Just catching up on this thread. Look, if you want that prototype finished by year end, sticking with Python is your only logical path. Diving into C++ now is just gonna introduce unnecessary overhead and slow down your development cycle significantly. You dont need to worry about high-performance deployment until the prototype actually functions. However, if you feel limited, I would suggest looking at NVIDIA Jetson Orin Nano 8GB for hardware acceleration, as it handles inference pretty well without needing deep C++ knowledge. You might want to consider using Google Coral USB Accelerator Edge TPU if your project involves vision tasks, as it offloads the heavy lifting from your CPU while still using simple Python scripts. Be careful with dependencies though, as you need to make sure your environment stays clean. You could also experiment with Intel OpenVINO Toolkit which is fantastic for optimizing models before you deploy. Just stick to the basics for now. Get your model training on a standard workstation first and dont get distracted by optimization issues yet. Most beginners hit a wall because they try to optimize before they even have a working loop. Focus on the logic, get the math right, and keep your stack simple. You can always rewrite parts in C later if the latency becomes a real issue, but for a local project, that is unlikely to be your bottleneck.

Honestly, stick to Python. Trying to force yourself into C++ when you are already feeling burnt out is a recipe for disaster. Unfortunately, my experience trying to learn low-level deployment while building a prototype was a nightmare. I had issues with memory management that wasted weeks of my time and the debugging process just was not as good as expected for someone trying to move fast. You should prioritize Python because the ecosystem is simply too vast to ignore right now. Stick to these for your prototype:

• Python Software Foundation Python 3.12 for the core language logic.
• Meta PyTorch 2.4.0 because the documentation is actually manageable for beginners.
• Google TensorFlow 2.17.0 for when you need to deploy something slightly more rigid. Python handles the complexity of neural nets way better than you might expect. If you hit a performance bottleneck later, it is usually because of inefficient code, not the language itself. Focus on writing clean scripts first. I tried running my initial models on a cheap NVIDIA GeForce RTX 4060 8GB GDDR6 card and it performed just fine for local testing. Just keep it simple and do not overcomplicate your stack before you even have a working model. You really do not need to worry about C++ until you are scaling things for enterprise environments, which is a totally different beast.

> I dont have the time to learn both at once since Im trying to build a prototype for a local project by the end of the year and my brain is already fried. Totally get the stress, man. I was in your shoes a few years back and honestly, you should just stay with Python. You really wont hit a wall for a long time. For local projects, the overhead of C++ is just unnecessary friction when you just need to get a model running. If you want to keep your setup snappy, I swear by using a good IDE like JetBrains PyCharm Professional 2024.1 because the debugger is a lifesaver when your tensors are acting up. It makes navigating libraries so much easier. For learning, dont bother with expensive stuff. Just hit up the fast.ai courses; they are completely free and teach you how to build stuff first instead of drowning you in theory. Also, check out the documentation for Google TensorFlow 2.16.1 if you need something robust for production-style prototyping later on. It plays nice with Python and handles the heavy lifting without you needing to touch low-level code. Focus on getting your prototype done first. Once it works, you can always optimize or rewrite bits in another language if you actually run into performance issues, but 99 percent of the time, Python is more than enough for what you're trying to build.