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Performance

Data Pipeline

The runtime of the data pipeline (i.e. genetic sequence search and template search) can vary significantly depending on the size of the input and the number of homologous sequences found, as well as the available hardware (disk speed can influence genetic search speed in particular). If you would like to improve performance, it’s recommended to increase the disk speed (e.g. by leveraging a RAM-backed filesystem), or increase the available CPU cores and add more parallelisation. Also note that for sequences with deep MSAs, Jackhmmer or Nhmmer may need a substantial amount of RAM beyond the recommended 64 GB of RAM.

Model Inference

Table 8 in the Supplementary Information of the AlphaFold 3 paper provides compile-free inference timings for AlphaFold 3 when configured to run on 16 NVIDIA A100s, with 40 GB of memory per device. In contrast, this repository supports running AlphaFold 3 on a single NVIDIA A100 with 80 GB of memory in a configuration optimised to maximise throughput.

We compare compile-free inference timings of these two setups in the table below using GPU seconds (i.e. multiplying by 16 when using 16 A100s). The setup in this repository is more efficient (by at least 2×) across all token sizes, indicating its suitability for high-throughput applications.

Num Tokens 1 A100 80 GB (GPU secs) 16 A100 40 GB (GPU secs) Improvement
1024 62 352 5.7×
2048 275 1136 4.1×
3072 703 2016 2.9×
4096 1434 3648 2.5×
5120 2547 5552 2.2×

Running the Pipeline in Stages

The run_alphafold.py script can be executed in stages to optimise resource utilisation. This can be useful for:

  1. Splitting the CPU-only data pipeline from model inference (which requires a GPU), to optimise cost and resource usage.
  2. Caching the results of MSA/template search, then reusing the augmented JSON for multiple different inferences across seeds or across variations of other features (e.g. a ligand).

Data Pipeline Only

Launch run_alphafold.py with --norun_inference to generate Multiple Sequence Alignments (MSAs) and templates, without running featurisation and model inference. This stage can be quite costly in terms of runtime, CPU, and RAM use. The output will be JSON files augmented with MSAs and templates that can then be directly used as input for running inference.

Featurisation and Model Inference Only

Launch run_alphafold.py with --norun_data_pipeline to skip the data pipeline and run only featurisation and model inference. This stage requires the input JSON file to contain pre-computed MSAs and templates.

Accelerator Hardware Requirements

We officially support the following configurations, and have extensively tested them for numerical accuracy and throughput efficiency:

  • 1 NVIDIA A100 (80 GB)
  • 1 NVIDIA H100 (80 GB)

We compare compile-free inference timings of both configurations in the following table:

Num Tokens 1 A100 80 GB (seconds) 1 H100 80 GB (seconds)
1024 62 34
2048 275 144
3072 703 367
4096 1434 774
5120 2547 1416

Other Hardware Configurations

NVIDIA A100 (40 GB)

AlphaFold 3 can run on inputs of size up to 4,352 tokens on a single NVIDIA A100 (40 GB) with the following configuration changes:

  1. Enabling unified memory.

  2. Adjusting pair_transition_shard_spec in model_config.py:

      pair_transition_shard_spec: Sequence[_Shape2DType] = (
      (2048, None),
      (3072, 1024),
      (None, 512),
  )

While numerically accurate, this configuration will have lower throughput compared to the set up on the NVIDIA A100 (80 GB), due to less available memory.

NVIDIA P100

AlphaFold 3 can run on inputs of size up to 1,024 tokens on a single NVIDIA P100 with no configuration changes needed.

NVIDIA V100

There are known issues with V100 devices. See this Issue for tracking.

Other devices

There are known issues with CUDA Capability 7.x devices. See this Issue for tracking.

CUDA Capability 6.x and 8.x devices other than those listed explicitly here are believed to work for AlphaFold 3, but large-scale testing has only been performed for the devices mentioned above.

Compilation Buckets

To avoid excessive re-compilation of the model, AlphaFold 3 implements compilation buckets: ranges of input sizes using a single compilation of the model.

When featurising an input, AlphaFold 3 determines the smallest bucket the input fits into, then adds any necessary padding. This may avoid re-compiling the model when running inference on the input if it belongs to the same bucket as a previously processed input.

The configuration of bucket sizes involves a trade-off: more buckets leads to more re-compilations of the model, but less padding.

By default, the largest bucket size is 5,120 tokens. Processing inputs larger than this maximum bucket size triggers the creation of a new bucket for exactly that input size, and a re-compilation of the model. In this case, you may wish to redefine the compilation bucket sizes via the --buckets flag in run_alphafold.py to add additional larger bucket sizes. For example, suppose you are running inference on inputs with token sizes: 5132, 5280, 5342. Using the default bucket sizes configured in run_alphafold.py will trigger three separate model compilations, one for each unique token size. If instead you pass in the following flag to run_alphafold.py

--buckets 256,512,768,1024,1280,1536,2048,2560,3072,3584,4096,4608,5120,5376

when running inference on the above three input sizes, the model will be compiled only once for the bucket size 5376. Note: for this specific example with input sizes 5132, 5280, 5342, passing in --buckets 5376 is sufficient to achieve the desired compilation behaviour. The provided example with multiple buckets illustrates a more general solution suitable for diverse input sizes.

Additional Flags

Compilation Time Workaround with XLA Flags

To work around a known XLA issue causing the compilation time to greatly increase, the following environment variable must be set (it is set by default in the provided Dockerfile).

ENV XLA_FLAGS="--xla_gpu_enable_triton_gemm=false"

GPU Memory

The following environment variables (set by default in the Dockerfile) enable folding a single input of size up to 5,120 tokens on a single A100 (80 GB) or a single H100 (80 GB):

ENV XLA_PYTHON_CLIENT_PREALLOCATE=true
ENV XLA_CLIENT_MEM_FRACTION=0.95

Unified Memory

If you would like to run AlphaFold 3 on inputs larger than 5,120 tokens, or on a GPU with less memory (an A100 with 40 GB of memory, for instance), we recommend enabling unified memory. Enabling unified memory allows the program to spill GPU memory to host memory if there isn't enough space. This prevents an OOM, at the cost of making the program slower by accessing host memory instead of device memory. To learn more, check out the NVIDIA blog post.

You can enable unified memory by setting the following environment variables in your Dockerfile:

ENV XLA_PYTHON_CLIENT_PREALLOCATE=false
ENV TF_FORCE_UNIFIED_MEMORY=true
ENV XLA_CLIENT_MEM_FRACTION=3.2

JAX Persistent Compilation Cache

You may also want to make use of the JAX persistent compilation cache, to avoid unnecessary recompilation of the model between runs. You can enable the compilation cache with the --jax_compilation_cache_dir <YOUR_DIRECTORY> flag in run_alphafold.py.

More detailed instructions are available in the JAX documentation, and more specifically the instructions for use on Google Cloud. In particular, note that if you would like to make use of a non-local filesystem, such as Google Cloud Storage, you will need to install etils (this is not included by default in the AlphaFold 3 Docker container).