AMEVA-Forge vs PyTorch Comparison
AMEVA-Forge was built from the ground up to provide identical API ergonomics to PyTorch while operating natively within browser sandboxes.
Feature & Operational Comparison
| Feature | PyTorch | AMEVA-Forge (Release 1.0) |
|---|---|---|
| Primary Runtime | C++ Native / Python C-API (CUDA/ROCm/CPU) | Browser WebGPU (WGSL) / Pyodide WASM |
| Installation Footprint | ~1 GB - 4 GB (Heavy binary package) | < 500 KB (Zero install via browser) |
| Target Workloads | Data center LLMs, HPC, Distributed clusters | Educational AI, Edge Small Models, In-Browser Training |
| API Style | torch.nn, torch.optim, Autograd |
100% PyTorch Compatible (forge.nn, forge.optim) |
| Zero-Leak Memory | Custom C++ Memory Caching Allocator | Weakref GC Finalizer + WebGPU Staging Buffer Pool |
API Syntax Parity
Code written in AMEVA-Forge looks and feels identical to standard PyTorch code:
# ── PyTorch ──
import torch
import torch.nn as nn
model = nn.Sequential(nn.Linear(2, 4), nn.ReLU(), nn.Linear(4, 1))
out = model(torch.randn(4, 2))
loss = out.sum()
loss.backward()
# ── AMEVA-Forge (Identical API!) ──
import forge as torch
import forge.nn as nn
model = nn.Sequential(nn.Linear(2, 4), nn.ReLU(), nn.Linear(4, 1)).to("gpu")
out = model(torch.randn(4, 2, device="gpu"))
loss = out.sum()
loss.backward()