The blazux/qwen3.8-Flash-DGX recipe memory-maps that table read-only from NVMe and fetches only the required rows per token, keeping it out of shared memory.
https://t.co/GRlgUfIFEv
Running RadixArk/Qwen3.8-Flash-Next-NVFP4 on one 128GB DGX Spark with vLLM: TP=1, MTP=2, 262K context, one concurrent sequence.
It uses roughly 97 GiB of unified memory , including ~79.4 GiB model weights and ~14.3 GiB KV cache.
Continuous coding reaches 30+ tokens/s.
The checkpoint occupies ~126 GiB on disk, plus ~19 GiB for the Docker/vLLM image: approximately 145 GiB total.
The checkpoint includes a ~47.7 GiB FP8 n-gram/PLE table.
Apple’s M5 Ultra and NVIDIA’s DGX Spark are worth comparing, but chasing the newest AI hardware is a loser’s game. Use your current setup until it hits a real bottleneck. Upgrade only when the gain justifies the cost. I bought DGX Spark to learn—and it’s doing exactly that.