Kill the NVIDIA Tax.
Run your AI models on any silicon.
Tony is an intelligent virtualization layer for AI compute. Compile, optimize, and deploy models across any chipset with zero code changes and near-zero latency.
Currently vetting early-stage AI labs for our private beta.
import tony_ai
# Initialize the virtualization layer
engine = tony_ai.init()
# Abstract the hardware and compile seamlessly
model = engine.compile(
model_id="meta-llama/Llama-3-70B",
target_hardware="amd-rocm", # Seamless execution on alternative chips
optimize="latency"
)
model.deploy(cluster="tony-virtual-pool")
print("Model running efficiently at 100% capacity.")Architecture
How It Works: Breaking the Hardware Monopoly
A single virtualization layer sits between your models and the underlying silicon — translating, optimizing, and dispatching at runtime.
Your AI Models
PyTorch, ONNX, Hugging Face Hub
Tony Virtualization Layer
Dynamic Compiler Middleware & Kernel Optimizer
Any Silicon Infrastructure
AMD ROCm, Intel Gaudi, Apple Silicon, Specialized ASICs
Platform
Built for teams running models at the edge of feasibility.
Hardware Abstraction Middleware
Automatically translate and map complex computational graphs (PyTorch, ONNX) to non-NVIDIA runtimes using advanced compiler virtualization.
Zero-Latency Kernel Optimization
Eliminate memory-mapping bottlenecks between host RAM and alternative VRAM architectures for maximum execution speed.
Unified Infrastructure API
Bring multi-cloud and multi-hardware flexibility to your MLOps workflow with a single, production-ready SDK layer.
Why now
The compute bottleneck is stalling AI innovation. Tony breaks the hardware monopoly by turning fragmented silicon into a unified, accessible, and hyper-optimized compute pool.