Lamb Labs
Builds Model Processing Units (MPUs) that hardcode AI models, including weights, directl
Lamb Labs develops Model Processing Units (MPUs), a hardware approach that hardcodes an entire model, including its weights, into silicon so the model itself becomes the chip. The company states this addresses the memory-bandwidth bottleneck GPUs face, targeting 20,000+ tokens per second. Work spans software, FPGA fabric, and custom silicon.
The product line includes Larry, a one-bit quantized version of Qwen3.8 27B sized to fit on-chip, and Little Lamb, a small language model trained from scratch with its weights burned into FPGA fabric for on-chip token generation. An FPGA accelerator prototype runs on a Kria KV260 board, and the roadmap includes custom ASICs with model architecture hard-coded on-chip, alongside an RL environment that searches chip designs for speed and energy per token.
Lamb Labs targets teams working on fast model inference across software and hardware. The homepage offers live demos of Larry and the FPGA prototype, with a contact option for custom silicon work; no pricing or packaging details are stated.
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