Strata Engine Delivers 65 Tokens/sec on 12GB VRAM via System RAM Offloading
Developer KnownAd4832 released Strata, an open-source CUDA inference engine designed to optimize large language model execution on mid-range consumer GPUs paired with DDR5 system RAM offloading. Running the quantized Qwen3.8-Flash-Next model on an RTX 5070 with 12GB VRAM and 64GB DDR5 RAM, Strata achieved up to 65.1 tokens per second generation and over 540 tokens per second prompt processing. Strata demonstrates up to a 4x speedup over llama.cpp for offloaded quantized models that exceed standard GPU VRAM capacity. This shows how hardware-tailored CUDA kernels and advanced quantization formats can significantly lower hardware barriers for local AI inference on consumer PCs. Tested on an RTX 5070 12GB, Ryzen 5 7600, and 64GB DDR5-5600 RAM, the engine leverages specialized quantization schemes including ISTA-DASLab's RCO-GSQ and standard GGUF formats (Q2_0, IQ2_XS, IQ3_XXS) across 128K context lengths. Total memory requirements for the tested model configurations range from 37.6GB to 47GB of combined RAM and VRAM, plus 0.91GB for the vision encoder.
## BACKGROUND
Quantization reduces the precision of model weights to shrink memory footprints and accelerate inference, allowing large models to fit within consumer GPUs or combined RAM/VRAM setups. Techniques like ISTA-DASLab's GSQ (Group-Sparse Quantization) and RCO (Residual Control Optimization) target per-weight mixed precision to preserve accuracy even at low bitrates such as 2-bit or 3-bit formats.