Technology
Technology Stack
Our platform is built on a modern, scalable architecture designed for high-throughput AI workloads.
| Layer | Technology | Purpose |
|---|---|---|
| Training | PyTorch, JAX, CUDA | Distributed model training across GPU clusters |
| Inference | ONNX Runtime, TensorRT | Optimized serving for production workloads |
| Orchestration | Kubernetes, Ray | Auto-scaling compute orchestration |
| Data | Apache Spark, Delta Lake | Large-scale data processing pipelines |
| Monitoring | Prometheus, Grafana | Real-time system and model metrics |
Infrastructure
GPUs
256
NVIDIA H100 across 4 clusters
Storage
2PB
Training data and model artifacts
Uptime
99.95%
Production SLA
Our infrastructure spans multiple cloud regions with automatic failover and geo-distributed model serving. Training jobs are managed through a custom scheduler that optimizes GPU utilization across all active experiments.
Model Architecture
We develop custom transformer architectures optimized for specific domains. Our models range from 125M to 70B parameters, each tailored for its target use case.
# Model configuration example
class NativeTransformer:
config = {
"hidden_size": 4096,
"num_layers": 32,
"num_heads": 32,
"vocab_size": 128000,
"context_length": 32768,
}
Benchmarks
| Benchmark | Model | Score | Rank |
|---|---|---|---|
| MMLU | NativeLM-70B | 86.4% | Top 5 |
| HumanEval | NativeCode-13B | 78.2% | Top 10 |
| ImageNet | NativeVision-L | 97.3% | Top 3 |
| GLUE | LinguaCore-v3 | 92.1% | Top 8 |
API Access
Selected models are available through our research API for academic and non-commercial use. Contact us for access credentials and documentation.
API Preview — Our inference API is currently in limited preview. Request access to join the waitlist.