aiDAPTIV TM

更快的 AI 推理性能与更大规模的 LLM 训练，全程在本地部署（On-Prem）私有环境中完成

Technical Resources 

A benchmark only means something if you can see how it was made

Every published result identifies the workload, system, runtime, comparison, and limits.

What to show
Includes
Workload
Model, precision, prompt or dataset, task
System
CPU/GPU, unified-memory or discrete-memory architecture, operating system, cache memory configuration
Runtime
Version, quantization, relevant settings
Comparison
Baseline, metric, conditions, tradeoffs, limits
What to show

Workload

Includes

Model, precision, prompt or dataset, task

What to show

System

Includes

CPU/GPU, unified-memory or discrete-memory architecture, operating system, cache memory configuration

What to show

Runtime

Includes

Version, quantization, relevant settings

What to show

Comparison

Includes

Baseline, metric, conditions, tradeoffs, limits

Whitepapers and technical guides

Beyond VRAM and DRAM: Extending and Reusing KV Cache 

Learn how Pascari aiDAPTIV™ can extend eligible KV cache retention and reuse compatible shared input across document, RAG, coding, and agent workflows.

Download the whitepaper
aiDAPTIV Middleware Resources

Access current aiDAPTIVLink 2 installation guidance, environment requirements, and fine-tuning workflow resources.

View aiDAPTIV Middleware on GitHub
Explore the technology
How aiDAPTIV works
KV cache extension & reuse
Dynamic MoE
Elastic fine-tuning

SEAMLESS INTEGRATION

  • Optimized middleware to extends GPU memory capacity
  • 2x 2TB aiDAPTIVCache to support 70B model
  • 低延迟

HIGH ENDURANCE

  • 业界领先，高达100次的五年内每日写入次数（DWPD）
  • 采用业界先进NAND 纠错算法的SLC NAND

aiDAPTIV+ BENEFITS

  • 即插即用，无缝集成
  • 无需修改现有 AI 应用
  • Reuse existing HW or add nodes

aiDAPTIV+ MIDDLEWARE

  • 模型自动分割与 GPU 资源调度
  • Hold pending slices on aiDAPTIVCache
  • Swap pending slices w/ finished slices on GPU

FOR SYSTEM INTEGRATORS

  • Access to ai100E SSD
  • Middleware library license

  • Full Phison support to bring up