A Reddit user benchmarked Qwen3.8 27B on an RTX 5070 Ti laptop with 12GB VRAM: the 2-bit quantized version hit 35.9 chars/sec but flunked the classic "1.1 yuan small ball" problem; the 3-bit version answered all six questions correctly but throughput dropped to 7.5 chars/sec. Swapping in Qwen3.6 35B MoE, the result was 59 chars/sec and a clean sweep across all six questions—last generation wins.

What this is

The tester compared two models. Qwen3.8 27B is a new dense model from Alibaba (every inference activates all parameters), quantized to both 2-bit and 3-bit (compressing model precision to save VRAM). Qwen3.6 35B-A3B uses a MoE architecture (Mixture of Experts: 35B total params but only ~3B active per inference), quantized to 4-bit. Across six common-sense questions plus one coding task: 27B Q2 missed one question but ran fast; 27B Q3 answered all correctly but slow enough to stall; the MoE 4-bit ran fastest and nailed every answer.

Industry view

Supporters see this as proof that MoE is the real path for local deployment—on the same VRAM, you can run smarter and faster models. For finance, medical, and government institutions that cannot upload data to the cloud, private AI assistants just became genuinely viable.But engineers also push back: a single-respondent Reddit test carries too little weight, and only llama.cpp was tested as an inference framework. MoE's expert routing demands careful VRAM scheduling, so the deployment barrier for average users isn't actually low. If the Qwen3.8 team ships a 4-bit-optimized build in the next release, the results could flip.

Impact on regular people

For enterprise IT: 12GB-class VRAM can now host a usable local LLM, lowering compliance costs in data-sensitive industries.For working professionals: handling customer data, contracts, and internal memos no longer requires uploading to third-party servers, and the deployment bar for non-technical users is dropping.For the consumer market: GPU vendors and model labs are fighting over the same narrative—"run the strongest model on your own machine." The next generation of consumer hardware is worth watching.