Nvidia vs. Apple Silicon: Benchmarking 70B+ LLM Inference on Consumer Hardware
A new arXiv paper (2605.00519) systematically benchmarks consumer-grade LLM inference for models exceeding 70B parameters on Nvidia Blackwell and Apple Silicon. On Nvidia, TensorRT-LLM's NVFP4 quantization achieves 151 tokens/s vs. 92 tokens/s for BF16, a 1.6x throughput gain, but introduces a 'Backend Dichotomy' trading startup latency for speed. A 'VRAM Wall' forces aggressive quantization on discrete GPUs. The study highlights ecosystem-specific trade-offs for deploying datacenter-class LLMs locally.
Key facts
- Paper arXiv:2605.00519 analyzes LLM inference on Nvidia Blackwell and Apple Silicon.
- NVFP4 quantization delivers 151 tokens/s vs. 92 tokens/s for BF16 on Nvidia Blackwell.
- TensorRT-LLM stack exhibits a 'Backend Dichotomy' between startup latency and generation speed.
- Models exceeding 70B parameters face a 'VRAM Wall' on consumer GPUs.
- Apple Silicon ecosystem is characterized for intra-architecture trade-offs.
- The study is empirical and systematic, focusing on consumer hardware.
- Nvidia Blackwell architecture uses NVFP4 quantization format.
- Paper compares performance, efficiency, and ecosystem barriers.
Entities
Institutions
- Nvidia
- Apple
- TensorRT-LLM