g2n — optimize and run PyTorch models
g2n is a PyTorch platform with two halves:
- Optimize — a
torch.compilebackend: custom fusion passes, Triton kernels, a persistent compile cache, and (on Enterprise) full autotuning. Honest framing: steady-state latency is ~torch.compileparity on most blocks — the wins are cold-start time (cached kernels load in <1 ms per restart), VRAM, and the serving layer. - Run — a serving layer that turns those models into a production
inference node: model registry, HTTP server, dynamic batching, quantization
(int8w/int4w), CUDA-graph replay, and VRAM management for small GPUs.
This includes local LLMs:
hf:andgguf:models with packed int4 quantization (fused int4 GEMM on CUDA — faster, lower-VRAM decode), partial CPU offload for models bigger than VRAM, and streaming generation over HTTP.
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/...
# Run a local LLM (Pro+): int4w fits an 8B model on a 6 GB card
g2n.register_model("qwen", "hf:Qwen/Qwen2.5-7B-Instruct",
framework="llm", precision="int4w")
g2n.generate("qwen", "Hello!", on_token=print)
Install
pip install g2n-enterprise # full stack: torch, triton, open-core g2n,
# transformers (hf: LLMs), llama.cpp (gguf: LLMs)
pip install g2n # open-core only (free, Community tier)
The full runtime ships in the base install — no extras to remember. (One
caveat: llama-cpp-python compiles from source, so the install needs a C/C++
toolchain, or a prebuilt wheel from the llama-cpp-python docs.)
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 (sales@g2n.dev).
Documentation
Full documentation lives at https://g2n.dev/docs.
| 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 |
| LLM | Local LLMs: hf:/gguf: engines, int4w/fp8w, SDPA, offload, streaming /generate |
| Quantum | The circuit simulator |
| Licensing | Keys, activation, seats, offline use, renewal |
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.
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