Making AI More Accurate: Microscaling on NVIDIA Blackwell

3 Apr 202408:00

TLDRThe script discusses advancements in machine learning quantization, highlighting the shift towards lower precision formats like FP6 and FP4 to increase computational efficiency. It emphasizes the importance of microscaling to enhance these formats' accuracy and range. The challenge of standardizing these new formats is also mentioned, along with the need for clear guidelines for programmers to effectively utilize the benefits of reduced precision formats for significant performance gains.


  • πŸ“ˆ Machine learning is increasingly using quantization to improve computational efficiency by employing smaller bit representations for numbers.
  • πŸ”’ The industry standard formats like FP16 and INT8 have been popular for offering significant speedups without compromising accuracy.
  • 🌟 Nvidia's Blackwell announcement introduced new FP6 and FP4 formats, pushing the boundaries of computational efficiency further.
  • πŸ”„ Despite the limited range of operations with FP4 (about 6 operations), it's considered sufficient for certain machine learning tasks, especially inference on low-power devices.
  • πŸ”§ Ongoing research is needed to confirm the accuracy and reliability of these low-precision formats for everyday use.
  • πŸ“Š Microscaling, a technique introduced by Microsoft, is a key feature in the new Nvidia chip, allowing for dynamic adjustments in the range of numbers being processed.
  • 🀝 The industry is moving towards standardization with organizations like IEEE working on standards for reduced precision formats, though the pace is slow compared to the rapid advancements in machine learning.
  • πŸ”„ The diversity of reduced precision formats can lead to inconsistencies in mathematical operations across different architectures, highlighting the need for unified standards.
  • πŸ› οΈ While frameworks like TensorFlow and PyTorch abstract much of the complexity, extracting the maximum performance from these reduced precision formats may require more specialized knowledge and skill.
  • πŸ’‘ Clear guidelines and industry consensus on the implementation and usage of these formats are crucial for their successful adoption and integration into programming practices.

Q & A

  • What is the purpose of quantization in machine learning?

    -Quantization in machine learning is the process of using smaller numbers, specifically smaller bits, to increase computational efficiency. This allows for a higher number of operations, such as Giga flops and GigaOps, to be performed within a given time frame, which is crucial for handling large-scale machine learning tasks.

  • Why have formats like FP16 and BFloat16 become popular recently?

    -Formats like FP16 and BFloat16 have gained popularity because they offer substantial speedups without significantly compromising accuracy. These reduced precision formats enable faster computations while still maintaining the performance needed for machine learning tasks.

  • What new formats did Nvidia announce in their latest chip that could help accelerate machine learning workloads?

    -Nvidia announced support for FP6 and FP4 formats, which are floating-point precision in six bits and four bits, respectively. The goal is to perform more operations by using fewer bits, thus increasing computational efficiency.

  • What are the challenges associated with using FP4 format for floating-point numbers?

    -The challenge with FP4 format is that it only provides four bits to represent a number, one of which is for the sign, another for indicating infinity or not, and leaves only two bits to cover the entire range of numbers. This limitation restricts the format to a very limited set of operations, making it difficult to represent a wide range of decimal values accurately.

  • How does microscaling help in addressing the limitations of reduced precision formats like FP4?

    -Microscaling addresses the limitations of reduced precision formats by using additional bits, typically eight, as a scaling factor. This allows for a more flexible representation of numbers by shifting the range of interest on the number line, effectively increasing the accuracy and range for the specific mathematical operations within that region of interest.

  • What is the significance of the Microsoft research in the context of microscaling?

    -Microsoft research introduced the concept of microscaling with their MSFP12 format, which combines an FP4 format with an 8-bit scaling factor. This innovation allows the scaling factor to be applied to 12 different FP4 values, reducing the overhead and making the reduced precision formats more practical for use in machine learning and other computational tasks.

  • How does Nvidia's approach to microscaling differ from Microsoft's MSFP12?

    -Nvidia's approach allows for the support of 32, 64, or even 10,000 FP4 values with a single 8-bit scaling factor. This means that the scaling penalty is paid only once for a large number of operations within the same region of interest, making the reduced precision formats even more scalable and efficient.

  • What are the implications of the lack of consistency in reduced precision formats across different architectures?

    -The lack of consistency in reduced precision formats can lead to difficulties in managing mathematics between different architectures. It becomes challenging to ensure that operations like division by zero or handling infinities are managed correctly across various platforms, which can impact the reliability and portability of machine learning models.

  • What role does the IEEE standards body play in the development of precision formats?

    -The IEEE standards body is responsible for establishing and maintaining standards for various data formats, including floating-point precision formats. They have established standards like IEEE 754 for FP64 and FP32, and are working on standards for 16-bit and 8-bit formats. This helps ensure consistency and interoperability across different hardware and software platforms.

  • Why is it important for the industry to come together and define clear guidelines for reduced precision formats?

    -Clear guidelines are essential for the industry to ensure that programmers and developers understand how these reduced precision formats are implemented and what they mean. This understanding is crucial for maximizing the benefits of these formats and for the development of accurate, efficient, and portable machine learning models.

  • How might the complexity of reduced precision formats affect programmers dealing with fundamental mathematical operations?

    -The complexity of reduced precision formats can pose a significant challenge for programmers who are working with the fundamentals of mathematics. While frameworks like TensorFlow and PyTorch abstract away some of these complexities, extracting the maximum performance from these formats may require more specialized knowledge and skills, potentially creating a barrier to entry for some developers.



