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On-Premises LLM Training & Inference
Pascari aiDAPTIV
Pascari aiDAPTIV delivers a scalable, high-capacity LLM training environment for domain-specific model training on-premises. It helps organisations keep control of their data while supporting local training, fine-tuning and inference.
Behind-the-Firewall AI
LLM Fine-Tuning
AI PCs, Edge & Servers
Flash-Extended Memory
aiDAPTIV Technology View
GPU memory, flash memory and middleware working together for local LLM training and inference.
On-Prem
GPU Memory
HBM / GDDR
High-speed GPU memory used for active AI model processing.
Flash Extension
Cache Memory
Cost-effective flash memory extends effective AI workload capacity.
Memory management middleware
Helps manage model slices between GPU memory, system memory and flash cache.
01
Data ingest and RAG
Prepare information for domain-specific AI workflows.
INGEST
02
Fine-tuning and validation
Train models with proprietary data for tailored AI output.
TRAIN
03
Local inference
Run trained models on-premises where data is created or collected.
RUN
What It Solves
On-premises LLM training with greater data control
Organisations are increasingly looking for AI capabilities that are affordable, secure, private and tailored to their own users and business data. Pascari aiDAPTIV supports this by enabling on-premises LLM training and inference while keeping data closer to the organisation.
Domain-specific model training
aiDAPTIV is designed to help organisations train models with proprietary data, allowing them to build AI capabilities that are more relevant to their industry, workflows and internal knowledge.
Scalable AI workload support
The solution combines AI software, middleware and flash memory extension to support larger AI workloads without relying only on high-cost GPU memory capacity.
Benefits
Simple to deploy, private by design and built for practical cost control
The core value of aiDAPTIV is that organisations can explore on-premises LLM training without giving up data control or depending fully on public cloud processing.
CONTROL
Keeps data in your control
Enables LLM training behind your firewall, supporting control over private data and data sovereignty requirements.
DEPLOY
Simple to use and deploy
Offers an all-in-one AI toolset for ingest, RAG, fine-tuning and inference through an intuitive graphical user interface.
BUDGET
Fits your budget
Offloads expensive HBM and GDDR memory to cost-effective flash memory, reducing the need for many high-cost and power-hungry GPU cards for suitable workloads.
Why Choose aiDAPTIV
Built for organisations that need practical on-premises LLM training
aiDAPTIV is positioned around control, affordability, capacity, security and easier deployment.
01
Customization & Control
Train models with proprietary data to support more relevant and domain-specific AI output.
02
Affordability
Reduce cloud dependency and minimise long-term operational cost for suitable on-premises workloads.
03
High Capacity & Scalability
Support larger LLM training with an optimised AI hardware and software ecosystem.
04
Data Security & Compliance
Keep sensitive data in-house and support compliance needs for regulated environments.
05
Easy Deployment & Integration
Integrate into existing IT infrastructure for practical deployment in offices, classrooms or data centres.
Technology Capabilities
AI toolset, memory middleware and improved inference
The capabilities below reflect what aiDAPTIV is meant to provide: local model workflows, flash-extended memory and improved inference behaviour for suitable AI workloads.
AI TOOLSET
Ingest, RAG, fine-tuning and inference
aiDAPTIV provides an all-in-one AI toolset that supports data ingest, RAG, fine-tuning and inference through a graphical user interface.
MIDDLEWARE
Memory management middleware
The middleware helps manage memory across GPU memory, system memory and flash cache to reduce bottlenecks for local AI workloads.
CACHE MEMORY
Flash-extended GPU memory
Flash memory extends effective GPU memory capacity, supporting larger models, longer context and extended AI sessions on suitable platforms.
INFERENCE
Faster inference experience
aiDAPTIV is positioned to improve Time to First Token recall, extend token length and support more context for AI responses.
Industry Use Cases
Building smarter businesses with on-premises LLM domain training
These are the industry categories and use-case directions highlighted for on-premises LLM training with Pascari aiDAPTIV.
01
Financial Services
Fraud detection, algorithmic trading, risk management, customer service automation and regulatory compliance.
02
Healthcare
Medical imaging analysis, patient data analysis, clinical trial optimisation, drug discovery and documentation.
03
Legal Services
Case law research, contract review, e-discovery, litigation strategy and regulatory compliance.
04
Government & Defense
Intelligence analysis, cybersecurity enhancement, policy development, operations and secure communication.
05
Manufacturing
Predictive maintenance, supply chain optimisation, quality control, production efficiency and safety monitoring.
06
Energy & Utilities
Grid management, predictive maintenance, renewable forecasting, cybersecurity and smart meter analysis.
07
Telecommunications
Network optimisation, support automation, fraud detection, 5G planning and real-time speech analysis.
08
Retail & E-Commerce
Personalised shopping, demand forecasting, AI chatbots, pricing strategies and fraud prevention.
09
Education
Adaptive learning, AI-generated content, student analytics, accessibility and research summarisation.
10
Transportation & Logistics
Route optimisation, fleet management, supply chain risk analysis, autonomous systems and traffic prediction.
Middleware Flow
How aiDAPTIV manages model slices and memory
At a high level, the middleware helps manage model slices between GPU memory, system memory and flash cache so larger AI workloads can be handled locally.
01 · SLICE
Slice the model
The model is divided into slices so the workload can be handled across available resources.
02 · ASSIGN
Assign to GPU
Model slices are assigned to GPU memory for active AI workload processing.
03 · HOLD
Hold pending slices
Pending slices are held by aiDAPTIV middleware while active slices are being processed.
04 · SWAP
Swap with finished slices
Pending slices are swapped with finished slices to continue training or inference.
Explore Pascari aiDAPTIV
Speak with Nanyang Tech to learn more about Pascari aiDAPTIV and how this Phison solution may fit on-premises LLM training, inference and domain-specific AI workload requirements.