g2n-enterprise 1.7.0

A platform to optimize AND run PyTorch models: license-gated compiler (enhanced planner, persistent cache, multi-accelerator routing), a quantum circuit simulator with batched parameter sweeps, plus a model registry and inference server, on top of open-core g2n.

g2n — optimize and run PyTorch models

g2n is a PyTorch platform with two halves:

• Optimize — a torch.compile backend that makes your existing models faster and lighter: custom fusion passes, Triton kernels, a persistent compile cache, and (on Enterprise) full autotuning.
• Run — a serving layer that turns those models into a production inference node: model registry, HTTP server, dynamic batching, quantization, CUDA-graph replay, and VRAM management for small GPUs.

It also ships a quantum circuit simulator (g2n.quantum) — a classical statevector simulator built on torch, for developing and testing quantum algorithms. It is a simulator, not quantum hardware, and never claims otherwise.

import torch, g2n_enterprise as g2n

g2n.activate("G2N-XXXX-XXXX-XXXX")        # once per machine; cached offline

# Optimize: drop-in
model = g2n.compile(my_model)              # or torch.compile(m, backend="g2n")

# Run: registry -> HTTP inference node on :8900
g2n.register_model("clf", "torchscript:/models/clf.pt",
                   precision="int8w",      # weight-only int8: ~4x less weight memory
                   cuda_graph=True, max_batch=16)
g2n.serve()                                # GET/POST http://host:8900/v1/...

Install

pip install g2n-enterprise[runtime]   # full: torch + triton + open-core g2n
pip install g2n                       # open-core only (free, Community tier)

Python ≥ 3.10. Everything degrades gracefully: no GPU → CPU paths; no license → Community tier; a compile failure → your unmodified model. Your code never breaks because of g2n.

Tiers

	Community (free)	Pro ($49/mo)	Enterprise ($499/mo)
Hybrid fusion + JIT codegen	✓	✓	✓
Enhanced buffer planner (memory fusion)		✓	✓
Persistent compile cache		✓	✓
Model registry + inference server		✓	✓
Quantum simulator: unlimited qubits + fusion	≤24 qubits	✓	✓
Dynamic request batching			✓
Batched quantum parameter sweeps			✓
Multi-accelerator routing + max-autotune			✓
Validated model-zoo configs, priority support			✓

Buy at g2n.dev · seats: Pro 5, Enterprise 25 · 14-day Pro trial on request ([e-mail]).

Documentation

Doc	What it answers
Overview	What g2n is, how the pieces fit, what it is NOT
Getting started	Install → activate → first compile → first serve
Optimize	The compiler: what each tier unlocks, cache, autotune
Serving	The inference node: precision/quantization, batching, HTTP API
Quantum	The circuit simulator
Licensing	Keys, activation, seats, offline use, renewal
License server	Self-hosting / vendor operations + HTTP API

Honest numbers, always

g2n never ships fabricated benchmarks. Every published number is measured by a script in benchmarks/ on named hardware, and the tooling to measure on your hardware is built in:

import g2n_enterprise as g2n
from g2n_enterprise.serve.reference import example_inputs
g2n.benchmark("mlp", example_inputs(batch=32), rounds=200)
# -> eager vs optimized median latency + peak VRAM, on THIS box

Speedups depend on your model and GPU. Measure before you trust — that includes our numbers.