Sold by: Liquid AI
Deployed on AWS
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Liquid LFM 40B is designed to handle complex tasks, offering an optimal balance between size and output quality.
Overview
Liquid LFM 40B strikes a unique balance between model size and output quality. With 12 billion activated parameters, it delivers performance comparable to larger models while its MoE architecture ensures higher throughput and cost-efficient deployment on accessible hardware.
The model excels in areas such as general and expert knowledge, mathematics, logical reasoning, and long-context tasks. Its primary language is English, but it also demonstrates multilingual capabilities in Spanish, French, German, Chinese, Arabic, Japanese, and Korean.
However, certain limitations exist. The model may struggle with precise numerical calculations, time-sensitive information, or unconventional tasks like counting specific letters in a word. Human preference optimization techniques have yet to be fully implemented, leaving room for further enhancement.
This product is specifically optimized for peak performance on H100 GPUs.
Highlights
- **Innovative Model Architecture**: Liquid AI's Foundation Models utilize a unique architecture that combines liquid neural networks and non-transformer designs, allowing these models to be efficient in memory usage and capable of handling sequential data, such as text, video, and real-time signals. This setup optimizes performance while minimizing computational demands.
- **Enhanced Adaptability and Real-Time Learning**: Unlike conventional models, LFMs can adapt their internal processes based on new inputs in real time, making them highly responsive.
- **Efficiency in Long-Context Processing**: Liquid AI's models can efficiently process extended input sequences without the steep memory and processing requirements typical of transformer-based models, supporting applications like document summarization and complex chatbot interactions with minimal hardware demands. With LFMs, it’s possible to fit up to 1 million tokens-worth of data and map it onto 16 gigabytes of memory. **
Details
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Deployed on AWS
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Pricing
Free trial
Try this product free for 7 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.g4dn.12xlarge Inference (Batch) Recommended | Model inference on the ml.g4dn.12xlarge instance type, batch mode | $41.93 |
ml.p5.48xlarge Inference (Real-Time) Recommended | Model inference on the ml.p5.48xlarge instance type, real-time mode | $41.93 |
Dimensions summary
You pay by the hour based on the AWS instance type you run the LFM 40B model on. Two options are available. The first runs inference on the ml.g4dn.12xlarge instance in batch mode, which processes requests in groups. The second runs inference on the ml.p5.48xlarge instance in real-time mode, which handles requests as they arrive. Each option bills per host hour, so cost scales with how long you keep the instance running. Your choice depends on whether you need batch processing or live responses and which hardware fits your workload.
Top-of-mind questions for buyers
Billing runs per host hour for as long as the instance is running. An idle but running instance still accrues host-hour charges. Only stopping or terminating the instance ends software billing. Underlying AWS storage or other resource fees may still apply while the instance exists.
Both bill per host hour. Batch mode processes requests in groups, which suits high-volume jobs where responses do not need to be instant. Real-time mode handles requests as they arrive, which suits live applications needing low latency. Your workload type and chosen hardware determine which instance you run.
No token or request caps apply. You pay for the instance running time, not for the number of tokens or requests processed. The 40B model uses a Mixture of Experts design with 12B activated parameters, so throughput depends on the instance hardware you select, not on a metered request count.
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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
Bedrock release
Additional details
Inputs
- Summary
The model leverages OpenAI's chat format as detailed in OpenAI API documentation , with the following key specifics:
- Supports text-only interactions.
- Input MIME type
- application/json
https://github.com/Liquid4All/aws-samples/blob/main/notebooks/sagemaker/data/input/real-time/simple.json
https://github.com/Liquid4All/aws-samples/blob/main/notebooks/sagemaker/data/input/real-time/simple.json
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
Request body | https://platform.openai.com/docs/api-reference/chat | Type: FreeText | Yes |
Resources
Vendor resources
Support
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.
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