Boltz for Blackwell GPUs

Installation and testing scripts for running Boltz biomolecular structure prediction on NVIDIA Blackwell architecture GPUs (compute capability 12.1).

Supported Hardware

• NVIDIA GB10 (DGX Spark)
• NVIDIA GB100 (B100)
• NVIDIA GB200 (B200)
• Other Blackwell architecture GPUs with compute capability 12.1

The Problem

Standard PyTorch releases don't support Blackwell GPUs (sm_121) yet. This causes:

• nvrtc: error: invalid value for --gpu-architecture (-arch) during runtime kernel compilation
• cuEquivariance kernel failures

The Solution

Use PyTorch nightly with CUDA 13.0 which includes:

• sm_120 architecture support with forward compatibility to sm_121
• Updated nvrtc that handles Blackwell GPUs

Run Boltz with --no_kernels flag to disable cuEquivariance kernels.

Quick Start (PyPI)

The easiest way to get started:

# Install boltz-blackwell from PyPI
pip install boltz-blackwell

# Install PyTorch nightly + Boltz with one command
boltz-blackwell install

# Verify everything is working
boltz-blackwell check

# Run a prediction (--no_kernels is automatic)
boltz-blackwell predict input.yaml --output results/

Installation from Source

# Clone this repo
git clone https://github.com/tmsincomb/boltz-blackwell.git
cd boltz-blackwell

# Install the package
pip install -e .

# Install PyTorch and Boltz
boltz-blackwell install

# Or use the shell script
./install.sh

Manual Installation

If you prefer to install manually:

# 1. Install PyTorch nightly with CUDA 13.0
pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu130

# 2. Install Boltz
pip install boltz[cuda] -U

# 3. Verify installation
python -c "import torch; print('CUDA:', torch.version.cuda); print('Arch:', torch.cuda.get_arch_list())"

Usage

Using boltz-blackwell CLI (Recommended)

The boltz-blackwell CLI automatically adds the --no_kernels flag:

# Basic prediction
boltz-blackwell predict input.yaml

# Fast mode (quick test)
boltz-blackwell predict input.yaml --fast

# High quality mode
boltz-blackwell predict input.yaml --high-quality

# Custom settings
boltz-blackwell predict input.yaml --samples 3 --steps 100 --output results/

# With MSA server (best accuracy, requires internet)
boltz-blackwell predict input.yaml --use-msa-server

Using boltz directly

# Single-sequence mode (fully local, fast but lower accuracy)
boltz predict input.yaml --out_dir output/ --no_kernels

High-Quality Prediction

# More samples and steps (slower, better quality)
boltz predict input.yaml --out_dir output/ --no_kernels \
    --diffusion_samples 5 \
    --recycling_steps 3 \
    --sampling_steps 200

With MSA (Best Accuracy)

For best accuracy, provide a pre-computed MSA or use the ColabFold server:

# With MSA server (requires internet)
boltz predict input.yaml --out_dir output/ --no_kernels --use_msa_server

Input Format

Create a YAML file describing your protein/complex:

version: 1
sequences:
  - protein:
      id: A
      sequence: GIVEQCCTSICSLYQLENYCN
      msa: empty  # Use 'empty' for single-sequence mode

See examples/ for more input file examples.

Key Flags for Blackwell GPUs

Benchmarks

Tested on NVIDIA GB10 (DGX Spark):

Troubleshooting

"nvrtc: error: invalid value for --gpu-architecture"

Your PyTorch doesn't support Blackwell GPUs. Install the CUDA 13.0 nightly:

pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu130

"no kernel image is available for execution on the device"

Same issue - need PyTorch with sm_120/sm_121 support.

"CUDA error: no kernel image is available"

Make sure you're using --no_kernels flag with boltz.

cuEquivariance errors

The cuEquivariance library doesn't support sm_121 yet. Always use --no_kernels.

Requirements

• Python 3.10+
• NVIDIA Blackwell GPU (compute capability 12.1)
• CUDA 13.0+ drivers
• ~16GB GPU memory for small proteins
• ~40GB+ GPU memory for larger complexes

License

This installation guide is MIT licensed. Boltz itself is under MIT License.

References

• Boltz GitHub
• Boltz-1 Paper
• Boltz-2 Paper
• PyTorch Nightly Builds