AI model
Multimodal mixture-of-experts model released by Qwen.
Strata runs a 125B-class Qwen model at about 100 tok/s on one RTX 4090 because its sparse MoE activates only about 6B parameters per token. The design moves the hardware bill to system RAM, and the 100 tok/s figure holds mainly for code and structured output.
Reality
Salvatore Sanfilippo's open-source ds4 engine runs a short list of large open-weight models locally by compressing their routed experts to about two bits. Even compressed, the supported builds need high-memory Macs or GPU systems that most people do not already own.
Reality
VIDRAFT says POCKET-Darwin-180B, a 111 GB 4-bit GGUF build of its 180B mixture-of-experts model, runs in llama.cpp on about $1,400 of consumer hardware. The accuracy evidence so far is one MMLU-Pro comparison that VIDRAFT reports itself.
Reality
GLM-5.3-Flash leads agentic terminal work, DeepSeek V4 Flash is billed as the cheapest per token, and a 2.52B MiniCPM5-2B runs locally under Apache 2.0. The comparison flags most of those numbers as vendor-reported.
Reality
Alibaba's Qwen3.8-Flash-Next preview activates 6B of its 125B parameters per token. Per-token compute drops to under a quarter of the dense 27B's, and about 125GB of weights still has to stay on device.
Reality
Qwen3.8-Flash-Next puts 36 Gated DeltaNet layers and 12 sparse-attention layers on Hugging Face, which means the retrieval budget Qwen4 will inherit is something you can measure against your own traces now.
Perspective Coverage
5 publishersReality