The infrastructure reality is that 'open-weight' does not mean easy to run: a 2.4T-class MoE like Qwen3.8-Max is not a commodity local model, requiring massive memory and accelerator counts similar to Kimi K3.

Jamin Ball argued that pricing comparisons were overstated because 'vanilla' token prices ignore token efficiency and because these models are enormous: Qwen 3.8 Max >2T params; Kimi K3 ~104B active per token; GLM 5.2 = 744B total, 40B active. For K3, loading weights alone is >1TB memory. Requires at least 8 H100/B200 GPUs to run. Moonshot recommends 64+ accelerators in supernode-style setups. This same critique implicitly applies to Qwen3.8-Max, even if its active-parameter count is somewhat lower than K3's: a 2.4T-class MoE is not a commodity local model. StableQuan made the practical version of the same point more bluntly: long, RAM-heavy prompts and slow tool calls make giant models painful on consumer hardware, recommending API use instead.

Read full article ↗excerpt · fair-use quotation