Part 7/11:
- The device employs specialized hardware support for FP4 (4-bit floating point), enabling efficient model quantization that keeps models small and fast without sacrificing much quality. This means Larry can run models in FP4 mode with close-to-FP8 accuracy—a remarkable feat for such a tiny device.
Why This Matters
While Larry isn't the fastest at inference, especially on singular large models, it excels by offering:
Massive shared memory enabling multi-model and multi-task workloads
Portability — small enough to carry around, yet powerful enough to train or fine-tune models
Cost-efficiency — roughly $315/year in operational costs versus Terry’s over $1,400, due to lower power consumption and size