Tesla K80 GPU是否支持Int8？量化深度学习模型部署可行性咨询

Great question! Let’s break down whether the Tesla K80 is a fit for your Int8-quantized deep learning model deployment, covering both support and practical real-world suitability.

1. Does the Tesla K80 support Int8? #

The Tesla K80 is built on NVIDIA’s Kepler architecture (GK210 GPU cores). The key thing to note here is that Kepler does not have native hardware support for Int8 instructions—this capability was first introduced with the Pascal architecture (e.g., Tesla P4/P100) and expanded in later generations like Volta, Turing, and Ampere with dedicated Int8 Tensor Cores.

That said, you can still run Int8-quantized models on a K80, but it will be via software emulation. Tools like TensorRT or PyTorch’s quantization libraries will fall back to using general-purpose integer arithmetic instructions to simulate Int8 computations. The catch? This emulation doesn’t deliver the performance gains you’d expect from hardware-accelerated Int8—you’ll likely see minimal (if any) speedup over FP32 inference, and in some cases, it might even run slower due to the overhead of software emulation.

2. Is the K80 suitable for this deployment scenario? #

This depends entirely on your priorities:

• If you’re aiming for maximum performance gains or production-grade deployment: No, the K80 is not a good choice. The lack of hardware Int8 support means you won’t get the 2-4x speedup or ~75% memory reduction that hardware-accelerated Int8 provides. For production, opt for Pascal+ generation GPUs (e.g., Tesla T4, A10G, L4) that have native Int8 support.
• If you’re just validating model correctness or have low-performance requirements: Yes, it can work. You’ll still get the memory savings from Int8 quantization (since model weights are stored as 8-bit integers), but don’t expect a meaningful boost in inference speed.
• Pro tip: If you do proceed with the K80, keep an eye on logs from tools like TensorRT—they’ll explicitly warn you when falling back to software emulation for Int8 operations.

内容的提问来源于stack exchange，提问作者Jeyadevan Rajan