llama.cpp CPU offload reportedly tripled DeepSeek V4 Flash speed on a 4060 Ti
A Reddit report claims the 98GB DeepSeek-V4-Flash quant jumped from 2 to 7 t/s on a single 4060 Ti plus CPU, with no hardware changes between two llama.cpp builds.
According to a post on r/LocalLLaMA, a 98GB Q2_K_XL quantization of DeepSeek-V4-Flash went from roughly 2 t/s to 7 t/s on a single RTX 4060 Ti (16GB VRAM) paired with a 6-core CPU, with the reporter attributing the jump to somewhere between llama.cpp builds b9986 and b10034. No hardware changes, no new quant. A pure software delta.
That’s the context that makes this worth watching: the model is too big to fit in 16GB, so the CPU is handling expert offload for the routed MoE layers while the GPU takes the attention and dense blocks. That split is exactly where llama.cpp has been seeing active optimization work, with flags like -mla 3 for DeepSeek-architecture MLA and tensor-routing flags that keep the always-active parameters on the GPU. A 3.5x speed improvement from a build update alone, if the numbers hold across multiple runs, is a meaningful signal that the CPU offload path is improving fast right now.
The caveat: this comes from a single Reddit report, and token rates can vary with context length and batch size. The specific build range (b9986 to b10034) gives you a concrete bisect target if you want to reproduce it yourself. If you’re already running a large MoE on split GPU/CPU hardware and haven’t updated your llama.cpp build in a few weeks, it’s worth a pull.