fouroversix 0.1.0

More Accurate FP4 Quantization with Adaptive Block Scaling

Four Over Six (4/6)

A method for improving the accuracy of NVFP4 quantization with Adaptive Block Scaling.

This repository contains kernels for efficient NVFP4 quantization and matrix multiplication, and fast post-training quantization with our method, 4/6. If you have any questions, please get in touch or submit an issue.

Setup

To speed up build times, set CUDA_ARCHS=100 to only compile kernels for B-series GPUs (i.e. B200, GB200, GB300), or CUDA_ARCHS=120 for RTX 50 and 60 Series GPUs (i.e. RTX 5090, RTX 6000).

git clone --recursive https://github.com/mit-han-lab/fouroversix.git
cd fouroversix
pip install --no-build-isolation -e ".[tests]"

If you don't have a Blackwell GPU, you may use our reference implementation, which is slow but helpful for testing, by setting SKIP_CUDA_BUILD=1 before running pip install.

API

Quantize a Model to NVFP4

from fouroversix import AdaptiveBlockScalingRule, apply_ptq
from transformers import AutoModelForCausalLM

# NVFP4 using 4/6 with MSE block selection
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B")
apply_ptq(model)

# Standard NVFP4 round-to-nearest quantization
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B")
apply_ptq(
    model,
    a_scale_rule=AdaptiveBlockScalingRule.always_6,
    w_scale_rule=AdaptiveBlockScalingRule.always_6,
)

Quantize a Tensor to NVFP4

Check the quantize_to_fp4 arguments for more details about how you can enable certain features during quantization, such as stochastic rounding or 2D block quantization.

import torch
from fouroversix import AdaptiveBlockScalingRule, quantize_to_fp4

x = torch.randn(1024, 1024, dtype=torch.bfloat16, device="cuda")
x_e2m1, x_e4m3, x_normconst = quantize_to_fp4(x)

# Standard NVFP4 round-to-nearest quantization
x_e2m1, x_e4m3, x_normconst = quantize_to_fp4(
    x,
    scale_rule=AdaptiveBlockScalingRule.always_6,
)

Multiply Two NVFP4 Tensors

from fouroversix import fp4_matmul

# Starting from two BF16 tensors with shape (M, K) and (N, K):
out = fp4_matmul(a, b)

# If you've already quantized two tensors A and B as shown above:
out = fp4_matmul(
    a_e2m1=a_e2m1,
    a_sf=a_e4m3,
    a_normconst=a_normconst,
    b_e2m1=b_e2m1,
    b_sf=b_e4m3,
    b_normconst=b_normconst,
)

PTQ Evaluation with LM Evaluation Harness

# Round-to-nearest quantization with 4/6:
python -m scripts.ptq --model-name meta-llama/Llama-3.2-1B --ptq-method rtn --task wikitext

# Standard NVFP4 round-to-nearest (RTN) quantization:
python -m scripts.ptq --model-name meta-llama/Llama-3.2-1B --ptq-method rtn --task wikitext --a-scale-rule always_6 --w-scale-rule always_6

# AWQ with 4/6:
python -m scripts.ptq --model-name meta-llama/Llama-3.2-1B --ptq-method awq --task wikitext

# High-precision baseline, no NVFP4 quantization:
python -m scripts.ptq --model-name meta-llama/Llama-3.2-1B --ptq-method high_precision --task wikitext

If you would prefer not to worry about setting up your local environment, or about acquiring a Blackwell GPU to run your experiments faster, you may run PTQ experiments on Modal by adding the --modal flag, and optionally the --detach flag which will enable you to CTRL+C. The first time you launch experiments on Modal, it may take several minutes to build everything, but following commands will reuse the cached images.

Notes

This repository contains three implementations of NVFP4 quantization, each of which has various limitations:

• CUDA: Only supports forward passes, making it usable for post-training quantization as shown above. Training kernels will be released soon. Requires a Blackwell GPU.
• Triton: Slower, but supports all operations needed for efficient NVFP4 training, including stochastic rounding, the random Hadamard transform, transposed inputs, and 2D block scaling. Also requires a Blackwell GPU.
• PyTorch: A reference implementation written in PyTorch that can run on any GPU. May have some educational value. Should not be used in real-world use cases.

These three implementations have very subtle numerical differences, which we are working on fixing. Our quantize_to_fp4 function will automatically select one of these backends based on your GPU and the quantization parameters you select. If you would like to force selection of a specific backend, you may specify it by setting backend=QuantizeBackend.cuda in quantize_to_fp4, or a_quantize_kwargs={"backend": QuantizeBackend.cuda}, w_quantize_kwargs={"backend": QuantizeBackend.cuda} in apply_ptq.

TODOs

In the coming days and weeks, we will be updating our implementation and publishing more code. Here are our highest-priority items at the moment:

• Match numerics of PyTorch and Triton backends to the CUDA backend
• Add support for other options (MXFP4, stochastic rounding, RHT, 2D block scaling, transposed inputs) in the CUDA implementation
• Release PTQ implementations for AWQ, GPTQ, and SmoothQuant
• Unit tests
• Training implementation + full NVFP4 linear layer with 4/6

Contributing

We welcome contributions to our repository, but get in touch before making any substantial changes. Also, please make sure any code changes are compliant with our linter:

ruff check

Citation

Please use the following BibTeX entry to cite this work:

@misc{cook2025sixaccuratenvfp4quantization,
      title={Four Over Six: More Accurate NVFP4 Quantization with Adaptive Block Scaling},
      author={Jack Cook and Junxian Guo and Guangxuan Xiao and Yujun Lin and Song Han},
      year={2025},
      eprint={2512.02010},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2512.02010},
}

License

This repository is available under the MIT license. See the LICENSE.md file for details.