OPEN LINK ↗
// 77d 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
77d ago
2026-03-11
PUBLISHED
80d ago
2026-03-08
RELEVANCE
6/ 10
AUTHOR
9r4n4y