I Built a Neural Network Inference Engine From Scratch in C++ (No PyTorch, No ONNX, Just AVX2)
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Why does inference need a framework at all? Every time I ran a tiny linear model through PyTorch, I felt like I was driving a go-kart with a jet engine strapped to it. The model was a few hundred KB. PyTorch's runtime was gigabytes. Somewhere between model(x) and the actual floating-point math, an entire universe of abstraction — autograd graphs, dispatch layers, tensor metadata — was quietly eating my CPU cycles. So I asked a simple question: what does inference actually look like with nothing…
1Key Takeaways
- Why does inference need a framework at all?
- Every time I ran a tiny linear model through PyTorch, I felt like I was driving a go-kart with a jet engine strapped to it.
- Somewhere between model(x) and the actual floating-point math, an entire universe of abstraction — autograd graphs, dispatch layers, tensor metadata — was quietly eating my CPU cycles.
- So I asked a simple question: what does inference actually look like with nothing….
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that why does inference need a framework at all?
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