple: deploy the same Qwen3.5-122B-A10B model that had been the subject of extensive tuning on a Proxmox-hosted RTX PRO 6000 setup (see [msg 6549]), but now on radically different hardware — two NVIDIA GB10 DGX Spark systems. Each Spark packs 120 GB of unified memory, an ARM Cortex-X925 CPU, a single Blackwell SM121 GPU, and an InfiniBand RoCE interconnect between them. This is not a conventional GPU server; it's a compact, unified-memory appliance designed for AI development, and deploying a 122-billion-parameter model across two of them requires navigating a minefield of ARM compatibility issues, Docker-based workflows, and multi-node networking challenges.

The assistant had already formulated a high-level plan in [msg 6554], complete with a structured todo list covering system exploration, recipe searching, model downloading, networking setup, and deployment. But before executing that plan, it needed data — hard, specific facts about what was actually running on these machines.

The Anatomy of a Reconnaissance Message

Message [msg 6556] opens with a concise synthesis of what the previous exploration ([msg 6555]) had revealed: "Good data. Head Spark has vLLM running using ~98GB. Second Spark has ~12GB used. Both have CUDA 13.0, driver 580.95.05, aarch64." This single sentence encapsulates the most critical findings — the head node is nearly memory-exhausted by an existing deployment, the second node has ample headroom, and both share the same CUDA/driver stack on ARM64 architecture. The assistant then declares its intent: "Let me dig deeper into both systems and fetch the key guides."

What follows is a coordinated salvo of five parallel operations, all dispatched simultaneously within the same message round:

  1. Deep inventory of the head Spark (ssh aurora@10.1.230.180): A comprehensive probe querying GPU details via nvidia-smi -q, disk space, Python version and location, CUDA toolkit presence, Docker version, running systemd services matching LLM-related keywords, active vLLM/SGLang processes, InfiniBand status via ibstat, network configuration for the 192.168.200.x subnet, and installed pip packages matching inference frameworks.
  2. Deep inventory of the second Spark (via SSH jump ssh -J aurora@10.1.230.180 aurora@192.168.200.13): A similar but slightly lighter probe covering disk space, Python, Docker, IB status, network configuration, pip packages, and running processes.
  3. Fetch the official NVIDIA SGLang guide for DGX Spark from build.nvidia.com/spark/sglang.
  4. Fetch a community vLLM setup guide from github.com/eelbaz/dgx-spark-vllm-setup. This parallel dispatch is a hallmark of the assistant's tool-use architecture: all five operations are launched in a single round, and the assistant must wait for all results before proceeding. The message is thus a snapshot of pure intent — the assistant has committed to gathering this specific set of information, but cannot act on any of it until the next round.

What the Assistant Was Looking For

Each probe was carefully chosen to answer specific questions that would shape the deployment strategy:

GPU and Memory State: The head Spark showed 104 GB used out of 119 GB total, with 15 GB available. This is critical — the existing vLLM deployment (likely serving GLM-4.7-Flash, as later context reveals) is consuming nearly all available unified memory. The Qwen3.5-122B-A10B-FP8 model weighs approximately 119 GB (as we learn from the chunk summary), which means the existing deployment must be stopped and its containers removed before the new model can even begin loading. The second Spark, with only 18 GB used and 70 GB free, has plenty of room.

CUDA and Driver Compatibility: Both systems report CUDA 13.0 and driver 580.95.05 — an extremely modern stack. This is significant because the assistant had been battling CUDA version mismatches throughout the earlier segments (see the flash-attn build struggles in segment 0). Finding a unified, modern CUDA environment on both nodes eliminates a major class of deployment failures.

Existing Infrastructure: The presence of Docker containers named vllm_node and vllm_embeddings using a vllm-node image reveals that the Sparks already have a containerized vLLM workflow. The assistant's probe for systemd services matching "vllm|sglang|llm|infer" and running processes confirms the deployment model: Docker-based, likely with some form of process supervision.

