Before this test, I ran Qwen3.8-27B NVFP4 on the same DGX Spark. It gave 11.2 tokens each second. Qwen3.8-Flash-Next has 180 billion parameters. It uses 7.31 billion parameters for each token. It also has a built-in draft head for speculative decoding. The result is 43.9 tokens each second. That is four times the speed of the 27B model. The model is 6.7 times larger.

**Configuration:** `nvidia/Qwen3.8-Flash-Next-NVFP4`. Server: vLLM, tensor parallel size 1. Key-value cache: FP8. MTP depth: 3. Thinking mode: off. Sampling: greedy. Each test wrote 320 tokens. Hardware: GB10 Blackwell, 121.7 GiB unified memory, 20-core Grace CPU. I measured all numbers on this machine.

**How the model fits:** The checkpoint holds 123.53 GiB of tensor data. The memory is 121.7 GiB. The PLE n-gram table holds 51.2 billion parameters and occupies 47.68 GiB. This table does not enter memory. Each token reads only 16 rows. vLLM reads those rows from the NVMe disk when it needs them. The 512 experts hold 120.8 billion parameters. They occupy 56.25 GiB at NVFP4 and stay in memory.

**Functions I tested on the live server:** Tool calls work. No text leaked into the reply. Thinking mode works. Image input works. The server accepted video input. My test clip was faulty, so that result is not conclusive, not testing this for now.

Has anyone run this on DGX spark? Anything i need to consider while using it or things i should try?

Qwen3.8-Flash-Next (180B MoE) on one DGX Spark: 43.9 tok/s. The same machine gave 11.2 tok/s with Qwen3.8-27B dense model.