In this tutorial, we work with Dory 1.0 4B, a small reasoning model from NVIDIA’s Nemotron Labs whose middle layers form a shared block that can be applied once, twice, three, or four times for every token. Each extra pass, or loop, spends more computation per token without adding a single parameter, so one checkpoint can run at four different compute budgets with no retraining. We build the llama.cpp branch that NVIDIA’s model card pins, download the 3.7 GB GGUF, and read the architecture straight from the file to predict what each loop should cost in parameters and cache memory. We then check those predictions against the running server, measure decode speed at every loop count, test whether the cheaper last-loop cache changes the model’s predictions, and run a paired GSM8K evaluation to see how accuracy, answer length, and time per solved question move as the loops increase.
We build llama-server from the exact commit of the Dory branch that NVIDIA’s model card pins, because Dory support in llama.cpp is still an open draft pull request and no released binary can load the model. The cell detects the platform and passes the card’s Metal flags on a Mac, or compiles CUDA kernels for the attached GPU alone on Colab, skipping the flash-attention kernels that the card’s –flash-attn off never uses,…
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