I’m using ONNX Runtime with 4-bit quantization on a Raspberry Pi 4.
I preload the quantized model into shared memory so multiple processes can reuse it.
Evict old sessions by LRU when I hit a 1 GB RAM cap.
For batching, I accumulate inputs over 50 ms to boost throughput without hurting latency.
So far I get ~15 RPS on a 7 B Llama 2 model.
Your 4060 Ti with 16 GB is perfect for 1080p gaming and light AI work—extra memory really helps with things like Stable Diffusion or small LLMs. The RTX 5060’s GDDR7 is faster, but 8 GB can fill up quickly under those loads. AMD’s new RX 7600/7700 cards with 12–16 GB and better ROCm support might be a solid non-Nvidia option.
Absolutely—adding more nuclear plants helps power AI today, but we also have to keep funding fusion research. Fusion could give us clean, almost endless energy down the road. We need reliable nuclear now and fusion in the future.
I crashed recently after someone in front applied break too hard and too late. Couldn’t walk for two weeks—something like BrakeBright would’ve been a gamechanger. How do you prevent false triggers on bumpy roads though?