Lamini
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About
Lamini is a platform that helps enterprises build highly accurate AI agents by reducing hallucinations and optimizing for cost and speed. It offers various features including Memory Tuning, Memory RAG, and a Classifier Agent Toolkit. The platform supports various use cases like Text-to-SQL, classification, and function calling. Lamini can be deployed on-premise, in the cloud, or even air-gapped, ensuring data privacy. It is used by Fortune 500 companies and startups.
Platform
Task
Features
• reduce hallucinations by 95%
• text-to-sql
• classifier agent toolkit
• memory rag
• memory tuning
• deploy securely, anywhere
• reduce openai spend
• classification agent workflows
FAQs
What hardware do you use in your cluster?
Lamini On-Demand currently uses MI250s, but we have MI300s available for our Lamini Reserved plans. Please contact us to learn more about Lamini Reserved and our MI300 cluster.
How do I size the number of GPUs?
Increasing the number of GPUs will speed up your job by approximately 1.5x per GPU. Lamini will automatically reschedule your long running jobs, even if they’re only scheduled on 1 GPU.
Is there a difference in price between input and output tokens?
For Lamini On-Demand, the price for both input and output tokens is $0.50 per million tokens.
Do you offer any volume discounts?
Not for Lamini On-Demand. If you want to run a large volume of jobs or data, contact us about Lamini Reserved or Self-managed for better pricing.
How do you license?
For Lamini Reserved and Self-Managed, we license based on the number and type of GPU(s). Please contact us for a quote.
Do you offer special pricing for startups?
Yes, we do. Please contact us.
How much data do you need to start?
For an initial evaluation data set, you will need about 20-40 input-output pairs to start. As you iterate, you will add more data until you achieve the level of accuracy required for your use case.
How long does it take to run a tuning job? About how much will it cost to run a tuning job?
It takes approximately 50 steps for every 100 data points you want to train, but this will vary significantly based on size and complexity of your data points. We calculate tuning job costs by: $1 per step * number of GPUs. Example: Memory tuning 100 data points with 50 steps → $50 on one GPU or $50 * 2 = $100 on 2 GPUs
What are steps?
In the context of tuning models, a "step" refers to a single update of the model's weights / one iteration. You can set the number of steps you want per job when you submit it.
Can I run the Meta Llama Text-to-SQL Memory Tuning Notebook?
Yes! Our free $300 in credits is enough to run the Meta Llama Notebook and tuning jobs from scratch.
What if I made my account earlier, do I still get free credits?
Yes, if you created an account earlier, you should have received $300 in free credit. If you didn’t receive your credit, please contact us.
My job is too slow. How can I speed it up?
You can request more GPUs for your job. Each additional GPU will improve performance by about 1.5x. Requesting more GPUs will increase the cost of the job.
What is your inference speed?
We built our inference engine to be highly performant. We run on AMD MI250 and MI300 GPUs and Nvidia H100 GPUs so our Single Stream memory wall is 200 tokens/sec, 331 tokens/sec, and 209 tokens/sec respectively. Learn more about evaluating performance of inference frameworks here.
What is a datapoint?
A datapoint is a single instance of data used in training. For example, in a text classification task, each sentence or document would be a datapoint. The number of datapoints affects the overall training time and cost.
How are steps calculated?
Steps are provided by the user when submitting a job. By default, we assume 50 steps per 100 datapoints, but this can be adjusted based on your specific needs. More complex tasks or larger models might require more steps per datapoint.
Pricing Plans
On-demand
$0.50 / per 1M tokens• $0.50/1M inference tokens
• one price for input, output, and JSON output
• $1/tuning step
• Linear multiplier for burst tuning across multiple GPUs
• Access to top open source models
• Runs on Lamini’s optimized compute platform
Reserved
Unknown Price• Run on reserved GPUs from Lamini
• Unlimited tuning and inference
• Unmatched inference throughput
• Full evaluation suite
• Access to world-class ML experts
• Enterprise support
Self-managed
Unknown Price• Run Lamini on your own GPUs
• No internet access needed
• Pay per software license
• Full evaluation suite
• Access to world-class ML experts
• Enterprise support
Starter
$250.00 / per year• Upto 10 projects
• Customizable dashboard
• Upto 50 tasks
• Upto 1 GB storage
• Unlimited proofings
Pro
$400.00 / per year• Upto 10 projects
• Customizable dashboard
• Upto 50 tasks
• Upto 1 GB storage
• Unlimited proofings
• Unlimited custom fields
• Unlimited milestones
• Unlimited timeline
Job Opportunities
Machine Learning Engineer - Customer Facing
Lamini helps enterprises build accurate, fast, secure, and cost-efficient AI agents using their own data. Deploy on-prem or in the cloud.
Benefits:
Competitive base salary
Equity
Benefits
Education Requirements:
Bachelor's degree in Computer Science or related field
Experience Requirements:
3+ years of experience with deep learning models in production
2+ years of experience in a customer-facing role
Other Requirements:
Designed novel and innovative solutions for technical platforms in a developing business area
Strong technical aptitude to partner with engineers and proficiency in software engineering
Ability to navigate and execute amidst ambiguity, and to flex into different domains based on the business problem at hand, finding simple, easy-to-understand solutions
Excitement for engaging in cross-organizational collaboration, working through trade-offs, and balancing competing priorities
A love of teaching, mentoring, and helping others succeed
Excellent communication and interpersonal skills, able to convey complicated topics in easily understandable terms to a diverse set of external and internal stakeholders
Responsibilities:
Act as the primary technical advisor for prospective customers evaluating LLM and finetuning projects on Lamini platform
Partner closely with account executives to understand customer requirements
Drive technical decision making by advising on optimal setup, architecture, and integration of Claude into the customer's existing infrastructure
Support customer onboarding by working cross-functionally to ensure successful ramp and adoption
Travel occasionally to customer sites for workshops, implementation support, and building relationships
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Data Center Technician
Lamini helps enterprises build accurate, fast, secure, and cost-efficient AI agents using their own data. Deploy on-prem or in the cloud.
Benefits:
Competitive base salary
Equity
Benefits
Education Requirements:
Bachelor’s degree in Computer Science, IT, Electrical Engineering, or a related field, or equivalent hands-on experience
Experience Requirements:
2+ years of experience in a data center environment
Responsibilities:
Oversee day-to-day operations of our GPU cluster
Assist with the deployment, configuration, and calibration of GPU servers
Implement and support hardware upgrades
Continuously monitor system performance
Quickly diagnose and resolve hardware and network issues, coordinating with team members to minimize disruptions
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DevOps engineer
Lamini helps enterprises build accurate, fast, secure, and cost-efficient AI agents using their own data. Deploy on-prem or in the cloud.
Benefits:
Competitive base salary
Equity
Benefits
Education Requirements:
Bachelor’s degree in Computer Science, or a related field
Responsibilities:
Design and implement robust software deployment processes for delivering high-quality platforms to enterprise customers
Maintain and enhance internal ML infrastructure
Diagnose and resolve issues related to deploying Lamini Platform in customer on-prem environments
Collaborate with data center vendors to manage GPU servers
Partner with cross-functional teams to ensure reliability and scalability are embedded in the design of new features and services
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Ratings & Reviews
No ratings available yet. Be the first to rate this tool!
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