aiDAPTIVLink is Phison’s middleware layer in the aiDAPTIV+ stack that lets you train and run larger language models on your own hardware by leveraging high‑performance SSD caching alongside your GPU VRAM. In practice, it minimizes “out‑of‑memory” barriers for fine‑tuning and keeps I/O efficient during inference.
Key capabilities
- Scale beyond VRAM limits: Use fast NVMe storage as an extension to GPU memory so you can work with bigger models or longer context windows on the same hardware.
- On‑prem by design: Keep data local to your workstation, server, or lab cluster, no cloud dependency.
- Minimal code changes: Designed to drop into existing PyTorch workflows with little or no refactoring.
- Lower time‑to‑first‑token (TTFT) at inference: Storage‑aware scheduling reduces cold‑start stalls so responses begin faster.
Note: aiDAPTIV+ removes memory bottlenecks for training, but it is not intended to speed up the core math of fine‑tuning itself.
aiDAPTIVLink is distributed as part of the aiDAPTIV+ offering.
- Commercial availability: Included with purchases of aiDAPTIVCache (Phison’s NVMe caching solution) for workstations and servers.
- Evaluation / pilots: Reach out for a free trial of aiDAPTIV+.
- Academic programs: Universities and student clubs can inquire about our outreach program for on‑prem testing.
Information on how to install aiDAPTIVLink is found here!
To ensure optimal performance and compatibility, aiDAPTIVLink requires the following software and hardware components:
aiDAPTIVLink is currently supported only on Linux systems.
✅ Recommended Distribution: Ubuntu 24.04.3 LTS (Desktop or Server Edition)
⚙️ Kernel Version: 6.14 or higher
🚫 Not supported: Windows or macOS
aiDAPTIVLink and aiDAPTIV+ have been validated with the following NVIDIA GPUs.
Note: PCIe Gen 4 ×16 bandwidth is recommended for optimal I/O throughput.
| Product Name | Architecture | Memory Type | Capacity | Bandwidth | FP16 TFLOPS | SMs | Power (TDP) | Notes |
|---|---|---|---|---|---|---|---|---|
| NVIDIA H200 NVL | Hopper | HBM3e | 141 GB | 4800 GB/s | 1671 | 132 | 300–600 W configurable | Data center |
| NVIDIA H100 PCIe | Hopper | HBM2e | 80 GB | 2048 GB/s | 1639 | 114 | 350 W | Data center |
| NVIDIA RTX Pro 6000 Blackwell (Workstation) | Blackwell | GDDR7 | 96 GB | 1792 GB/s | 1000 | 188 | 600 W | Flagship Pro |
| NVIDIA RTX Pro 6000 Blackwell (Max-Q) | Blackwell | GDDR7 | 96 GB | 1792 GB/s | 878 | 188 | 300 W | Mobile / Low-Power |
| NVIDIA RTX Pro 6000 Blackwell (Server) | Blackwell | GDDR7 | 96 GB | 1792 GB/s | 1000 | 188 | 400–600 W configurable | Data center |
| NVIDIA RTX 6000 Ada Generation | Ada Lovelace | GDDR6 | 48 GB | 960 GB/s | 728 | 142 | 300 W | Workstation |
| NVIDIA RTX 5000 Ada Generation | Ada Lovelace | GDDR6 | 32 GB | 576 GB/s | 522 | 100 | 250 W | Workstation |
| NVIDIA RTX 4500 Ada Generation | Ada Lovelace | GDDR6 | 24 GB | 432 GB/s | 317 | 60 | 210 W | Workstation |
| NVIDIA RTX 4000 Ada Generation | Ada Lovelace | GDDR6 | 20 GB | 360 GB/s | 214 | 48 | 130 W | Workstation |
