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Scaling Boundaries,
Not Budgets
Ease of Use
aiDAPTIV+ allows you to spend
more time training your data,
not your team of engineers.
Cost and AccessibilityValue
Phison’s aiDAPTIV+ leverages cost-effective NAND
flash to increase access to large-language model
(LLM) training with commodity workstation hardware.
Privacy
aiDAPTIV+ workstations allows
you to retain control of your
data and keep it on premises.
Streamlined Scaling for Data Model Training
aiDAPTIV+ is the ultimate turnkey solution for organizations to train large data models without additional staff and infrastructure.
The platform scales linearly with your data training and time requirements, allowing you to focus on results.
Hybrid Solution Boosts LLM Training Efficiency
Phison’s aiDAPTIV+ is a hybrid software / hardware solution for today’s biggest challenges in LLM training.
A single local workstation PC from one of our partners provides a cost-effective approach to LLM training, up to Llama 70b.
Scale-out
aiDAPTIV+ allows businesses to scale-out nodes to increase training data size and reduce training time.
Unlock Large Model Training
Until aiDAPTIV+, small and medium-sized businesses have been limited to small, imprecise training models with the ability to scale beyond Llama-2 7b.
Phison’s aiDAPTIV+ solution enables significantly larger training models, giving you the opportunity to run workloads previously reserved for data-centers.
BENEFITS
- Transparent drop-in
- No need to change your AI Application
- Reuse existing HW or add nodes
aiDAPTIV+ MIDDLEWARE
- Slice model, assign to each GPU
- Hold pending slices on aiDAPTIVCache
- Swap pending slices w/ finished slices on GPU
SYSTEM INTEGRATORS
- Access to ai100E SSD
- Middleware library license
- Full Phison support to bring up
SEAMLESS INTEGRATION
- Optimized middleware to extends GPU memory capacity
- 2x 2TB aiDAPTIVCache to support 70B model
- Low latency
HIGH ENDURANCE
- Industry-leading 100 DWPD with 5 year warranty
- SLC NAND with advanced NAND correction algorithm
Partners & Agents
System Integrators
