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.
import os
import sys
import time
import json
import shutil
import subprocess
import multiprocessing
WORK_DIR = "/content"
REPO_URL = "https://github.com/PrismML-Eng/llama.cpp"
REPO_DIR = os.path.join(WORK_DIR, "llama.cpp")
BUILD_DIR = os.path.join(REPO_DIR, "build")
BIN_DIR = os.path.join(BUILD_DIR, "bin")
HF_REPO = "prism-ml/Bonsai-27B-gguf"
MODEL_FILE = "Bonsai-27B-Q1_0.gguf"
MODEL_PATH = os.path.join(WORK_DIR, MODEL_FILE)
SERVER_HOST = "127.0.0.1"
SERVER_PORT = 8080
SERVER_URL = f"http://{SERVER_HOST}:{SERVER_PORT}"
GEN_PARAMS = {"temperature": 0.7, "top_p": 0.95, "top_k": 20}
CTX_SIZE = 8192
N_GPU_LAYERS = 99
USE_KV_Q4 = False
def sh(cmd, check=True, **kw):
"""Run a shell command, streaming output to the notebook."""
print(f"n$ {cmd}")
return subprocess.run(cmd, shell=True, check=check, **kw)
print("=" * 70)
print("[1/7] Checking environment")
print("=" * 70)
gpu = subprocess.run("nvidia-smi --query-gpu=name,memory.total --format=csv,noheader",
shell=True, capture_output=True, text=True)
if gpu.returncode != 0:
sys.exit("No GPU detected. In Colab: Runtime -> Change runtime type -> GPU (T4).")
print(f"GPU detected: {gpu.stdout.strip()}")
print("Bonsai-27B needs only ~5.2 GB peak at 4K context — any Colab GPU works.")
sh("pip -q install huggingface_hub requests")
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.
print("=" * 70)
print("[2/7] Building PrismML llama.cpp fork with CUDA (cached after 1st run)")
print("=" * 70)
if not os.path.isdir(REPO_DIR):
sh(f"git clone --depth 1 {REPO_URL} {REPO_DIR}")
else:
print("Repo already cloned — skipping.")
cli_bin = os.path.join(BIN_DIR, "llama-cli")
server_bin = os.path.join(BIN_DIR, "llama-server")
bench_bin = os.path.join(BIN_DIR, "llama-bench")
if not (os.path.exists(cli_bin) and os.path.exists(server_bin)):
jobs = multiprocessing.cpu_count()
sh(f"cmake -S {REPO_DIR} -B {BUILD_DIR} -DGGML_CUDA=ON -DCMAKE_BUILD_TYPE=Release")
sh(f"cmake --build {BUILD_DIR} -j{jobs} --target llama-cli llama-server llama-bench")
else:
print("Binaries already built — skipping.")
We clone the PrismML fork of llama.cpp, which provides the specialized kernels required for the model’s Q1_0_g128 quantization format. We configure a CUDA-enabled release build with CMake and compile the command-line, server, and benchmarking executables. We also reuse previously generated binaries when they already exist, reducing repeated setup time in the same Colab session.
print("=" * 70)
print("[3/7] Downloading weights from Hugging Face")
print("=" * 70)
from huggingface_hub import hf_hub_download
if not os.path.exists(MODEL_PATH):
downloaded = hf_hub_download(repo_id=HF_REPO, filename=MODEL_FILE,
local_dir=WORK_DIR)
print(f"Downloaded to: {downloaded}")
else:
print("Model already on disk — skipping.")
print(f"Model size on disk: {os.path.getsize(MODEL_PATH) / 1e9:.2f} GB")
We connect to the Hugging Face Hub and download the Bonsai-27B GGUF model into the Colab workspace. We skip the transfer when the model file is already available locally, allowing subsequent runs to proceed more efficiently. We then calculate and display the deployed model size to confirm that the compressed weights are stored correctly.
print("=" * 70)
print("[4/7] Smoke test with llama-cli")
print("=" * 70)
sh(
f'{cli_bin} -m {MODEL_PATH} '
f'-p "Explain in two sentences why 1-bit quantization saves memory." '
f'-n 128 -ngl {N_GPU_LAYERS} '
f'--temp {GEN_PARAMS["temperature"]} '
f'--top-p {GEN_PARAMS["top_p"]} --top-k {GEN_PARAMS["top_k"]} '
f'-no-cnv 2>/dev/null',
check=False,
)
print("=" * 70)
print("[5/7] Starting llama-server (OpenAI-compatible API)")
print("=" * 70)
import requests
kv_flags = "-ctk q4_0 -ctv q4_0" if USE_KV_Q4 else ""
server_cmd = (
f"{server_bin} -m {MODEL_PATH} "
f"--host {SERVER_HOST} --port {SERVER_PORT} "
f"-ngl {N_GPU_LAYERS} -c {CTX_SIZE} {kv_flags}"
)
print(f"$ {server_cmd} (background)")
server_log = open(os.path.join(WORK_DIR, "server.log"), "w")
server_proc = subprocess.Popen(server_cmd, shell=True,
stdout=server_log, stderr=server_log)
for _ in range(120):
try:
if requests.get(f"{SERVER_URL}/health", timeout=2).status_code == 200:
print("Server is up.")
