REDDIT · REDDIT// 31d agoINFRASTRUCTURE

LocalLLaMA asks if MoE offload can make 120B models practical on a 12GB RTX 4070

A Reddit thread in r/LocalLLaMA asks whether models like GPT-OSS 120B or Qwen3.5-122B-A10B can run efficiently by keeping only active MoE experts and part of the KV cache in a 12GB RTX 4070 while pushing the rest into system RAM. It is a practical local-inference discussion about memory placement, bandwidth limits, and whether partial offload can still deliver high token speeds on consumer hardware.

// ANALYSIS

This is the kind of nuts-and-bolts question that matters more than benchmark theater for local AI builders: MoE makes selective GPU placement plausible, but real speed still lives or dies on memory bandwidth and cache residency.

–MoE models activate only a fraction of total parameters per token, which is why users are asking if a small GPU can carry just the hot path
–In practice, fitting experts into VRAM does not guarantee 70-100 tok/s if KV cache or weight movement keeps spilling over PCIe and system RAM
–The discussion is more about inference engineering than model quality, especially for setups built around a 12GB card like the RTX 4070
–Tools such as llama.cpp and vLLM have made hybrid CPU/GPU placement more common, but performance depends heavily on quantization, cache size, and backend support
–Useful thread for local LLM operators, but it is a community help post rather than a launch, release, or formal technical guide

// TAGS

gpt-oss-120bqwen3-5-122bllmgpuinference

DISCOVERED

31d ago

2026-03-11

PUBLISHED

34d ago

2026-03-08

RELEVANCE

6/ 10

AUTHOR

9r4n4y