FP8 Conversion: Direct Or Via BF16?

Alex Johnson

-Dec 3, 2025

Hey there! Ever wondered about the best way to convert your models to FP8? It's a hot topic in the world of machine learning, especially when dealing with different numerical formats like BF16. Let's dive into the nitty-gritty of FP8 conversion, and whether you can go straight in or if you need to detour via BF16.

Understanding FP8 and BF16

Before we jump into the conversion process, let’s quickly recap what FP8 and BF16 are and why they matter. In the realm of deep learning, numerical precision is crucial. It determines how accurately we can represent the weights, activations, and gradients in our models.

BF16 (Brain Floating Point 16): This is a 16-bit floating-point format that has become popular due to its balance between range and precision. BF16 maintains a wider dynamic range similar to FP32 (32-bit floating point) but with fewer bits, making it efficient for both training and inference.
FP8 (Floating Point 8): As the name suggests, FP8 is an 8-bit floating-point format. It's even more compact than BF16, offering significant memory savings and speed improvements. However, the trade-off is lower precision, which can be a concern for some models.

The big question then becomes: Can we directly convert to FP8, or do we need to go through BF16 first? Let's explore this.

Direct Conversion to FP8: Is It Possible?

So, can you directly convert your models to FP8? The short answer is, it depends. Converting directly to FP8 can be a tempting idea, especially if you're aiming for maximum efficiency. However, there are some factors to consider. Direct FP8 conversion might be feasible if:

The Original Model Format: If your model is already in a lower precision format (like FP16 or BF16), a direct conversion might be smoother. Starting from a higher precision format (like FP32) and directly jumping to FP8 might lead to significant information loss. You need to carefully evaluate if the model can still perform adequately after such a drastic reduction in precision.
The Model Architecture: Some models are more resilient to lower precision formats than others. For instance, smaller models or those trained with quantization-aware training might handle FP8 conversion better. Quantization-aware training is a technique where the model is trained with the knowledge that it will eventually be quantized, making it more robust to precision loss.
The Conversion Tools and Techniques: The tools and techniques used for conversion play a significant role. Some conversion methods are more sophisticated and can mitigate the loss of precision during the conversion process. It's crucial to use tools that are specifically designed for FP8 conversion and that offer options for fine-tuning the conversion process.

However, there are potential problems you might encounter with direct FP8 conversion:

Information Loss: The most significant concern is the potential loss of information. FP8 has a limited dynamic range and precision compared to BF16 or FP32. This can lead to a degradation in model performance if not handled carefully. Preserving model accuracy is the key challenge here.
Stability Issues: Lower precision formats can sometimes lead to instability during training or inference. Numerical underflow and overflow become more prevalent, potentially causing your model to produce incorrect results or even crash. Ensuring numerical stability is critical.
Hardware Compatibility: While FP8 is gaining traction, not all hardware fully supports it yet. You need to ensure that your hardware (GPUs, TPUs, etc.) can efficiently handle FP8 computations. Hardware acceleration for FP8 is continuously improving, but it's still a consideration.

Converting from BF16 to FP8: A Safer Route?

Given the potential pitfalls of direct FP8 conversion, many practitioners opt for a two-step approach: converting from higher precision (like FP32) to BF16 first, and then from BF16 to FP8. This approach can offer a more controlled transition, minimizing information loss. The advantages of converting from BF16 to FP8 include:

Reduced Information Loss: BF16 retains a good balance between range and precision, making it a suitable intermediate format. Converting to BF16 first allows the model to adapt to a lower precision format before the final step down to FP8. This gradual reduction helps in preserving critical model information.
Stability: BF16 is generally more stable than FP8 due to its wider dynamic range. Converting to BF16 first can help identify and address any stability issues before moving to FP8. Enhanced stability means fewer surprises during deployment.
Wider Tool Support: BF16 is a well-established format with broad support across various deep learning frameworks and hardware platforms. This means you have a wider range of tools and libraries available for BF16 conversion, making the process smoother. Leveraging established tools simplifies the workflow.

