Run inference and fine-tuning workloads on chipset-optimized runtimes—from Intel Xeon to NVIDIA GPUs—without rewriting code or changing your stack.
Certified by leading chipset manufacturers
Three problems keeping your AI team up at night
LLM inference costs grow faster than revenue. You're paying premium prices for generic cloud infrastructure that wasn't optimized for your specific hardware.
Tied to one cloud provider or chipset vendor. Migration means rewriting everything, so you're stuck negotiating from weakness.
Separate systems for training, fine-tuning, and inference. Your team manages three different platforms, tripling complexity and cost.
Three products. One mission: Make enterprise LLM operations fast, cheap, and simple.
Stop paying for duplicate hardware. Duality manages both workloads with intelligent orchestration that automatically shifts resources based on demand.
Every chipset has unique performance characteristics. We work directly with Intel, NVIDIA, Qualcomm, and AMD to extract maximum throughput from your hardware.
Training custom models shouldn't take weeks or require a PhD. Our fine-tuning platform leverages chipset-specific optimizations to dramatically reduce training time.
Direct partnerships that give you 2-3x better performance than generic AI platforms
Real results from enterprises solving specific deployment challenges
Ship products with embedded AI without the traditional 18-month hardware cycle. Our edge-optimized runtimes work on Qualcomm and Intel chipsets already in your devices.
Stop running separate clusters for inference and training. Duality lets you use the same hardware for both, dramatically reducing your datacenter footprint.
From ADAS systems to in-cabin experiences, our chipset-optimized runtimes deliver consistent inference performance even in extreme conditions.
Run compressed models on IoT devices without sacrificing accuracy. Our optimization techniques work on devices with as little as 4GB memory.
All benchmarks independently verified and reproducible
| Metric | Inference (Quantized) | LoRA/QLoRA Fine-Tuning | Full-Parameter Fine-Tuning | DPO/RLHF |
|---|---|---|---|---|
| Primary Bottleneck | Memory Bandwidth | Compute & Memory Bandwidth | Compute & Inter-GPU Network | Compute & Memory Capacity |
| Key Performance Indicators | TTFT, TPOT, Throughput | Training Throughput, Time-to-Completion | Training Throughput, Time-to-Completion | Reward Model Accuracy, Training Stability |
| VRAM per Billion Parameters | <2GB | <4GB (e.g., <24GB for 7B) | >16GB (e.g., >300GB for 7B) | >20GB (requires multiple copies) |
| Compute Intensity | Low to Medium | Medium | Very High | Very High |
| Multi-Node Network Sensitivity | Low | Low to Medium | Very High | Very High |
Schedule a 30-day proof-of-concept where we deploy on your infrastructure with your models. See real performance gains before any purchase commitment.