Own Forever.
Fusera slots directly into your inference pipeline at the compilation phase. Simply compile your model once using Fusera, then feel free to use it anywhere, from internal tools to production pipelines.
Model Development
PyTorch • HuggingFace • JAX
Model Export
ONNX • TorchScript • SavedModel
Inference Serving
TorchServe • Triton • vLLM
Production Deployment
Docker • K8s • Cloud APIs
Monitoring & Scaling
Prometheus • DataDog • Custom
Model Development
PyTorch • HuggingFace • JAX
Model Export
ONNX • TorchScript • SavedModel
Optimization & Compilation
Fusera
Inference Serving
TorchServe • Triton • vLLM
Production Deployment
Docker • K8s • Cloud APIs
Monitoring & Scaling
Prometheus • DataDog • Custom
from transformers import AutoModel
# Load your model
model = AutoModel.from_pretrained("bert-base-uncased")
# Compile for optimization
compiled_model = torch.compile(model)
+ import fusera
from transformers import AutoModel
# Load your model
model = AutoModel.from_pretrained("bert-base-uncased")
- # Compile for optimization
- compiled_model = torch.compile(model)
+ # Compile with Fusera optimization
+ compiled_model = fusera.compile(model)
Build AI Products.
Fusera automatically optimizes your PyTorch models so your team can ship features instead of debugging performance bottlenecks. The best part is we use generator-verifed infra, so we can guarantee that our compiled code is both correct and performant.
What Your Team Can Build
- 💬 Chatbots with human-like response times
- 🤖 Multi-agent workflows that actually scale
- 🔄 Synthetic data generation that doesn't break the bank
What Your Team Stops Doing
- ⚙️ Writing custom CUDA kernels
- 🔍 Profiling inference bottlenecks
- 💸 Scaling hardware to fix software problems
Forging the
Pytorch inference optimization is the first step towards the future, a learned compiler stack. We plan on expanding our compiler to enhance both training and testing, allowing major frameworks and hardwares, and even data dependent architectures.