Sold by: NVIDIA
Deployed on AWS
Free Trial
The Boltz-2 NIM provides state-of-the-art biomolecular structure prediction and binding affinity prediction capabilities for combinations of proteins, RNA, DNA, and other molecules. Based on the Boltz-2 architecture, this NIM enables researchers to predict complex biomolecular structures with high accuracy and quantify molecular interactions, supporting a wide range of molecular configurations and binding studies.
Overview
Boltz-2 represents a significant advancement in computational biology, offering unprecedented capabilities for predicting:
Protein structures: Single and multi-chain protein complexes
Nucleic acid structures: DNA and RNA molecules in various configurations
Protein-nucleic acid complexes: Interactions between proteins and genetic material
Ligand binding and affinity prediction: Small molecule interactions with biomolecules, including predicted binding affinity scores
Modified residues: Post-translational modifications and chemical modifications
Constraint-guided predictions: Structure predictions conditioned on specified interaction pockets or contacts
The model supports both single-molecule predictions and complex multi-molecular assemblies, making it suitable for a wide range of research applications from basic structural biology to drug discovery.
Highlights
- NIMs offer a performant, simple, portable, and enterprise-grade route for self-hosted AI applications. Four major advantages that NIMs offer for system administrators and developers are: - Performance - Increased productivity - Portable deployment - Enterprise-grade
Details
Sold by
Delivery method
Latest version
Deployed on AWS
New
Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Free trial
Try this product free for 31 days according to the free trial terms set by the vendor.
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.g5.12xlarge Inference (Batch) Recommended | Model inference on the ml.g5.12xlarge instance type, batch mode | $1.00 |
ml.g6e.12xlarge Inference (Real-Time) Recommended | Model inference on the ml.g6e.12xlarge instance type, real-time mode | $1.00 |
ml.g6e.24xlarge Inference (Real-Time) | Model inference on the ml.g6e.24xlarge instance type, real-time mode | $1.00 |
ml.g6e.48xlarge Inference (Real-Time) | Model inference on the ml.g6e.48xlarge instance type, real-time mode | $1.00 |
ml.p4d.24xlarge Inference (Real-Time) | Model inference on the ml.p4d.24xlarge instance type, real-time mode | $1.00 |
ml.p5.48xlarge Inference (Real-Time) | Model inference on the ml.p5.48xlarge instance type, real-time mode | $1.00 |
ml.p4de.24xlarge Inference (Real-Time) | Model inference on the ml.p4de.24xlarge instance type, real-time mode | $1.00 |
ml.p5e.48xlarge Inference (Real-Time) | Model inference on the ml.p5e.48xlarge instance type, real-time mode | $1.00 |
ml.p5en.48xlarge Inference (Real-Time) | Model inference on the ml.p5en.48xlarge instance type, real-time mode | $1.00 |
ml.g6e.xlarge Inference (Real-Time) | Model inference on the ml.g6e.xlarge instance type, real-time mode | $1.00 |
Dimensions summary
You pay by the hour based on the AWS instance type you run the model on. Billing is usage-based, so charges accrue only while an instance runs. Fourteen dimensions cover real-time inference across a range of GPU instance sizes, from smaller single-GPU hosts to larger multi-GPU hosts. One dimension covers batch-mode inference on a separate instance type. Larger instances carry higher hourly rates. You choose the instance that matches your throughput and latency needs, and pricing scales with the GPU capacity of that instance.
Top-of-mind questions for buyers
One host-hour is one hour of the model running on the chosen AWS instance. You pay for each hour an instance stays active, regardless of how many predictions you run. Charges accrue by wall-clock running time, not by the number of molecular structures predicted.
Both meter by host-hour, but batch mode runs on the ml.g5.12xlarge instance and processes prediction jobs in groups. Real-time mode runs across the range of GPU instances and returns results as requests arrive. You pick the mode matching your workload; each accrues hourly on its own instance.
Charges apply per host-hour only while the instance runs. A stopped instance stops accruing software charges. Idle running instances still accrue charges, since billing tracks running time rather than prediction activity. Stop instances when not in use to avoid ongoing hourly charges.
docs.nvidia.com
Helpful?
Vendor refund policy
None
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Additional details
Inputs
- Summary
The model accepts JSON requests with parameters on /invocations and /ping APIs that can be used to control the generated text. See examples and field descriptions below.
- Input MIME type
- application/json
payload = { "polymers": [ { "id": "A", "molecule_type": "protein", "sequence": "MALWMRLLPLLALLALWGPDPAAAFVNQHLCGSHLVEALYLVCGERGFFYTPKTRREAEDLQVGQVELGGGPGAGSLQPLALEGSLQKRGIVEQCCTSICSLYQLENYCN" } ], "recycling_steps": 3, "sampling_steps": 50, "diffusion_samples": 1, "step_scale": 1.638, "output_format": "mmcif" }
Resources
Support
Vendor support
Free support via NVIDIA NIM Developer Forum:
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Customer reviews
No customer reviews yet
Be the first to review this product . We've partnered with PeerSpot to gather customer feedback. You can share your experience by writing or recording a review, or scheduling a call with a PeerSpot analyst.