| NVIDIA RTX 4000 SFF Ada Generation | Ada Lovelace | GDDR6 | 20 GB | 280 GB/s | 153 | 48 | 70 W | Small Form Factor |
| NVIDIA RTX A6000 | Ampere | GDDR6 | 48 GB | 768 GB/s | 310 | 84 | 300 W | Legacy Pro |
| NVIDIA RTX A5000 | Ampere | GDDR6 | 24 GB | 768 GB/s | 222 | 64 | 230 W | Legacy Pro |
| NVIDIA L40 | Ada Lovelace | GDDR6 | 48 GB | 960 GB/s | — | — | — | Data center |
| NVIDIA L40S | Ada Lovelace | GDDR6 | 48 GB | 960 GB/s | — | — | — | Data center |
| Product Name | Architecture | Memory Type | Capacity | Bandwidth | FP16 TFLOPS | SMs | Power (TDP) | Notes |
|---|---|---|---|---|---|---|---|---|
| GeForce RTX 5090 | Blackwell | GDDR7 | 32 GB | 1792 GB/s | 838 | 170 | 575 W | Desktop |
| GeForce RTX 5080 | Blackwell | GDDR7 | 16 GB | 960 GB/s | 450 | 84 | 360 W | Desktop |
| GeForce RTX 5070 Ti | Blackwell | GDDR7 | 16 GB | 896 GB/s | 352 | 70 | 300 W | Desktop |
| GeForce RTX 4090 | Ada Lovelace | GDDR6X | 24 GB | 1008 GB/s | 661 | 128 | 450 W | Desktop |
| GeForce RTX 4090 D | Ada Lovelace | GDDR6X | 24 GB | 1008 GB/s | — | — | 450 W | China variant |
| GeForce RTX 4080 Super | Ada Lovelace | GDDR6X | 16 GB | 736 GB/s | 418 | 80 | 320 W | Desktop |
| GeForce RTX 4080 | Ada Lovelace | GDDR6X | 16 GB | 717 GB/s | 390 | 76 | 320 W | Desktop |
| GeForce RTX 4070 Ti Super | Ada Lovelace | GDDR6X | 16 GB | 672 GB/s | 321 | 66 | 285 W | Desktop |
| GeForce RTX 3090 Ti | Ampere | GDDR6X | 24 GB | 1008 GB/s | 320 | 84 | 450 W | Legacy |
| GeForce RTX 3090 | Ampere | GDDR6X | 24 GB | 936 GB/s | 285 | 82 | 350 W | Legacy |
| Product Name | Architecture | Memory Type | Capacity | Bandwidth | FP16 TFLOPS | SMs | Power (TDP) | Notes |
|---|---|---|---|---|---|---|---|---|
| GeForce RTX 5090 Laptop GPU | Blackwell | GDDR7 | 24 GB | 896 GB/s | 456 | 82 | 95–150 W | Mobile |
| GeForce RTX 5080 Laptop GPU | Blackwell | GDDR7 | 16 GB | 896 GB/s | 333 | 60 | 80–150 W | Mobile |
| GeForce RTX 4090 Laptop GPU | Ada Lovelace | GDDR6 | 16 GB | 576 GB/s | 343 | 76 | 80–150 W | Mobile |
| GeForce RTX 3080 Ti Laptop GPU | Ampere | GDDR6 | 16 GB | 512 GB/s | 189 | 58 | 80–150 W | Legacy Mobile |
- Minimum VRAM for inference: 20–24 GB (for Llama-3 8B-class models).
- Minimum VRAM for fine-tuning: 16 GB (requires aiDAPTIVCache SSD tiering).
- Interface: PCIe Gen 4 ×16 recommended; PCIe Gen 3 supported with reduced throughput.
- Architectures validated: Blackwell (B100 / RTX 50 Series), Hopper (H100/H200), Ada Lovelace (RTX 40 Series), Ampere (RTX 30 Series).
- In progress: AMD Radeon Instinct and Intel Gaudi series under internal testing.
Specifications validated from Phison Technical Marketing internal testing and official NVIDIA product documentation as of October 2025.
aiDAPTIV+ extends GPU memory by tiering model weights and activations across GPU VRAM, system RAM, and aiDAPTIVCache SSDs.
This lets you fine-tune and run larger models on affordable hardware.
- Training: total usable memory ≈ GPU VRAM + system RAM + aiDAPTIVCache.
