Unverified50% confidenceFactExact time
MoE模型采用稀疏激活，Qwen 3.5 122B总参数122B但每次推理仅激活约10B，需将全部参数加载进内存，对内存容量极度敏感
1
Sources
50%
Confidence
Long-term
Relevance
7/12/2026
First Seen
Sources
AMD Ryzen AI Halo Hands-On: Can x86 Architecture Challenge NVIDIA's DGX Spark?
bilibili量子菠萝_7/7/2026
Related Claims
UnverifiedA model with 70 billion parameters that originally requires 80 GB of VRAM requires only about 40 GB of memory after 4-bit quantization (Q4_K_M level), with inference quality loss typically within 3%–8% on most benchmarks.71% similarUnverifiedTraditional LLM inference engines have severe memory fragmentation issues when handling KV Cache; for a 13B parameter model, the KV Cache for a single request may occupy several GB of memory, while the actual utilization rate may be below 50%.69% similarUnverifiedWithin the Qwen3 series there is a MoE model with a total parameter count of 2.4T and approximately 95B activated parameters, and downloading the model files requires approximately 5TB of storage space.68% similarUnverifiedCombining MoE with quantization technology can make it possible for a laptop with 16 GB of memory to run MoE models with tens of billions of parameters.68% similarUnverifiedQuantization formats can compress the runtime memory of a 70B-parameter model from 140 GB to under 40 GB.68% similar
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