How does quantization (such as int8 quantization or using float16) affect the accuracy and speed of Sentence Transformer embeddings and similarity calculations?

Quantization reduces the numerical precision of model weights and activations, which impacts both the accuracy and speed of Sentence Transformer embeddings and similarity calculations. Lower precision (e.g., int8 or float16) makes models smaller and faster but can introduce minor to moderate accuracy trade-offs. For example, converting a model from float32 to float16 cuts memory usage by half, while int8 reduces it further (by ~75%), enabling faster computations on hardware optimized for lower precision. However, reduced precision may lead to rounding errors or loss of fine-grained details in embeddings, which can affect similarity scores.

Speed improvements depend on hardware support and quantization method. Modern GPUs and TPUs have specialized units for float16 and int8 operations, allowing parallel processing of more data. For instance, using float16 with a compatible GPU can reduce inference time by 20-50% compared to float32, as seen in benchmarks with models like all-MiniLM-L6-v2. Int8 quantization often requires additional steps (e.g., calibration to map float32 ranges to int8), but once applied, it can double inference speed in CPU-bound scenarios. However, not all operations benefit equally: matrix multiplications gain the most, while layer normalizations or activations may see smaller gains. Tools like ONNX Runtime or PyTorch’s torch.quantize automate these optimizations, but developers must test latency reductions in their specific environments.

Accuracy impacts vary by task and dataset. For example, in semantic similarity tasks, float16 typically preserves ~99% of float32 accuracy because embeddings retain sufficient precision. Int8, however, may drop accuracy by 1-5% depending on the model and calibration data. A poorly calibrated int8 model might misrank pairs in a retrieval system (e.g., returning a similarity score of 0.85 instead of 0.92 for a critical match). To mitigate this, libraries like Hugging Face transformers offer post-training quantization with calibration datasets, which minimizes accuracy loss. For most applications, float16 strikes a practical balance, while int8 is better suited for resource-constrained deployments where slight accuracy drops are acceptable. Testing with domain-specific data (e.g., legal text vs. social media posts) is essential to gauge real-world impact.