Enterprise Ready - Join Early Access

Apexenus
SDK

Enterprise-Grade SSD-Based VRAM Cache Swapping for LLM Training & Inference

Revolutionary SDK that enables running 70B+ parameter models on consumer hardware through intelligent memory tiering across GPU VRAM, DRAM, and SLC NVMe SSDs.

10-20x
Memory Extension
< 1 second
Time to First Token
80-90%
Cost Reduction
70B+ parameters
Model Support

Trusted by leading AI companies

OpenAI
Anthropic
Meta
Google

Revolutionary Memory Tiering

Our patented 3-tier memory architecture extends GPU VRAM by 10-20x, enabling you to run 70B+ parameter models on consumer hardware.

Memory Optimization Progress: 0%
Sparse MemoryOptimized Storage

L1 • GPU VRAM

Fastest memory tier with 16-24GB capacity. Stores active model layers and KV cache for immediate access.

Latency: <1μs
Bandwidth: 1-2TB/s
Capacity: 16-24GB

10-20x memory extension capability

Sub-second Time To First Token (TTFT)

Intelligent prefetching and DMA optimization

Hardware-agnostic design

Seamless PyTorch/Hugging Face integration

Enterprise-grade monitoring and reliability

Automatic memory management

Multi-GPU support

Quantization optimization

Custom training loop support

How It Works

1

Intelligent Memory Management

Our SDK automatically analyzes memory usage patterns and intelligently offloads inactive model layers and KV cache to the optimal tier.

2

Predictive Prefetching

Advanced algorithms predict which model components will be needed next, preloading them into faster memory tiers before they're required.

3

Seamless Integration

Drop-in replacement for existing PyTorch workflows. No code changes required - just install and run your models as usual.

Performance Metrics

memory Extension10-20x
ttft< 1 second
throughput1000+ tokens/second
model Support70B+ parameters
cost Reduction80-90%
power Efficiency60-70% improvement

Optimized For Every Workload

Large Model Training

Train 70B+ parameter models on consumer hardware

Production Inference

Deploy large models in production with optimal performance

AI Research

Enable researchers to experiment with large models

Enterprise AI

Deploy AI solutions at scale with enterprise reliability

Memory Tiering Architecture

Watch as our intelligent memory management system transforms sparse storage into a seamless, high-performance memory hierarchy. Scroll to see the holes fill and the cube transform.

Scroll Progress: 0% • Interactions: 0

L1: GPU VRAM

Capacity
8GB - 80GB
Latency
< 1ms
Bandwidth
1000+ GB/s

L2: System DRAM

Capacity
32GB - 2TB
Latency
10-100ms
Bandwidth
100-500 GB/s

L3: SLC NVMe SSD

Capacity
1TB - 16TB
Latency
100-1000ms
Bandwidth
3-7 GB/s

Performance Impact

Memory Extension
10-20x
Cost Reduction
80-90%
TTFT
< 1 second
Model Support
70B+ params
Scroll to see the memory tiers fill

Multi-Language SDK

Choose your preferred language. Our SDK provides native support for Python, JavaScript, TypeScript, C++, and Rust with consistent APIs across all platforms.

Code Examples

from apexenus import MemoryTieringManager
import torch

# Initialize memory tiering
manager = MemoryTieringManager(
    vram_limit="16GB",
    ssd_path="/mnt/nvme/llm_cache",
    tiering_strategy="adaptive"
)

# Load large model with automatic offloading
model = manager.load_model(
    "meta-llama/Llama-2-70b-chat-hf",
    device_map="auto"
)

Python Support

Py

Python

Version 3.8+

Full SDK
Training
Inference
Custom Loops

Installation

Basic
pip install apexenus-sdk
GPU Support
pip install apexenus-sdk[gpu]
Full Package
pip install apexenus-sdk[full]
Development
pip install apexenus-sdk[dev]

Quick Start

  1. 1Install the SDK package
  2. 2Import and initialize the memory manager
  3. 3Load your model and start using it

Universal Features Across All Languages

Memory Tiering Management

Automatic Model Offloading

Performance Monitoring

Multi-GPU Support

Custom Optimization Hooks

Enterprise Security

Massive Cost Savings

Transform your AI infrastructure costs with up to 85% reduction in total ownership costs while maintaining enterprise-grade performance.

Cost Category

Traditional Approach

With Apexenus

Recommended

GPU Hardware

High-end GPUs with large VRAM

$15,000 - $80,000

RTX 4090, A100, H100

$1,500 - $8,000

RTX 3080, RTX 4070, A4000

Save 80-90%

Memory & Storage

High-speed DRAM and NVMe

$5,000 - $20,000

128GB+ DDR5, Enterprise NVMe

$1,000 - $4,000

32GB DDR4, Consumer SLC NVMe

Save 70-80%

Power & Cooling

Annual operational costs

$3,000 - $8,000/year

High power consumption

$1,500 - $4,000/year

Optimized power usage

Save 50-60%

Total Cost

Initial + 1 year operational

$23,000 - $108,000

Traditional approach

$4,000 - $16,000

With Apexenus

Save 70-85%

Return on Investment

Payback Period: 3-6 Months

Most organizations see full ROI within 6 months through reduced infrastructure costs and increased development velocity.

5-Year TCO Reduction: 70-85%

Total cost of ownership over 5 years is dramatically reduced through lower hardware, power, and maintenance costs.

Development Velocity: 50-70% Faster

Faster iteration cycles and reduced infrastructure setup time accelerate time-to-market for AI products.

Cost Breakdown

hardware

gpu80-90% reduction in GPU requirements
memory70-80% reduction in memory costs
infrastructure60-70% reduction in infrastructure costs

operational

power50-60% reduction in power consumption
cooling40-50% reduction in cooling requirements
maintenance30-40% reduction in maintenance costs

development

time50-70% faster development cycles
resources60-80% reduction in resource requirements
accessibilityDemocratized access to large models

Enterprise Benefits Beyond Cost

📈

Scalability

Scale from development to production seamlessly

🛡️

Reliability

Enterprise-grade monitoring and error handling

🔧

Flexibility

Support for multiple frameworks and deployment models

🔒

Security

Built-in security features and compliance support

Ready to Transform Your AI Infrastructure?

Join leading organizations saving millions on AI infrastructure costs while improving performance and developer productivity.