Qwen3.6-Next 35B-A3B on a single DGX Spark: 25.5 → 45.2 tok/s
1× GB10 · 16-bit · temp 0 medians · per the methodology
The most useful benchmark for most Spark owners is not the biggest model — it is what a strong mid-size MoE does on one box, and how far the stock config is from the real ceiling. Answer: a long way. Same silicon, same weights, config changes only:
| config | dialogue | code |
|---|---|---|
| out of the box (eager) | 25.5 | 25.5 |
| + launch-overhead fix | 29.7 | 29.5 |
| + decoding-path tuning | 41.2 | 45.2 |
That is +77% on code from configuration alone — no new hardware, no different weights. The winning settings differ from model to model and have to be measured, not copied.
The multi-node myth, addressed
This entry is deliberately single-node. For a model this size, adding nodes does nearly nothing for single-stream speed — clustering pays for models too big for one box, and for aggregate throughput. If someone tells you to buy a second Spark to make a 35B model chat faster, they have not measured it.
What the gap means
Most Spark owners are running the 25.5 line and comparing it to reviews. The 45.2 line is the same computer. The difference is knowing what to configure and how to verify the win survives restarts — two of the standard optimizations silently cancel each other on many stacks.
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