Instructions to use diffuse-cpp/LLaDA-8B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use diffuse-cpp/LLaDA-8B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="diffuse-cpp/LLaDA-8B-Instruct-GGUF",
	filename="llada-8b-q4km.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use diffuse-cpp/LLaDA-8B-Instruct-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf diffuse-cpp/LLaDA-8B-Instruct-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf diffuse-cpp/LLaDA-8B-Instruct-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf diffuse-cpp/LLaDA-8B-Instruct-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf diffuse-cpp/LLaDA-8B-Instruct-GGUF:Q8_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf diffuse-cpp/LLaDA-8B-Instruct-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf diffuse-cpp/LLaDA-8B-Instruct-GGUF:Q8_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf diffuse-cpp/LLaDA-8B-Instruct-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf diffuse-cpp/LLaDA-8B-Instruct-GGUF:Q8_0

Use Docker

docker model run hf.co/diffuse-cpp/LLaDA-8B-Instruct-GGUF:Q8_0

LM Studio
Jan

vLLM

How to use diffuse-cpp/LLaDA-8B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "diffuse-cpp/LLaDA-8B-Instruct-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "diffuse-cpp/LLaDA-8B-Instruct-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/diffuse-cpp/LLaDA-8B-Instruct-GGUF:Q8_0

Ollama
How to use diffuse-cpp/LLaDA-8B-Instruct-GGUF with Ollama:
```
ollama run hf.co/diffuse-cpp/LLaDA-8B-Instruct-GGUF:Q8_0
```

Unsloth Studio

How to use diffuse-cpp/LLaDA-8B-Instruct-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for diffuse-cpp/LLaDA-8B-Instruct-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for diffuse-cpp/LLaDA-8B-Instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for diffuse-cpp/LLaDA-8B-Instruct-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use diffuse-cpp/LLaDA-8B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/diffuse-cpp/LLaDA-8B-Instruct-GGUF:Q8_0
```

Lemonade

How to use diffuse-cpp/LLaDA-8B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull diffuse-cpp/LLaDA-8B-Instruct-GGUF:Q8_0

Run and chat with the model

lemonade run user.LLaDA-8B-Instruct-GGUF-Q8_0

List all available models

lemonade list

LLaDA-8B-Instruct-GGUF / README.md

Carmenest

docs: improve model card with quickstart, benchmarks, Apache-2.0

f80c9ca verified 3 months ago

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	---
	license: apache-2.0
	tags:
	- diffusion
	- llada
	- gguf
	- cpu-inference
	- diffuse-cpp
	language:
	- en
	base_model: GSAI-ML/LLaDA-8B-Instruct
	pipeline_tag: text-generation
	---

	# LLaDA-8B-Instruct-GGUF

	GGUF quantizations of [GSAI-ML/LLaDA-8B-Instruct](https://huggingface.co/GSAI-ML/LLaDA-8B-Instruct) for use with [diffuse-cpp](https://github.com/iafiscal1212/diffuse-cpp), the first C++ inference engine for Diffusion Language Models.

	LLaDA is a masked diffusion language model based on the Llama backbone. Unlike autoregressive models that generate one token at a time, LLaDA generates all tokens in parallel through iterative refinement — making it compute-bound rather than memory-bound on CPU.

	On a 12-core CPU, LLaDA with diffuse-cpp reaches 27.7 tok/s on translation tasks — 3.3x faster than llama.cpp (8.51 tok/s) on the same hardware.

	## Available Quantizations

	\| File \| Type \| Size \| Description \|
	\|------\|------\|------\|-------------\|
	\| `llada-8b-f16.gguf` \| F16 \| ~14.9 GB \| Full precision, best quality \|
	\| `llada-8b-q8_0.gguf` \| Q8_0 \| ~8.4 GB \| 8-bit quantization, near-lossless \|
	\| `llada-8b-q4km.gguf` \| Q4_K_M \| ~5.1 GB \| 4-bit mixed, best speed/quality ratio \|

	Recommended: Q4_K_M for most users.

	## Quick Start

	```bash
	# Download
	huggingface-cli download diffuse-cpp/LLaDA-8B-Instruct-GGUF llada-8b-q4km.gguf

	# Build diffuse-cpp
	git clone --recursive https://github.com/iafiscal1212/diffuse-cpp.git
	cd diffuse-cpp
	cmake -B build -DCMAKE_BUILD_TYPE=Release
	cmake --build build -j$(nproc)

	# Run
	./build/diffuse-cli -m ../llada-8b-q4km.gguf \
	--tokens "128000,3923,374,279,6864,315,9822,30" \
	-n 256 -s 16 -t 12 --remasking entropy_exit
	```

	## Performance

	Benchmarked on AMD EPYC 4465P 12-Core, Q4_K_M, entropy_exit + inter-step cache, B=256:

	\| Prompt \| No-Cache \| Cache \| Steps \| vs llama.cpp \|
	\|--------\|----------\|-------\|-------\|-------------\|
	\| Capital of France? \| 17.5 \| 24.4 tok/s \| 3 \| 2.9x \|
	\| Translate to French \| 25.9 \| 27.7 tok/s \| 2 \| 3.3x \|
	\| 15 x 23? \| 12.8 \| 15.7 tok/s \| 4 \| 1.8x \|
	\| Translate to Spanish \| 7.6 \| 22.9 tok/s \| 7 \| 2.7x \|
	\| Python is_prime() \| 3.2 \| 4.9 tok/s \| 16 \| 0.6x \|
	\| Poem about ocean \| 3.2 \| 5.3 tok/s \| 16 \| 0.6x \|
	\| Why is sky blue? \| 3.3 \| 12.0 tok/s \| 16 \| 1.4x \|
	\| List the planets \| 3.3 \| 9.4 tok/s \| 15 \| 1.1x \|
	\| Average \| 9.6 \| 15.3 tok/s \| \| 1.8x \|

	- Inter-step cache: 1.6x average speedup with no quality degradation
	- 6 of 8 prompts outperform llama.cpp (8.51 tok/s baseline)
	- LLaDA excels at translation tasks (converges in 2-5 steps)

	## Model Details

	- Architecture: Llama backbone with bidirectional (non-causal) attention
	- Parameters: 8B
	- Layers: 32
	- Hidden size: 4096
	- Attention: MHA (32 query heads, 32 KV heads)
	- FFN: SwiGLU, intermediate 12288
	- Vocabulary: 126,464 tokens
	- RoPE theta: 500,000
	- Mask token ID: 126336

	## Also Available

	- [Dream-v0-Instruct-7B-GGUF](https://huggingface.co/diffuse-cpp/Dream-v0-Instruct-7B-GGUF) — Qwen2.5 backbone, GQA. Excels at math and code (21.6 tok/s, correctly solves arithmetic in 2 steps).

	## Citation

	```bibtex
	@software{diffuse_cpp_2026,
	title={diffuse-cpp: High-Performance Inference for Diffusion Language Models},
	author={Carmen Esteban},
	year={2026},
	url={https://github.com/iafiscal1212/diffuse-cpp}
	}
	```

	## License

	Apache 2.0