aiDAPTIV ^TM

Faster Inference and Larger LLM Training, Done Privately On-Prem

Overview

When memory is the limit, compute is not enough

Modern AI often hits a memory wall before it runs out of compute.

Pascari aiDAPTIV™ is a purpose-built Phison solution for AI systems, from client PCs and workstations to edge deployments and servers. It combines aiDAPTIV Middleware and aiDAPTIV Cache Memory to extend usable AI memory across GPU memory, system memory, and a dedicated flash capacity tier.

Run larger models. Retain more context.

AI workloads demand more memory than you think

A model may load cleanly until the real work begins. Add a long document, RAG material, tool definitions, agent state, or a larger MoE model, and the same system can run out of room.

Model weights consume capacity before the first prompt is processed

KV cache grows with conversations, documents, and retrieved context

Agent workflows carry instructions, tools, observations, and working artifacts across many steps

MoE models need the full expert pool available even though only a subset is active for each token

Fine-tuning adds gradients, optimizer state, activations, and training data

A model may load cleanly until the real work begins. Add a long document, RAG material, tool definitions, agent state, or a larger MoE model, and the same system can run out of room.

A memory system built for AI data

Memory tier	Primary role	Typical data examples
GPU memory or unified memory	Immediate computation	Active model state, active KV cache, active experts, current training layers
System memory	Staging and expanded capacity on discrete-GPU systems	Prefetched data, intermediate cache, state likely to be needed soon
aiDAPTIV Cache Memory	Retained capacity	Less-active KV cache, colder experts, staged model or training data

Memory Tier

GPU memory or unified memory

Primary role

Immediate computation

Typical data examples

Active model state, active KV cache, active experts, current training layers

Memory Tier

System memory

Primary role

Staging and expanded capacity on
discrete-GPU systems

Typical data examples

Prefetched data, intermediate cache, state likely to be needed soon

Memory Tier

aiDAPTIV Cache Memory

Primary role

Retained capacity

Typical data examples

Less-active KV cache, colder experts, staged model or training data

What aiDAPTIV enables

Keep more context

Run a larger model

Adapt a larger model

What is happening?

Start here

Long prompts, RAG, or repeated documents make time to first token painful

A sparse MoE model does not fit in available memory

Fine-tuning ends with out-of-memory errors

My model or total context does not fit, and I am not sure which memory limit is responsible

aiDAPTIV ^TM

Faster Inference and Larger LLM Training, Done Privately On-Prem

Overview

When memory is the limit, compute is not enough