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Storage-Attached AI Runtime
Your GPU Just Got
Infinitely Bigger
One middleware layer intercepts GPU out-of-memory crashes and silently extends VRAM across NVMe SSDs — no code changes, no new GPUs. Run 100B+ parameter models on a single workstation, today.
8×
memory reach
100B+
parameter models on-premises
Zero
code changes
24×
faster TTFT
Three-Tier Memory Architecture

How aiDAPTIV™ extends VRAM

Tier 01 - Compute
GPU VRAM

Active Compute Layer

The fastest and most constrained resource. Under aiDAPTIV™, VRAM only holds the currently-active neural layers and Attention Heads — not the entire model — freeing it for pure computation.

Saturated — Computing
Tier 02 - Buffer
KV Buffer

High-Speed Transit Tier

A vital bridge where the aiDAPTIVLink middleware proactively stages pending tensors and buffers transient KV data, minimizing latency during active GPU computation.

Dynamic Prefetching
Tier 03 - Extension
aiDAPTIV™ Cache MEMORY

Infinite Storage Pool

Phison's FTL is tuned explicitly for AI tensor read/write patterns. Idle model parameters and KV Cache are stored here — terabytes of logical VRAM. Out-Of-Memory is eliminated.

Deep Storage Pool
Architecture Comparison

Scale-Out vs. Scale-Up

Legacy — Scale-Out

Networked GPU Cluster

CapEx$3M+
BottleneckNetwork (NVLink / IB)
ChallengeLow compute utilization
VS

aiDAPTIV™ — Scale-Up

Storage-Attached AI

CapEx$50K
AdvantagePCIe NVMe Expansion
BenefitMaximize compute capacity
Empirical Performance & Cost

Measured results

Training Time (hours) by Model Size

Single node, 4× GPU — aiDAPTIV™ vs. GPU-only

with aiDAPTIV™
GPU card only
Training Set Size
140 GB
260 GB
1,400 GB
Min GPU Count
aiDAPTIV™ / GPU-only
4 / 4
4 / 6
4 / 30

Scaling is linear based on GPU count and model size. Training based on 10M tokens.

Quicker Time to First Token for Faster Inference

aiDAPTIV™ vs. GPU card only — 8B model · hover dots for details

with aiDAPTIV™
GPU card only
Core Mechanisms

Four innovations that make it possible

1
Transparent Hooking
aiDAPTIV™ intercepts PyTorch's memory allocator at the lowest level. Applications require zero code changes — the system seamlessly redirects allocations to the SSD tier without any application-level intervention.
2
Predictive Pre-fetching
aiDAPTIVLink accurately predicts and streams the next required model slices from the SSD into VRAM while the GPU computes active tensors. This just-in-time data swap effectively prevents GPU starvation.
3
KV Cache Optimization
For long-context inference, computed KV Cache states are persisted to SSD. Subsequent queries retrieve these with O(1) complexity — eliminating O(N²) redundant computation and delivering up to 10× speed improvement.
4
Hardware Co-Design
Standard SSDs degrade quickly under continuous AI tensor workloads. Phison engineered the Flash Translation Layer (FTL) from scratch to optimise garbage collection and wear-levelling specifically for tensor movement patterns.
Runtime Trace

What happens when OOM is intercepted

aidaptiv-tracer — tail -f /var/log/aidaptiv.log
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