Interconnect Status: The InfiniBand query on the second Spark returns a worrying result: "State: Down, Physical state: Disabled" for the RoCE interface. This is a red flag for multi-node tensor parallelism — if the IB link between the two Sparks is down, inter-node communication will fall back to slower Ethernet, potentially making TP=2 impractical. The assistant will need to investigate and potentially reconfigure the IB link.

ARM Architecture: Both systems are aarch64 (ARM64). This constrains the available software — not all Docker images, PyTorch builds, or CUDA toolkits support ARM64. The assistant's pip package probe and Docker version check are designed to verify that the existing software stack is ARM-compatible and to identify what's already installed.

Available Recipes: The web fetches target two key resources: NVIDIA's official SGLang playbook for DGX Spark (which would provide a supported, documented path) and a community vLLM setup guide (which might offer workarounds for issues not covered by the official documentation). The assistant is hedging its bets — it doesn't yet know which inference engine will work best.

Assumptions Embedded in the Approach

The assistant's reconnaissance strategy reveals several implicit assumptions:

That the existing deployment can be safely stopped. The head Spark is running production-like services (the containers have been up for 7 weeks). The assistant assumes it has the authority to stop these services and reclaim the GPU memory. This is a reasonable assumption given the user's directive, but it's worth noting — stopping a long-running inference service could have downstream consequences if other users or automated systems depend on it.

That Docker-based deployment is the right approach. The assistant's probes focus heavily on Docker: checking Docker version, listing images and containers, looking for Docker-based guides. This is a reasonable assumption given the DGX Spark's appliance-like nature and the existing Docker workflow, but it precludes bare-metal deployment options that might offer better performance.

That the web resources will be accessible and relevant. The assistant fetches NVIDIA's official SGLang guide and a community vLLM repo without knowing their contents in advance. This is a gamble — the guide might be outdated, the repo might not support the Qwen3.5 model, or both might assume a single-node deployment. The assistant is gathering options, not commitments.

That the second Spark can reach the internet through the first. The SSH jump pattern (ssh -J aurora@10.1.230.180 aurora@192.168.200.13) confirms the second Spark is on a private subnet (192.168.200.x) behind the head Spark. The assistant's todo list in [msg 6554] noted "Second spark needs to be setup to get internet through the first one IF NEEDED." The network probes in this message are designed to validate this topology and identify the correct interfaces for inter-node communication.

The Thinking Process Visible in the Message

Although the message does not contain explicit reasoning blocks (the assistant's reasoning is embedded in the tool call construction), we can infer the cognitive process from the structure of the probes:

  1. Synthesize the high-level findings from the previous round (msg 6555).
  2. Identify gaps in the current understanding: exact GPU model, disk space, Python/CUDA versions, running services, IB status, network topology, available software.
  3. Design probes to fill each gap, parallelizing independent queries to minimize round-trips.
  4. Collect reference materials from the web to inform the next phase of decision-making.
  5. Wait — the assistant cannot act on any of this information until the next round, when all tool results return. This is a deliberate, methodical approach that prioritizes information completeness over speed. The assistant could have jumped straight to deployment based on the initial exploration, but instead chose to invest a round in deeper reconnaissance. This is the mark of an experienced operator who knows that the cost of a failed deployment (debugging, cleanup, restart) far exceeds the cost of an extra reconnaissance round.

What This Message Creates

The output of this message is not a configuration file, a running service, or a benchmark result. It is knowledge — a structured, multi-dimensional map of the deployment target that will inform every subsequent decision. Specifically, this message creates:

Conclusion

Message [msg 6556] is a textbook example of the reconnaissance phase in complex infrastructure deployment. It demonstrates that effective automation is not about executing commands blindly, but about knowing which questions to ask, in what order, and in parallel. The assistant's systematic inventory of both DGX Spark systems — GPU state, memory pressure, software stack, network topology, interconnect health, and available reference materials — builds the foundation for every decision that follows. In the next round, armed with this data, the assistant will pivot from reconnaissance to action: stopping the old GLM containers, downloading the Qwen3.5 model, and beginning the multi-node deployment that ultimately achieves ~27 tok/s throughput across the two Sparks. But none of that would be possible without the thorough groundwork laid in this single, data-rich message.