However, this two-step process isn't without its drawbacks:

Increased Complexity: It adds an extra step to the conversion process, making it slightly more complex and time-consuming. Managing the conversion pipeline requires careful planning.
Potential for Cumulative Loss: While BF16 helps reduce information loss, each conversion step introduces some loss. The cumulative effect of converting from FP32 to BF16 and then to FP8 needs to be carefully managed. Monitoring cumulative loss is crucial.

Using `convert.py` for FP8 Conversion

The convert.py script, often used with libraries like ExllamaV3, is a powerful tool for quantizing models. But how does it fare with FP8 conversion? If you've successfully used convert.py for BF16 quantization, you might wonder if it can be directly applied to FP8 models.

In many cases, convert.py can handle FP8 conversion, but it's essential to understand the underlying mechanisms and potential limitations. Here’s what you should consider:

Tool Compatibility: Ensure that the version of convert.py you are using supports FP8. Some older versions might not have full FP8 support, potentially leading to errors or suboptimal results. Staying updated with the latest tools is vital.
Configuration Options: Explore the configuration options available in convert.py. There might be specific settings or flags that you need to adjust for FP8 conversion. For example, you might need to specify the FP8 format (e.g., FP8_E4M3, FP8_E5M2) and other quantization parameters. Understanding configuration parameters is key to successful conversion.
Calibration and Fine-tuning: FP8 conversion often requires calibration and fine-tuning to mitigate the loss of precision. Calibration involves running a small dataset through the model to determine the optimal quantization parameters. Fine-tuning involves training the quantized model for a few epochs to recover any lost accuracy. Effective calibration and fine-tuning can significantly improve results.

If you encounter issues when using convert.py for FP8, don't hesitate to dive into the documentation or seek community support. Forums and discussion boards related to your specific tools (like ExllamaV3) can be invaluable resources.

Best Practices for FP8 Conversion

To wrap things up, let’s look at some best practices for FP8 conversion to ensure you get the best possible results:

Start with a Well-Trained Model: A model that performs well in its original precision (e.g., FP32 or BF16) is more likely to convert successfully to FP8. Model quality matters.
Consider Quantization-Aware Training: If possible, train your model with quantization-aware training. This technique makes the model more resilient to the effects of quantization. Training for quantization is a proactive approach.
Use Appropriate Conversion Tools: Select tools that are specifically designed for FP8 conversion and offer the flexibility to adjust quantization parameters. Choosing the right tools is half the battle.
Calibrate and Fine-Tune: Always calibrate your model after conversion and fine-tune it if necessary. This helps recover any lost accuracy. Calibration and fine-tuning are essential steps.
Monitor Performance: After conversion, thoroughly evaluate your model's performance. Check metrics like accuracy, latency, and memory usage to ensure that the conversion meets your requirements. Performance monitoring is crucial.
Experiment and Iterate: FP8 conversion can be tricky, and the optimal approach can vary depending on the model and task. Don't be afraid to experiment with different settings and techniques to find what works best. Iteration is key to optimization.

Conclusion

Converting to FP8 can be a powerful way to optimize your models for efficiency, but it's not a one-size-fits-all solution. Whether you can convert directly to FP8 or need to go through BF16 depends on various factors, including your model architecture, the tools you're using, and your performance requirements. By understanding the trade-offs and following best practices, you can make informed decisions and achieve the best possible results.

In summary, while direct conversion to FP8 is possible, converting via BF16 often provides a safer and more controlled transition, reducing the risk of information loss and stability issues. Tools like convert.py can be invaluable for this process, provided they are used with careful consideration of the specific requirements of FP8 quantization.

Happy converting, and may your models run efficiently ever after!

For further reading on quantization and model optimization, check out resources like TensorFlow Model Optimization. 📝