The Fork in the Road: Researching Deployment Options for Qwen3.5 Across Dual DGX Sparks

In the sprawling narrative of a multi-month ML infrastructure project, most messages are execution—running commands, fixing errors, verifying outputs. But occasionally a message arrives that is pure decision support: a moment where the assistant pauses the forward momentum to gather intelligence before committing to a path. Message [msg 6568] is exactly such a moment. It is a research-and-reconnaissance message that sits at a critical inflection point in the deployment of Qwen3.5-122B-A10B-FP8 across two NVIDIA DGX Spark nodes, and understanding it requires unpacking the complex technical landscape that led to this juncture.

The Context: A Clean Slate and an Urgent Question

To understand why this message was written, we must trace the events of the preceding minutes. The assistant had been tasked with deploying the Qwen3.5-122B-A10B-FP8 model—a 125-billion-parameter Mixture-of-Experts architecture quantized to FP8—across two DGX Spark systems connected via InfiniBand RoCE. The head Spark (spark-1a32) was running a Docker-based vLLM instance serving GLM-4.7-Flash-NVFP4, along with embeddings and reranker services. The second Spark (spark-5be3) was running its own embeddings vLLM.

The user's directive in [msg 6561] was crisp: stop the GLM model on the head, keep the embeddings on the secondary. The assistant executed this cleanly across [msg 6565] and [msg 6566], stopping the vllm-cluster, vllm-proxy, vllm-embeddings, and vllm-reranker services. By [msg 6567], the head GPU was down to ~44 MB of display-only usage—a pristine canvas.

But a critical discovery had been made in [msg 6560]. When the assistant probed the existing vLLM Docker image (v0.14.0rc2.dev262) for Qwen3.5 support, the results were stark: the registry contained Qwen3ForCausalLM, Qwen3MoeForCausalLM, Qwen3VLForConditionalGeneration—but no Qwen3_5MoeForConditionalGeneration. A direct import of vllm.model_executor.models.qwen3_5_moe failed with "No module found." The existing infrastructure simply could not serve the target model.

This is the precise moment [msg 6568] was born. The assistant had cleared the decks, freed the GPU memory, and now faced a fundamental architectural question: What serving framework can actually run Qwen3.5 on these DGX Sparks?

The Message: Three Parallel Investigations

The message dispatches three tool calls simultaneously, reflecting a sophisticated parallel-search strategy:

Tool 1: Web search for SGLang spark images. The query "SGLang Docker image DGX Spark lmsysorg/sglang:spark Qwen3.5 support 2026" targets a specific known image (lmsysorg/sglang:spark) that NVIDIA had previously promoted for DGX Spark deployments. The search returns two promising leads: a NVIDIA Developer Forums post about "New pre-built sglang Docker Images for NVIDIA DGX Spark" and a GitHub repository ridanuae/dgx-spark-sglang-qwen35—the latter being particularly suggestive, as its name explicitly combines DGX Spark, SGLang, and Qwen3.5.

Tool 2: Docker pull of the SGLang spark image. While the web search runs, the assistant simultaneously attempts to pull lmsysorg/sglang:spark onto the head Spark. This is a bold move—pulling a multi-gigabyte Docker image over the network without knowing if it supports the target model. The command times out after 120 seconds (the bash tool's timeout limit), returning no output. This timeout is itself informative: it suggests the image is large, the network connection to the registry is slow, or the registry is unreachable from this environment.

Tool 3: Environment readiness check. The third tool call runs three checks on the head Spark: whether the HuggingFace CLI is installed (it is not—command not found), available disk space (3.0 TB free on a 3.7 TB drive), and whether the Qwen3.5 model is already cached (it is not). This trio of checks establishes the baseline for the download operation that must follow.

The Reasoning: Why These Three Tools in Parallel?

The assistant's reasoning, visible in the preceding messages, reveals a deliberate strategy. The core insight is that the assistant is trying to parallelize two independent workstreams: researching the deployment approach (web search + Docker pull) and preparing for model acquisition (environment checks). These are not dependent on each other—the assistant can investigate SGLang viability while simultaneously verifying that the head Spark has the disk space and tooling to download the 119 GB model.

The Docker pull is particularly interesting as a strategic choice. Rather than first confirming that lmsysorg/sglang:spark supports Qwen3.5, the assistant starts the pull immediately. This reflects an assumption that even if this particular image doesn't work, having it available locally will be useful for other Spark-related work. It also reflects a time-risk calculation: Docker pulls are slow, so starting early minimizes future waiting. The timeout is an acceptable failure mode—it simply means the image wasn't pulled in this round, and the assistant can retry or pivot in the next round.

Assumptions Embedded in This Message

Several assumptions underpin the actions in [msg 6568]:

First, the assistant assumes that SGLang is a viable alternative to vLLM. This is a reasonable assumption given that SGLang has been used throughout the broader session (segments 37-41) for various model deployments, and the NVIDIA forums specifically promote SGLang for DGX Spark. However, it is not yet confirmed that the SGLang spark image supports the Qwen3.5 architecture, which is a relatively new model requiring bleeding-edge framework support.

Second, the assistant assumes the head Spark has internet access to Docker Hub. The Docker pull command is issued without checking registry connectivity first. While the head Spark clearly has internet (it was used for earlier web searches), Docker Hub may have different accessibility characteristics—rate limits, authentication requirements, or network filtering. The timeout could indicate any of these issues.

