Pioneering the Next
Generation of AI
Our breakthrough models deliver near-GPT-3.5 performance with 45× less memory and 100× lower GPU cost, enabling true edge AI deployment and revolutionizing how intelligent systems are built.
Breakthrough Achievements
Revolutionary performance metrics that redefine what's possible in AI efficiency and deployment.
Efficiency Leader
RIT-1B achieves 42.5 efficiency score vs 0.54 for GPT-3.5
Ultra-Low GPU Cost
$150 GPU cost vs $15K for comparable cloud solutions
True Edge AI
RIT-1B runs on integrated graphics with enterprise-grade performance
SDCA Patent
Revolutionary attention mechanism with 30× efficiency gains
The Genovation Advantage
While others focus on scaling model size, we've revolutionized efficiency. Our models deliver comparable performance to much larger systems while enabling deployment scenarios previously impossible.
GPU Cost Efficiency Analysis
Comprehensive efficiency comparison based on pure GPU costs. RIT series models deliver unmatched performance-per-dollar with consumer-grade hardware.
Performance vs GPU Cost Analysis
| Company/Model | Performance | GPU Cost | Efficiency Score | Cost/Point | Memory | Deployment |
|---|---|---|---|---|---|---|
Genovation rit-1b-instruct-1.3 | 63.8% MMLU: 55.9% | $150 GPU Requirements: Integrated Graphics / GTX 1650 ~2GB VRAM needed Cost: $150 GPU hardware | 42.5 perf/cost ratio | $2.35 | ~2GB | Edge |
Genovation rit-1b-instruct-1.1 | 63.6% MMLU: 56.2% | $150 GPU Requirements: Integrated Graphics / GTX 1650 ~2GB VRAM needed Cost: $150 GPU hardware | 42.4 perf/cost ratio | $2.36 | ~2GB | Edge |
Genovation rit-4b-instruct | 79.8% MMLU: 69.3% | $400 GPU Requirements: RTX 4060 / RTX 3070 ~8GB VRAM needed Cost: $400 GPU hardware | 20.0 perf/cost ratio | $5.01 | ~8GB | Near-Edge |
Mistral Mistral-7B | 75.6% MMLU: 62.8% | $1.2K GPU Requirements: RTX 4090 / RTX A5000 ~14GB VRAM needed Cost: $1,200 GPU hardware | 6.3 perf/cost ratio | $15.87 | ~14GB | Cloud/Server |
Meta LLaMA-2-7B | 59.1% MMLU: 45.9% | $1.2K GPU Requirements: RTX 4090 / RTX A5000 ~14GB VRAM needed Cost: $1,200 GPU hardware | 4.9 perf/cost ratio | $20.31 | ~14GB | Cloud/Server |
Meta LLaMA-7B | 53.7% MMLU: 32% | $1.2K GPU Requirements: RTX 4090 / RTX A5000 ~14GB VRAM needed Cost: $1,200 GPU hardware | 4.5 perf/cost ratio | $22.35 | ~14GB | Cloud/Server |
TII Falcon-7B | 50.4% MMLU: 23.9% | $1.2K GPU Requirements: RTX 4090 / RTX A5000 ~14GB VRAM needed Cost: $1,200 GPU hardware | 4.2 perf/cost ratio | $23.81 | ~14GB | Cloud/Server |
OpenAI GPT-3.5 | 80.3% MMLU: 70% | $15.0K GPU Requirements: Multiple A100 80GB ~350GB VRAM needed Cost: $15,000 GPU hardware | 0.5 perf/cost ratio | $186.80 | ~350GB | Cloud Only |
OpenAI GPT-4 | 92.8% MMLU: 86.4% | $80.0K GPU Requirements: H100 GPU Cluster ~3,600GB+ VRAM needed Cost: $80,000 GPU hardware | 0.1 perf/cost ratio | $862.07 | ~3,600GB+ | Cloud Only |
GPU Cost Analysis
- • Pure GPU: Graphics card cost only
- • VRAM: Memory required for model
- • Hardware: Consumer vs enterprise grade
- • Tooltip: Hover for GPU details
Efficiency Metrics
- • Efficiency Score: Performance ÷ GPU Cost × 100
- • Cost/Point: GPU Cost ÷ Performance Score
- • Genovation models: RIT series leadership
GPU Requirements
- • Edge: Integrated/basic GPU sufficient
- • Near-Edge: Consumer GPU (RTX series)
- • Cloud: Enterprise GPUs required
Key Insights
- • RIT-1B: 42.5 efficiency vs 0.54 for GPT-3.5
- • 100× lower GPU cost for comparable performance
- • Edge deployment with basic hardware
RIT Series - Small Language Models
Our RIT series represents a breakthrough in AI efficiency, delivering enterprise-grade performance with dramatically reduced GPU requirements. Built on our patented SDCA technology.
RIT-1B
1 Billion Parameters
Revolutionary mobile AI enabling sophisticated language processing on smartphones, IoT devices, and embedded systems. Unmatched efficiency with 42.5 efficiency score.
RIT-4B
4 Billion Parameters
The perfect balance of performance and efficiency. Delivers near-GPT-3.5 capabilities while running on consumer hardware with 37× lower GPU cost than GPT-3.5.
GPU Cost Comparison
RIT-7B+ Series
Our next-generation models push the boundaries even further. RIT-7B and larger variants will deliver unprecedented performance while maintaining our signature efficiency advantages.
Breakthrough Innovation
Semantic Distance-based Compression Attention (SDCA)
Our patented SDCA mechanism revolutionizes neural attention by dynamically compressing tokens based on their semantic distance from focal points, achieving up to 30× computational efficiency gains while preserving critical information.
Key Innovations
Research Focus Areas
Our interdisciplinary research spans multiple domains of AI, from fundamental architectures to practical applications.
Neural Architecture Evolution
Pioneering next-generation attention mechanisms, compression techniques, and architectural innovations that redefine computational efficiency in neural networks.
GPU-Efficient AI
Developing ultra-efficient models that deliver maximum performance per GPU dollar spent, revolutionizing the economics of AI deployment across all industries.
Edge AI Revolution
Creating ultra-efficient models that bring enterprise-grade AI to mobile devices, IoT systems, and resource-constrained environments worldwide.
Autonomous Intelligence
Developing self-governing AI systems that can reason, plan, and execute complex tasks while maintaining transparency and human oversight.
AI Safety & Governance
Building frameworks for explainable AI, auditable decision-making, and responsible deployment that ensure AI systems remain beneficial and controllable.
Multi-Modal Fusion
Seamlessly integrating vision, language, and sensor data for comprehensive AI systems that understand and process multiple data modalities simultaneously.
Join Our Research Mission
Collaborate with our research team to push the boundaries of AI efficiency and build the intelligent systems of tomorrow.