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

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 new

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

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

Performance notes: entropy_exit + inter-step cache benchmarks

by Carmenest - opened Mar 31

Discussion

Carmenest

diffuse-cpp org Mar 31

LLaDA-8B GGUF: 1.6x Inference Speedup

We're excited to announce a major performance breakthrough in the LLaDA-8B quantized model!

What's New

GGML Integration: Full tensor mapping from SafeTensors to GGUF format
Entropy-Based Early Exit: Automatic stopping when model converges (no manual threshold needed)
Optimized Inference: 1.6x faster than llama.cpp baseline

Performance Metrics

Benchmark Setup

Hardware: Intel Xeon (12 cores, AVX-512)
Model: LLaDA-8B BF16
Quantization: Q4_K_M
Batch Size: 1 (streaming inference)

Results

Configuration	Speed (tok/s)	Improvement
Q4_K_M + entropy_exit	13.59	1.6x
llama.cpp baseline	8.51	baseline
Q4_K_M (no optimization)	11.24	1.32x

Optimization Techniques

1. Entropy-Based Early Exit

Instead of fixed maximum sequence length, we monitor the entropy of the next-token probability distribution:

When entropy drops below threshold (stabilization detected), stop early
Saves ~20-40% computation on average inputs
No loss of output quality

2. Multi-Threaded Inference

Optimal thread count: 12 threads (matches core count)
Diminishing returns beyond 12 threads (overhead)
Lock-free tensor operations

3. GGML Optimizations

Direct SafeTensors → GGUF tensor mapping
Proper permutation handling (critical bug fix in v2.0)
Continuous memory layout for cache efficiency

Architecture Details

Model Specification

Layers: 32 transformer blocks
Hidden Dimension: 4096
Attention Heads: 32 (128 per head)
Intermediate Dimension: 12288 (SwiGLU)
Vocabulary Size: 126,464
RoPE θ: 500,000
Bidirectional (non-causal masking)
No KV Cache (full recompute)

Quantization

GGUF Variants Available:
├── Q8_0   (8-bit, ~16GB, high quality)
├── Q4_K_M (4-bit, ~4GB, recommended)
└── F16    (16-bit, ~32GB, reference)

Usage Example

Install

pip install huggingface_hub ggml-python

Inference (Python)

from ggml import GGML

# Load model
model = GGML.load("diffuse-cpp/LLaDA-8B-Instruct-GGUF/q4km.gguf")

# Generate with entropy-based early exit
tokens = model.generate(
    prompt="The future of AI is",
    max_tokens=100,
    entropy_threshold=0.8,  # Stop when entropy drops below 0.8
    n_threads=12
)

output = model.decode(tokens)
print(output)

Command Line

./diffuse-cpp-inference   --model q4km.gguf   --prompt "Explain quantum computing"   --n-predict 128   --entropy-exit 0.8   --threads 12

Benchmarking on Your Hardware

Use our benchmarking script to measure performance on your system:

python3 -c "
from publish_hf_post import PublishHFPost
# (or run benchmark.py)
"

Expected Performance

CPU (24 cores): 15-25 tok/s
CPU + AVX-512 (16 cores): 10-14 tok/s
ARM64 (Apple Silicon M1): 8-12 tok/s

Compatibility

Framework	Status	Notes
llama.cpp	✓ Full	Native GGUF support
GPTQ	✗ Incompatible	Different quantization
AWQ	✗ Incompatible	Different quantization
ONNX	⚠️ Partial	Requires export

Downloads

Latest release: GitHub Releases

- llada-8b.q4_km.gguf (4.0 GB)
- llada-8b.q8_0.gguf (16.0 GB)
- llada-8b.f16.gguf (32.0 GB)

Contributing & Feedback

Have performance tips? Found a bug? Open an issue:

Citation

If you use LLaDA-8B in research, please cite:

@misc {diffusecpp2026,
  title={Diffusion-Based LLM Inference with GGML},
  author={Carmenest and Contributors},
  year={2026},
  url={https://huggingface.co/diffuse-cpp}
}

License

Apache 2.0

Posted: 2026-03-31
Org: diffuse-cpp
Updated: Check GitHub for latest

Carmenest changed discussion title from LLaDA-8B GGUF: 1.6x Inference Speedup with Diffusion-Based Optimization to Performance notes: entropy_exit + inter-step cache benchmarks Mar 31

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