Third, the assistant assumes the HuggingFace CLI is the right tool for model download. The check for huggingface-cli is a standard approach, but the assistant doesn't immediately pivot to alternatives (like git clone of the HuggingFace repo, or wget of individual model files). This assumption proves mildly incorrect—the CLI is not installed—but the information is valuable nonetheless.

Fourth, and most subtly, the assistant assumes that a single serving framework will handle both nodes. The search is for a Docker image that can run on both Sparks, implicitly assuming symmetric deployment. The alternative—running different frameworks on each node with some custom networking layer—is not explored in this message.

Input Knowledge Required to Understand This Message

A reader needs substantial context to grasp the significance of this message:

The DGX Spark hardware: NVIDIA GB10 with 120 GB unified memory, ARM Cortex-X925 CPU, SM121 Blackwell GPU architecture. This is not a standard GPU server—it's a compact desktop supercomputer with unified CPU-GPU memory, which constrains model sizing and framework compatibility.
The Qwen3.5-122B-A10B-FP8 model: A Mixture-of-Experts architecture with 125B total parameters but only 10B active per token, quantized to FP8. The model requires approximately 119 GB of storage and, critically, framework support for the Qwen3_5MoeForConditionalGeneration architecture—support that was only added in very recent versions of vLLM and SGLang.
The existing vLLM 0.14 limitation: Discovered in [msg 6560], the Docker image running on the Sparks has vLLM 0.14.0rc2, which supports Qwen3 but not Qwen3.5. This gap is the entire reason this research message exists.
The InfiniBand interconnect: The two Sparks are connected via 40 Gbps InfiniBand RoCE (RDMA over Converged Ethernet) on the 192.168.200.x subnet. Multi-node tensor parallelism requires this high-bandwidth, low-latency link, and the serving framework must support NCCL communication over it.
The broader session history: This deployment is part of a long-running effort spanning segments 37-42, involving everything from EAGLE-3 speculation benchmarking to Blackwell kernel patching to multi-node Ray orchestration. The assistant has deep institutional knowledge of what works and what doesn't on this hardware.

Output Knowledge Created

This message produces several pieces of actionable intelligence:

Confirmed: SGLang spark images exist and may support Qwen3.5. The web search returns the ridanuae/dgx-spark-sglang-qwen35 repository, which is a strong signal that someone has already solved the Qwen3.5-on-DGX-Spark problem using SGLang. This becomes the lead candidate for the deployment approach.

Confirmed: The lmsysorg/sglang:spark image is large or the connection is slow. The 120-second timeout means this image cannot be pulled in a single tool round. The assistant will need to either increase the timeout, use a different image, or pre-pull it in a background process.

Confirmed: No HuggingFace CLI, no cached model, plenty of disk space. The head Spark has 3.0 TB free—more than enough for the 119 GB model. But the model must be downloaded from scratch, and the download tooling (huggingface-cli or equivalent) must be installed first.

Negative result: The Docker pull timed out. This is not a failure—it's data. The assistant now knows that pulling images to the Spark requires either a longer timeout, a smaller image, or a different registry strategy (perhaps pulling on the host and then loading into Docker).

The Thinking Process: A Window into Strategic Decision-Making

The assistant's reasoning in the preceding messages reveals a sophisticated mental model of the deployment landscape. In [msg 6559], the assistant explicitly weighs the vLLM versus SGLang question:

"But wait—the user wants to deploy Qwen3.5-122B-A10B, which is a hybrid GDN model. On the kpro6 setup, we needed SGLang with --attention-backend triton because flashinfer doesn't support hybrid GDN architectures."

This cross-reference to the kpro6 setup (from earlier segments) shows the assistant drawing on past experience: flashinfer doesn't support hybrid GDN architectures, so SGLang with the Triton attention backend was required. This historical knowledge directly informs the current research direction.

Similarly, in [msg 6558], the assistant performs a detailed memory budget analysis:

"Now I'm checking what FP8 versions are actually available... the model is a MoE architecture, so in FP8 it should be roughly 122GB, which with tensor parallelism across 2 nodes means about 61GB per node—leaving roughly 59GB for KV cache per node."

This calculation—122 GB / 2 nodes = 61 GB per node, leaving 59 GB for KV cache from the 120 GB unified memory pool—demonstrates the assistant's understanding of the memory constraints that will govern the deployment regardless of which framework is chosen.

What Happens Next

The results of [msg 6568] set the stage for the next round. The assistant now knows that SGLang is the more promising path (the ridanuae/dgx-spark-sglang-qwen35 repo is a strong signal), that the official SGLang spark image is too large to pull quickly, and that the model must be downloaded fresh. The next message will likely involve pivoting to the community SGLang image, installing HuggingFace tooling, and beginning the model download—all while keeping the second Spark's embeddings service running as requested.

This message, then, is not about execution. It is about orientation—taking stock of the landscape before committing resources. In the high-stakes world of multi-node ML deployment, where a single wrong framework choice can waste hours of download and setup time, such orientation is invaluable. [msg 6568] is the calm before the storm of actual deployment, the moment when the assistant gathers its intelligence before committing to a path.