> For clean Markdown of any page, append .md to the page URL. > For a complete documentation index, see https://docs.nvidia.com/bionemo/inference-runtime/llms.txt. > For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/bionemo/inference-runtime/_mcp/server. # BioIR Support Matrix What BioNeMo Inference Runtime (BioIR) runs, on which GPUs, and which fused kernels it uses. How to construct a model or call `build_processor` is in [Python API](/bionemo/inference-runtime/latest/references/api). Model keys are the strings in `bionemo_ir.hubs.FoldingSupportMatrix`. Pass them as `EngineProcessorConfig.model_source` or as `model_name=` on the constructor. ## Models and Data Pipeline Every model below runs on the **optimized PyTorch backend**. Distributed execution in `build_processor` is **replica mode** only: each engine worker holds a full model copy on a single GPU and folds independent inputs (`ParallelismMode.REPLICA`; [`api.md` Ray replicas](/bionemo/inference-runtime/latest/references/api#ray-multi-gpu-replicas)). Splitting one forward pass across GPUs (model / context parallelism, as in [Fold-CP](https://github.com/NVIDIA-Digital-Bio/boltz-cp)) is planned, not available yet. | Model key | `nn.Module` | Pipeline (`build_processor`) | Default `runtime_args` | | -------------------------------------------------------------------------- | ----------------------------- | ---------------------------- | ------------------------------------------------------------------------------- | | `alphafold2_1` … `alphafold2_5` | `OpenFold2` | Yes | `{}` — recycle count is the feature axis (`max_recycling_iters + 1`, default 4) | | `alphafold2_multimer_1` … `_5` | `OpenFold2` (multimer config) | Yes | `{}` | | `openfold2_finetuning_2` … `_5`, `openfold2_ptm_*`, `openfold2_no_templ_*` | `OpenFold2` | Yes | `{}` | | `boltz-1` | `Boltz1` | Yes | `{recycling_steps: 3, num_sampling_steps: 200, diffusion_samples: 1}` | | `boltz-2` | `Boltz2` | Yes | same as Boltz-1 | | `openfold3` | `OpenFold3` | Yes | same Boltz-style names ([`api.md` runtime args](/bionemo/inference-runtime/latest/references/api#runtime-args)) | | `protenix-v2` | `Protenix` | **No** | — (no pipeline) | | `boltz-2-affinity` | `Boltz2Affinity` | **No** | — | `protenix-v2` and `boltz-2-affinity` have an `nn.Module` but no tokenizer, feature factory, or post-processor. Do not pass them to `build_processor`. Ligand *structure* prediction on Boltz-1/2 and OpenFold3 is supported; affinity prediction is not. Input coverage for the bundled data pipeline (`InputRequest` → parser → writer). "Supported" means the pipeline accepts the input and produces a structure. "—" means the mode does not apply to that family. | Model | Monomer | Homo- / hetero-oligomer | Unpaired MSA | Paired MSA | Templates (caller-supplied CIF) | RNA / DNA | Ligands (CCD / SMILES) | Ligand affinity | | -------------------- | ------- | ----------------------- | ------------------- | ---------- | ------------------------------- | --------- | ---------------------- | --------------- | | AF2 / OF2 monomer | Yes | — | Required | — | Yes (protein) | — | — | — | | AF2 multimer | — | Yes | Required | Optional | Yes (protein) | — | — | — | | `boltz-1`, `boltz-2` | Yes | Yes | Required on protein | Optional | Yes (protein) | Yes | Yes | — | | `openfold3` | Yes | Yes | Required on protein | Optional | Yes (protein) | Yes | Yes | — | Notes: - Nucleic acids and ligands are Boltz-1/2 and OpenFold3 only. OpenFold2 / AlphaFold2 fold protein chains exclusively. - Templates are allowed only on `polymer_type="protein"`. BioIR does **not** run HHsearch / HMMsearch; pass CIF (or PDB) hits you already have. - Nucleic-acid and ligand chains carry no MSA (`msas` / `paired_msas` are empty). - AF2 monomer takes a single unpaired a3m per chain. AF2 multimer accepts paired a3m (some sample builders require it on every chain). Boltz-1/2 and OpenFold3 treat paired MSAs as optional. `Polymer.polymer_type` is `"protein"`, `"rna"`, `"dna"`, `"ccd_ligand"` (a CCD code or `_`-joined list), or `"smiles_ligand"` (a SMILES string in `sequence`). Schema details: [`api.md` input requests](/bionemo/inference-runtime/latest/references/api#input-requests). ## GPUs **This table is backend compatibility, not release qualification.** It says which fused kernels apply on each architecture; every row runs. The devices this release is qualified on are H200, H100, A100, L40S, GB200, and GB300, with measured results for each in [Benchmarks](/bionemo/inference-runtime/latest/references/benchmark). The rest of the table works and is not part of that set. Optimized CuTeDSL kernels cover Ampere through Hopper, plus SM100 / SM103 for pair-weighted averaging, outer-product mean and AdaLN; the kernel table below lists the exact SMs per kernel. On **SM100**, **SM103**, **SM120**, and **SM121**, triangle attention and dual GEMM fall back to cuEquivariance. The PyTorch backend still *runs* on any of these SKUs; rows below describe which fused kernels apply. | Architecture | Compute capability | Datacenter / client GPUs | Optimized kernels | | ------------------ | ------------------ | --------------------------------- | ------------------------------------------------------------------------------- | | **Ampere** | SM80 | A100, A30 | CuTeDSL CUBIN | | **Ampere (GA10x)** | SM86 | A10, A16, A40, RTX A6000 | CuTeDSL CUBIN | | **Ada Lovelace** | SM89 | L40, L40S | CuTeDSL CUBIN | | **Hopper** | SM90 | H100, H200, GH200 | CuTeDSL CUBIN | | **Blackwell** | SM100 | B100, B200, GB200 | CuTeDSL CUBIN (PWA, OPM, AdaLN); cuEquivariance (triangle attention, dual GEMM) | | **Blackwell** | SM103 | B300, GB300 | CuTeDSL CUBIN (PWA, OPM, AdaLN); cuEquivariance (triangle attention, dual GEMM) | | **Blackwell** | SM120 | RTX 5090, RTX 6000 Blackwell | cuEquivariance only (triangle attention, dual GEMM) | | **Blackwell** | SM121 | DGX Spark (GB10) | cuEquivariance only (triangle attention, dual GEMM) | Check the device: ```bash python -c 'import torch; print(torch.cuda.get_device_capability())' ``` `(8, 0)` is A100 / Ampere, `(8, 9)` is L40 / Ada, `(9, 0)` is H100 / Hopper, `(10, 0)` is B200 / SM100, `(10, 3)` is B300 / SM103, `(12, 0)` is SM120, `(12, 1)` is DGX Spark / SM121. On Boltz-1, Boltz-2, OpenFold3, and Protenix (`protenix-v2`) the diffusion **module** (including the token transformer) can be captured as a CUDA graph and replayed across sampling steps (largest win on short sequences). OpenFold2 / AlphaFold2 have no CUDA-graph module. How to enable it: [`api.md` CUDA graphs](/bionemo/inference-runtime/latest/references/api#cuda-graphs-boltz-12-openfold3-protenix). ## Fused Kernels `get_pretrained_config` selects CuTeDSL triangle and pairwise attention on SM80 / SM86 / SM89 / SM90 for fp16 / bf16. On SM100, SM103, SM120, and SM121, triangle attention and dual GEMM use **cuEquivariance** (no CuTeDSL CUBIN). Other SKUs or fp32 fall back to cuEquivariance (triangle attention if installed) or PyTorch SDPA. Call through the dispatchers below (`bionemo_ir._torch.attention_backend` and `bionemo_ir._torch.custom_ops`). Layers wrap the same ops: `TriangleAttention` / `AttentionPairBias`, `TriangleMultiplicationNode`, `PairWeightedAveraging`, `OuterProductMean`, `AdaLN`, `LNProjMoveaxisPad`, `Transition`. | Kernel | Used for | Implementation | SM (optimized) | Calling interface | | ------------------------------ | ------------------------------------- | ------------------------------------ | --------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Triangle attention | Pairformer / Evoformer triangle attn | CuTeDSL → CUBIN; else CUEQUIV / SDPA | 80, 86, 89, 90; CUEQUIV on 100, 103, 120, 121 | [`create_attention`](https://github.com/NVIDIA-BioNeMo/BioNeMo-Inference-Runtime/blob/main/bionemo_ir/_torch/attention_backend/utils.py) (`AttentionType.TRIANGLE`) | | Pairwise attention | Token / atom attention with pair bias | CuTeDSL → CUBIN; else SDPA | 80, 86, 89, 90 | [`create_attention`](https://github.com/NVIDIA-BioNeMo/BioNeMo-Inference-Runtime/blob/main/bionemo_ir/_torch/attention_backend/utils.py) (`AttentionType.PAIRWISE`) | | Dual-GEMM `x_x` / `x0_x1` | Triangle multiplication | CuTeDSL → CUBIN; else CUEQUIV | 80, 86, 89, 90; CUEQUIV on 100, 103, 120, 121 | [`get_dual_gemm_x_x_op`](https://github.com/NVIDIA-BioNeMo/BioNeMo-Inference-Runtime/blob/main/bionemo_ir/_torch/custom_ops/dual_gemm_x_x/ops.py) / [`get_dual_gemm_x0_x1_op`](https://github.com/NVIDIA-BioNeMo/BioNeMo-Inference-Runtime/blob/main/bionemo_ir/_torch/custom_ops/dual_gemm_x0_x1/ops.py) | | Pair-weighted averaging (PWA) | Pair → single update | CuTeDSL → CUBIN | 80, 90, 100, 103 | [`get_pair_weighted_averaging_op`](https://github.com/NVIDIA-BioNeMo/BioNeMo-Inference-Runtime/blob/main/bionemo_ir/_torch/custom_ops/pair_weighted_averaging/ops.py) | | Outer-product mean (OPM) | Single → pair update | CuTeDSL → CUBIN | 80, 86, 89, 90, 100, 103 | [`get_outer_product_mean_op`](https://github.com/NVIDIA-BioNeMo/BioNeMo-Inference-Runtime/blob/main/bionemo_ir/_torch/custom_ops/outer_product_mean/ops.py) | | Gated sigmoid | Attention output gate | CuTeDSL → CUBIN | 80, 86, 89, 90 | [`get_gated_sigmoid_op`](https://github.com/NVIDIA-BioNeMo/BioNeMo-Inference-Runtime/blob/main/bionemo_ir/_torch/custom_ops/gated_sigmoid/ops.py) | | AdaLN (LayerNorm + sigmoid) | Diffusion adaptive LayerNorm | CuTeDSL → CUBIN | 80, 86, 89, 90, 100, 103 | [`get_adaln_layernorm_sigmoid_op`](https://github.com/NVIDIA-BioNeMo/BioNeMo-Inference-Runtime/blob/main/bionemo_ir/_torch/custom_ops/adaln_layernorm_sigmoid/ops.py) | | Fused LN + proj + moveaxis/pad | Pair-bias LayerNorm + linear + layout | Triton | all CUDA | [`LNProjMoveaxisPad`](https://github.com/NVIDIA-BioNeMo/BioNeMo-Inference-Runtime/blob/main/bionemo_ir/_torch/custom_ops/fused_ln_proj_moveaxis_pad.py) / [`fused_ln_proj_moveaxis_pad`](https://github.com/NVIDIA-BioNeMo/BioNeMo-Inference-Runtime/blob/main/bionemo_ir/dsl_kernels/triton/fused_ln_proj_moveaxis_pad.py) | | Fused SwiGLU | Transition / FFN | Triton | all CUDA | [`FusedSwiGLU`](https://github.com/NVIDIA-BioNeMo/BioNeMo-Inference-Runtime/blob/main/bionemo_ir/dsl_kernels/triton/fused_swiglu.py) / [`fused_swiglu`](https://github.com/NVIDIA-BioNeMo/BioNeMo-Inference-Runtime/blob/main/bionemo_ir/dsl_kernels/triton/fused_swiglu.py) | Override backends on a config if you need a reference path, for example `config.trunk.set_triangle_attention_backend("SDPA")`. **CuTeDSL source is not open-sourced, and there is no plan to publish it.** The public tree and wheels ship the Python callables plus precompiled CUBIN payloads for every CuTeDSL kernel in the table above. They do not ship the CuTeDSL kernel implementations used to generate those CUBINs. Loading a CUBIN skips CuTeDSL JIT (`cute.compile`), so those kernels run at full speed on the first iterations — no kernel-JIT warmup is required to hide compile latency. If a CUBIN is missing for the current SM / dtype, those ops fall back to PyTorch (SDPA or a vanilla reference) rather than JIT-compiling CuTeDSL. Triton fused ops in `bionemo_ir.dsl_kernels.triton` remain ordinary Python source and still JIT on first use.