Most ML engineers leave massive performance gains on the table. Here are 5 techniques you can apply today — no retraining needed.

1. torch.compile() — 20-40% Speedup

model = torch.compile(model, mode="reduce-overhead")

Uses CUDA graphs. First inference is slow, every subsequent call is faster.

2. Channels-Last Memory — Free 10-15%

model = model.to(memory_format=torch.channels_last)
input_tensor = input_tensor.to(memory_format=torch.channels_last)

3. FP16 Inference — 2x Throughput

model = model.half()
with torch.no_grad(), torch.cuda.amp.autocast():
    output = model(input_tensor.half())

4. GPU Preprocessing (NVIDIA DALI)

Move decode + resize + normalize to GPU. Eliminates CPU bottlenecks.

5. Dynamic Batching

Accumulate requests and batch them. Even 4-8 inputs gives significant throughput gains.

Combined: 3-5x total speedup without touching model weights.

Want pruning, quantization, distillation, TensorRT, and edge deployment? That's what the full course covers.

Most ML engineers leave massive performance gains on the table because they think optimization requires retraining. Here are 5 techniques you can apply today.

1. torch.compile() — One Line, 20-40% Speedup

model = torch.compile(model, mode="reduce-overhead")

Uses CUDA graphs under the hood. First inference is slow (compilation), every subsequent call is faster.

2. Channels-Last Memory Format — Free 10-15%

model = model.to(memory_format=torch.channels_last)
input_tensor = input_tensor.to(memory_format=torch.channels_last)

Aligns memory access patterns with how CUDA kernels actually read data.

3. Half Precision (FP16) — 2x Throughput

model = model.half()
with torch.no_grad(), torch.cuda.amp.autocast():
    output = model(input_tensor.half())

Negligible accuracy impact for most vision tasks.

4. GPU Preprocessing with NVIDIA DALI

Move decode + resize + normalize to GPU. Eliminates CPU bottlenecks entirely:

from nvidia.dali.pipeline import pipeline_def
import nvidia.dali.fn as fn

@pipeline_def(batch_size=32, num_threads=4, device_id=0)
def pipeline():
    images = fn.readers.file(file_root="data/")
    images = fn.decoders.image(images, device="mixed")
    images = fn.resize(images, size=[224, 224])
    return fn.crop_mirror_normalize(images,
        mean=[0.485*255, 0.456*255, 0.406*255],
        std=[0.229*255, 0.224*255, 0.225*255])

5. Dynamic Batching — Amortize Kernel Overhead

Don't process one image at a time. Accumulate requests and batch them. Even batching 4-8 inputs gives significant throughput gains.

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Combined effect: 3-5x total speedup without touching model weights.

Want to go deeper into pruning, quantization, distillation, TensorRT, and edge deployment? That's what the full course covers.