break
except requests.exceptions.RequestException:
pass
time.sleep(2)
else:
server_proc.kill()
sys.exit("Server failed to start — check /content/server.log")
We perform a command-line smoke test to verify that the compiled runtime can load the quantized model and generate a valid response. We then start llama-server with full GPU layer offloading, the selected context window, and optional quantized KV-cache settings. We repeatedly query the health endpoint until the OpenAI-compatible inference service becomes ready for client requests.
print("=" * 70)
print("[6/7] Talking to Bonsai-27B via the OpenAI-compatible API")
print("=" * 70)
def chat(messages, stream=False, max_tokens=512, **overrides):
"""Minimal OpenAI-compatible chat client for the local llama-server."""
payload = {
"model": "bonsai-27b",
"messages": messages,
"max_tokens": max_tokens,
"stream": stream,
**GEN_PARAMS,
**overrides,
}
if not stream:
r = requests.post(f"{SERVER_URL}/v1/chat/completions", json=payload)
r.raise_for_status()
return r.json()["choices"][0]["message"]["content"]
r = requests.post(f"{SERVER_URL}/v1/chat/completions", json=payload, stream=True)
r.raise_for_status()
full = []
for line in r.iter_lines():
if not line or not line.startswith(b"data: "):
continue
chunk = line[len(b"data: "):]
if chunk == b"[DONE]":
break
delta = json.loads(chunk)["choices"][0]["delta"].get("content", "")
full.append(delta)
print(delta, end="", flush=True)
print()
return "".join(full)
SYSTEM = {"role": "system", "content": "You are a helpful assistant"}
print("n--- 6a: basic completion ---")
answer = chat([SYSTEM, {"role": "user",
"content": "What is the capital of France? One sentence."}])
print(answer)
print("n--- 6b: math reasoning, streamed token-by-token ---")
chat([SYSTEM, {"role": "user",
"content": "A train travels 120 km at 80 km/h, then 90 km at "
"60 km/h. What is its average speed for the whole "
"trip? Show your reasoning briefly."}],
stream=True, max_tokens=700)
print("n--- 6c: multi-turn chat ---")
history = [SYSTEM]
for user_msg in ["My name is Priya and I love graph algorithms.",
"Suggest one project idea that combines my interest with LLMs.",
"What was my name again?"]:
history.append({"role": "user", "content": user_msg})
reply = chat(history, max_tokens=300)
history.append({"role": "assistant", "content": reply})
print(f"nUSER: {user_msg}nBONSAI: {reply}")
print("n--- 6d: code generation ---")
print(chat([SYSTEM, {"role": "user",
"content": "Write a Python function that returns the n-th "
"Fibonacci number using memoization. Code only."}],
max_tokens=400))
We define a reusable Python chat client that sends OpenAI-compatible requests to the locally hosted Bonsai-27B server. We support both standard and server-sent-event streaming responses while applying the configured temperature, top-p, and top-k sampling parameters. We then evaluate basic factual answering, mathematical reasoning, multi-turn conversational memory, and Python code generation.
print("=" * 70)
print("[7/7] Optional extras")
print("=" * 70)
RUN_BENCHMARK = False
if RUN_BENCHMARK:
sh(f"{bench_bin} -m {MODEL_PATH} -ngl {N_GPU_LAYERS}", check=False)
print("""
NOTES & NEXT STEPS
------------------
* Long context: the model supports up to 262K tokens. On Colab, raise
CTX_SIZE and set USE_KV_Q4 = True (4-bit KV cache) — with it, 100K-token
contexts fit in roughly 6.8 GB peak, well inside a T4's 16 GB.
* Speculative decoding: the repo ships a DSpark drafter (Q4_1, ~1.79 GB)
that gives a lossless ~1.37x decode speedup on CUDA. See the PrismML
llama.cpp fork's README for the serving flags, and download the drafter
pack from the same HF repo if you want to try it.
* Vision: an optional ~0.63 GB mmproj pack adds image input; it is only
loaded when an image arrives, so text-only serving never pays for it.
* Quality vs size: if you want more headroom, the ternary sibling
(prism-ml/Ternary-Bonsai-27B-gguf, ~5.9 GB, ~95% of FP16) is a drop-in
swap — just change HF_REPO / MODEL_FILE above.
* Shutting down: run server_proc.kill() in a later cell to free the GPU.
""")
print("Done. The server is still running — call chat([...]) from new cells!")
We expose an optional benchmarking switch that measures prompt-processing and token-generation performance with the compiled llama-bench executable. We review advanced deployment options, including long-context inference, 4-bit KV caching, speculative decoding, vision support, and a higher-capacity ternary model variant. We finish while keeping the server process active so that we can continue calling the chat function from additional Colab cells.
In conclusion, we established a complete local inference workflow for running Bonsai-27B. We used the PrismML implementation to preserve compatibility with the model’s highly compressed 1.125-bit weight representation while keeping the full inference pipeline accessible through both command-line and OpenAI-compatible interfaces. We validated reasoning, conversational memory, streaming generation, and programming capabilities, while retaining control over sampling parameters, context length, GPU offloading, and KV-cache precision. This setup gives us a platform for experimenting with low-bit large language models, evaluating their efficiency and output quality, and integrating them into Python applications without relying on an external hosted inference service.
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Sana Hassan, a consulting intern at Marktechpost and dual-degree student at IIT Madras, is passionate about applying technology and AI to address real-world challenges. With a keen interest in solving practical problems, he brings a fresh perspective to the intersection of AI and real-life solutions.
