---
title: "BioNeMo"
description: "2 posts about BioNeMo, summarised, each linking to the original."
---

# BioNeMo
> 2 posts about BioNeMo, summarised, each linking to the original.

## Articles

### [Efficient MoE Training for Biological Foundation Models](https://yomu.fyi/post/efficient-moe-training-for-biological-foundation-models.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Michelle Horton
- Published: Sep 24, 2026
- Type: Tutorial

Mixture-of-experts (MoE) architectures scale model capacity more efficiently than dense transformers by activating only a subset of experts per token, but naive implementations suffer from fragmented expert computation, routing overhead, and memory pressure. NVIDIA Transformer Engine (TE) addresses these bottlenecks with optimized primitives including GroupedLinear for batched expert GEMMs, MXFP8 low-precision training, and a fused GroupedMLP kernel that combines quantization, SwiGLU activation, and routing-weight scaling. The BioNeMo recipe implements these techniques for biological foundation models, using TE’s autocast API for MXFP8 precision and the Sequential API to fuse the gate-up, ScaledSwiGLU, and down projections into a single operation. On eight NVIDIA B200 GPUs, the recipe achieved up to 2.21x the training throughput of the Hugging Face baseline for a Mixtral-8x7B model. The tutorial provides prerequisites, code examples, and configuration commands to reproduce the optimizations.


### [High-Throughput Structure Prediction with BioNeMo Inference Runtime](https://yomu.fyi/post/high-throughput-structure-prediction-with-bionemo-inference-runtime.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Elizabeth Goodman
- Published: Sep 10, 2026
- Type: Tutorial

Proteome-scale biomolecular structure prediction requires moving large worklists efficiently, and NVIDIA BioNeMo Inference Runtime (BioIR) accelerates supported models on NVIDIA GPUs while retaining a PyTorch workflow. Its end-to-end processor passes InputRequest objects through parsing, tokenization, feature generation, GPU inference, and PDB or mmCIF writing; a Ray backend places complete model replicas on GPUs for concurrent independent inputs, while direct PyTorch integration supports custom code. In a matched 1,000-target human-dimer benchmark, BioIR completed all targets at 58.5K successfully folded residues per allocated GPU-hour versus 20.2K for the tested public implementation, a 2.90× improvement, which ran out of memory on 29 targets. Rated-power-equivalent estimates for one million comparable targets were 11 versus 35 MWh at 8-GPU TDP and 21 versus 64 MWh at full-node maximum power, excluding data-center overhead and several pipeline costs.