NVIDIA Shows 3 Ways to Speed Up MoE Inference on DGX Spark, Lifting Ling-3.0-flash Throughput 42.5%
NVIDIA's technical blog details three optimizations for mixture-of-experts (MoE) inference on a single DGX Spark desktop system, using Ant Group's Ling-3.0-flash model. The optimizations—Humming MXFP4 MoE kernels, an FP8 LM head, and built-in MTP speculative decoding—boosted GSM8K decode throughput by 42.5% (from 37.81 to 53.89 tokens/s) with no statistically significant accuracy change. The post also highlights the memory-bound nature of MoE decoding and the integration trap when stacking FP8 LM head with MTP, requiring a shared LM-head module.
Why it matters: Provides concrete, actionable performance optimizations for running large MoE models on edge hardware, with real benchmark results and practical integration guidance.