Product selection
Next-gen innovation is local LLM's implementation.
Adaptive-LLM is a TieSet’s framework that leverages smaller local models to improve the efficiency and performance of Large Language Models (LLMs) that perform even better for various tasks by not sharing any sensitive data to anybody.
Standard Plan
Up to 20 Users-
Installed on client hardware or cloud infrastructure
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Supported as a service in the initial installation package
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Provide standard training on basic usage
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Initial consultation is free for 2 hours
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Ensure optimal performance environment with regular updates and maintenance
Premium Plan
Up to 100 Users-
Installed on client hardware or cloud infrastructure
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After hearing your company's requests, we will draft an introduction plan that meets your company's needs.
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Same as STANDARD plan Providing more comprehensive training and support (troubleshooting)
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Initial consulting is free for 5 hours
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Ensure optimal performance environment with regular updates and maintenance
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Local LLM integration with Box content access boosts capabilities through unlocked analytics insights.
Enterprise Plan
Unlimited Users-
Installed on client hardware or cloud infrastructure
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Full customization based on pre-installation interviews and full-scale requirements definition
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Dedicated support staff training and support, SLA signing and commitment
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Initial consulting is free for 10 hours
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Regular monitoring and optimization, and proactive suggestions for improvements and updates
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Local LLM integration with Box content access boosts capabilities through unlocked analytics insights.
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Private Label Options
Optimizing Costs
Reduce costs
When deploying large language models locally, costs can be optimized through strategic choices around hardware, scaling, and pricing models.
The Economics
Balance performance
Serving gigantic language models within private data centers involves balancing performance needs, scalability limits, and hardware/software expenses.
On-Premise
infrastructure and licensing
Running large language models on-premises requires budgeting for suitable infrastructure like GPUs and networking, as well as model licensing costs.
Local LLM Models
Utilization and Efficiency
The total cost of local LLMs deployed locally can be reduced through amortization of upfront costs, maximizing utilization, and improving efficiency.
Pricing Models
Volume Discounts
Expenses for large language models can be lowered by having multi-year contracts for optimal terms and pricing.
Controlling On-Prem Expenses
Workloads and Licensing
The costs to run LLMs on-premises can be controlled by optimizing infrastructure and exploring discounted licensing options.
Building a Better Tomorrow with Large Language Models.
The essential knowledge from the bigger LLM is extracted and transferred to smaller and more resource-friendly local models. This LLM functionality extraction process into smaller LLMs enhances task-specific performance while reducing computational requirements.
Why Choose Us
Experience the limitless possibilities – with local LLM's
The framework also incorporates a context module that dynamically assigns appropriate contexts to the local models, allowing it to adapt to diverse tasks effectively. Additionally, the framework utilizes data vectors for efficient storage and retrieval of task-specific data, optimizing processing speed and memory consumption.
Unleash innovation - deploy large models locally
By running powerful large language models within your own environment, you unleash innovation and gain the flexibility to customize capabilities for your specific needs.
Discover new potential - with on-premise language models
Large language models deployed on-premise remove barriers and allow exploration into new use cases and possibilities not offered by generic cloud services..