What happened
Open-source project NTransformer released C++/CUDA inference engine running 70-billion-parameter Llama models on single 24GB RTX 3090 GPUs. Streaming model layers through PCIe bypasses CPU processing entirely via direct NVMe-to-GPU memory access. According to repository documentation, three-tier adaptive caching of VRAM, pinned RAM, and NVMe operates alongside layer-skipping calibration to reach 0.5 tokens per second for quantised 70B models. Setup requires deep system modifications, including disabling IOMMU hardware isolation and patching NVIDIA kernel modules.
Why it matters
Hardware constraints for local frontier-model inference continue to collapse. By shifting bottlenecks from VRAM capacity to PCIe bandwidth, NTransformer proves consumer hardware can execute models larger than memory footprints. Software-level memory reduction arrives one day after Hugging Face acquired llama.cpp founders, highlighting industry-wide pushes to decouple model size from expensive hardware. Platform engineers evaluating local inference must track direct-storage architectures, but must weigh severe security trade-offs of disabling IOMMU DMA protection against capital savings.
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