Cut AI Infrastructure Costs 60%.
Ship 10x Faster.

Improve your model inference by using Fusera's ML-powered compiler. Change torch.compile() to fusera.compile() and slash your infrastructure costs while delivering real-time AI.

Our Product

Compile Once. 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

FUSERA

Optimization & Compilation

Fusera

Inference Serving

TorchServe • Triton • vLLM

Production Deployment

Docker • K8s • Cloud APIs

Monitoring & Scaling

Prometheus • DataDog • Custom

inference.py
Before
import torch

from transformers import AutoModel

# Load your model
model = AutoModel.from_pretrained("bert-base-uncased")

# Compile for optimization
compiled_model = torch.compile(model)


After
import torch
+ 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)
Our Value

Build AI Products. Skip the Performance Engineering.

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
Our Vision

Forging the Learned Compiler Stack.

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.

Now

Pytorch → CUDA (Inference)

Proving our learning system.

"
Next

Add Training Support

Extend to training graphs and broader PyTorch op coverage.

"
Soon

Multi-Framework, Multi-Hardware

Support TensorFlow, JAX, ONNX. Compile to CPUs, TPUs, ASICs.

"
Later

Fully Learned Compiler

Online + offline learning. MoE, dynamic compute, adaptive kernels.

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Every customer makes Fusera smarter. Every optimization benefits the entire network.
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