@VedaAI00: When Transformers Are Hardwired Into Hardware, Inference Speed Changes Orders of Magnitude. Luthira demonstrates a complete implementation of MicroGPT on an FPGA array. No GPU, no PyTorch, not even a CPU inference loop, just a pure hardware-driven Tra…
Summary
Luthira fully implements MicroGPT on an FPGA array, without GPU, PyTorch, or CPU inference loop, achieving inference speeds of 50,000+ tokens per second, demonstrating the potential of hardware-based Transformers.
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Cached at: 05/24/26, 08:36 PM
When a Transformer is directly “burned” into hardware, the order of magnitude of inference speed changes.
Luthira demonstrates a fully implemented MicroGPT on an FPGA array. No GPU, no PyTorch, not even a CPU inference loop — just a pure hardware-driven Transformer. Measured performance hits an astonishing 50,000+ tokens per second.
This “hardware-inference” approach hints at a trend: AI models are shedding their bloated software stacks.
In the future, the fastest inference may no longer happen inside a Python script, but in purpose-built chip logic.
I am “谁是藏镜人”, focused on uncovering the hardcore evolution of AI at the underlying engineering level.
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