In this tutorial, we deploy the 1-bit Bonsai-27B language model using the PrismML fork of llama.cpp, which provides the specialized CUDA kernels required to decode the model’s Q1_0_g128 GGUF quantization format. We begin by validating the GPU runtime, installing the required Python dependencies, compiling the CUDA-enabled inference binaries, and downloading the compressed model weights from Hugging Face. We then test the model through llama-cli, launch an OpenAI-compatible local inference server, and interact with it through a reusable Python client that supports standard completions, streamed responses, multi-turn conversations, and code generation. We also examine optional configurations for throughput benchmarking, quantized key-value caching, long-context inference, speculative decoding, and multimodal extensions.
We configure the Colab workspace, model repository, server endpoint, inference parameters, context size, and GPU offloading settings required throughout the tutorial. We define a reusable shell-command function and verify that the runtime exposes a compatible NVIDIA GPU before continuing. We then install the Hugging Face Hub and HTTP client dependencies needed for model retrieval and API communication.