- Inference: model weights + KV cache must fit primarily in GPU VRAM (can be reduced with quantization).
| Model | Precision | GPU VRAM | KV Cache | Total VRAM Required |
|---|---|---|---|---|
| Llama-3.2-3B | FP16 | 6 GB | 13.6 GB | 19.6 GB |
| Llama-3.2-3B | Q4 | 1.5 GB | 3.4 GB | 4.9 GB |
| Llama-3.1-8B | FP16 | 16 GB | 15.6 GB | 31.6 GB |
| Llama-3.1-8B | Q4 | 4 GB | 3.6 GB | 7.6 GB |
| Llama-3.1-70B | FP16 | 140 GB | 39 GB | 179 GB |
| Llama-3.1-70B | Q4 | 35 GB | 9.75 GB | 44.75 GB |
| SSD Capacity | Max Model Size Supported | Recommended System RAM |
|---|---|---|
| 320 GB | Up to 13B | 64 GB |
| 1 TB | Up to 34B | 64 GB |
| 2 TB | Up to 70B | 128 GB |
| 4 TB | Up to 180B | 256 GB |
aiDAPTIVLink has been tested with the following high-performance processors:
| Brand | Model | Sockets | Cores | Base Clock (GHz) | PCIe Lanes |
|---|---|---|---|---|---|
| Intel | Xeon Gold 5320 | Dual | 26 | 2.2 | 64 |
| Intel | Xeon Gold 6330 | Dual | 28 | 2.0 | 64 |
| Intel | Xeon w5-3425 | Single | 12 | 3.2 | 112 |
| Intel | Xeon Gold 6538Y+ | Dual | 32 | 2.2 | 80 |
| Intel | Xeon Silver 4410T | Dual | 10 | 2.7 | 80 |
| Intel | Xeon Silver 4410Y | Dual | 8 | 2.0 | 80 |
| Intel | Xeon Silver 5315Y | Dual | 8 | 3.2 | 64 |
| Intel | Core i9-13900 | Single | 24 | 1.5 (base) | 20 |
| Intel | Core i9-12900E | Single | 16 | 1.7 (base) | 20 |
| AMD | Ryzen Threadripper 7980X | Single | 64 | 5.1 | 92 |
| AMD | EPYC 7713P | Single | 64 | 2.0 | 128 |
| AMD | EPYC 9174F | Single | 16 | 4.1 | 128 |
Note: The CPUs listed above are ones Phison has validated internally.
In practice, aiDAPTIV+ can run on a much broader range of processors.
From a hardware standpoint, the key requirements are:
- PCIe Gen 4.0 or better
- ≥ 8 CPU cores
- At least 24 PCIe lanes (16 for a GPU + 2×4 for SSDs)
Most modern Intel and AMD processors released in the last 10 years meet these requirements.
aiDAPTIVLink requires the use of an aiDAPTIVCache SSD for storage-accelerated training and inference.
| Model | Form Factor | Interface | Notes |
|---|---|---|---|
| AI100E | U.2 or M.2 | PCIe 4.0 | Required for aiDAPTIVLink use |
Ensure the AI100E SSD is properly installed in a PCIe Gen 4 compatible slot for peak performance.
aiDAPTIV+ products are available through regional distributors.
Select your country or territory below to view purchase options.
- AI Training PC
- AI Workstation
- AI100E 320 GB M.2
- AI100E 1 TB M.2
- AI100E 2 TB M.2
- AI100E 1 TB U.2
- AI100E 2 TB U.2
Please contact your local Phison partners for aiDAPTIV+ inquiries in regions outside the US (TW, CN, MY, ID, JP).
Note: aiDAPTIV Cache (SSD) is only sold through System Integrators as part of a full AI Training PC / Workstation / Server solution.
Get in touch
- 📩 Sales/General:
peter_cmaylo@phison.com - 🧪 Free Trial:
aaron_pham@phison.com - 🎓 University outreach:
aaron_pham@phison.com
After you receive access, follow the repo’s Installation section to integrate aiDAPTIVLink into your training or inference workflow.
For full installation steps, supported OS details, and performance tuning, see the User Manual.