πŸ“ˆ Introduction to Quantization and its Impact on Machine Learning

This paragraph introduces the concept of quantization in machine learning, highlighting its role in using smaller numbers and bits to increase computational efficiency. It discusses the shift towards reduced precision formats like FP16 and BFloat16, which have gained popularity due to their ability to offer substantial speedups without compromising accuracy. The paragraph also mentions Nvidia's announcement of even lower precision formats, FP6 and FP4, and their potential to further accelerate computational workloads. However, it points out the inherent challenge in representing floating-point numbers with limited bits, emphasizing the need for research to ensure these formats meet accuracy requirements for everyday use. The introduction of microscaling as a solution to enhance the utility of these low-precision formats is also discussed, explaining how it allows for better handling of mathematical operations within a specific range of interest.


πŸ“š Standardization Challenges in Reduced Precision Formats

This paragraph delves into the challenges of standardization in the context of reduced precision formats in the industry. It notes the existence of various versions of FP8 and the complexities that arise when dealing with standard numerical formats, such as handling infinities, NaNs (Not a Numbers), and zero values. The paragraph emphasizes the slow pace of the standards body, IEEE, in catching up with the fast-moving machine learning industry, particularly in developing standards for 16-bit and 8-bit formats. It also mentions the importance of clear guidelines for implementing reduced precision formats and the need for the industry to collaborate on effectively communicating how these standards work. The paragraph concludes by acknowledging the potential barrier to entry for programmers dealing with fundamental mathematical operations at this level, and the significant financial benefits that can be gained from mastering these reduced precision formats.




Quantization in the context of machine learning refers to the process of reducing the number of bits used to represent a number, which in turn allows for increased computational efficiency. It is a technique used to speed up computations by using smaller numerical values, such as FP16 or INT8, without significantly compromising accuracy. In the video, quantization is central to the discussion of how lower precision formats like FP6 and FP4 can enable faster computation and inference on devices with limited computational power.


Precision in computing, particularly in the realm of machine learning, refers to the accuracy of numerical values used in calculations. It is often associated with the number of bits allocated to represent a floating-point number. Higher precision, such as double precision (FP64), offers more accurate representations but requires more computational resources. The video discusses the trade-off between precision and computational efficiency, with a focus on lower precision formats like FP6 and FP4.

πŸ’‘Giga Flops and GigaOps

Giga Flops (GFLOPS) and GigaOps (GOPS) are units of measurement for computational speed, indicating the number of floating-point operations and operations per second a computer can perform, respectively. In the context of the video, these terms are used to discuss the computational capacity and the potential increase in performance achieved through the use of lower precision numerical formats.

πŸ’‘NVIDIA Blackwell

NVIDIA Blackwell is a reference to an announcement made by NVIDIA, a leading company in the field of GPUs and AI technology. In the video, it is mentioned in the context of introducing new lower precision formats like FP6 and FP4, which aim to further accelerate machine learning workloads. The announcement signifies NVIDIA's continued innovation in hardware to support the growing demands of machine learning and AI.


Microscaling is a technique that enhances the effectiveness of reduced precision formats by using additional bits to scale the range of numbers that can be represented. This allows for a more flexible and accurate representation of values within a specific range, improving the performance and accuracy of calculations. Microscaling is particularly important in machine learning because it enables the scaling of a range of interest on the number line, which can adapt to the specific needs of a computation. In the video, microscaling is presented as a solution to the limitations of reduced precision formats, allowing for a broader application while maintaining accuracy.


In machine learning, inference refers to the process of using a trained model to make predictions or decisions based on new input data. It is the practical application of a model in real-world scenarios. In the context of the video, the discussion around lower precision formats like FP6 and FP4 focuses on their potential to enable efficient inference on devices with limited power, making it possible to deploy complex models without the need for high-performance computing resources.

πŸ’‘Reduced Precision Formats

Reduced precision formats are numerical representations that use fewer bits than their full-precision counterparts. These formats are designed to optimize the trade-off between computational efficiency and accuracy. By using fewer bits, reduced precision formats can increase the speed of calculations and reduce the memory requirements, which is particularly beneficial for machine learning applications that need to run on resource-constrained devices.

πŸ’‘Floating Point Number Format

A floating point number format is a way of representing real numbers that can express a wide range of values and approximate decimal fractions. In computing, the most common standard is the IEEE 754 format, which defines how to represent numbers in binary form. The format includes a sign bit, an exponent, and a mantissa (or fraction). In the video, the floating point number format is discussed in the context of reduced precision, where the challenge is to represent a vast range of numbers using a limited number of bits.

πŸ’‘Microsoft Research

Microsoft Research is the research division of Microsoft, dedicated to advancing the state of the art in computing and promoting its practical applications. In the context of the video, Microsoft Research is credited with the development of microscaling, a technique that enhances reduced precision formats by allowing for more effective scaling of numerical values. This innovation demonstrates Microsoft's contribution to the field of machine learning and hardware optimization.


In the context of computing and machine learning, standards are established guidelines or specifications that ensure consistency and compatibility across different systems and technologies. They are crucial for interoperability and the development of an ecosystem where various components can work together seamlessly. The video discusses the need for standards in the rapidly evolving field of machine learning, particularly as it relates to the implementation of reduced precision formats.


Machine learning frameworks are software libraries that provide an environment for the development and deployment of machine learning models. They often include high-level APIs for tasks such as data manipulation, model construction, and training. In the video, frameworks like TensorFlow and PyTorch are mentioned as tools that abstract away some of the complexity associated with low-level numerical precision and computation, making it easier for developers to work with machine learning models without needing to understand all the underlying mathematical details.


The introduction of quantization in machine learning allows for the use of smaller numbers and bits to increase computational speed.

Quantization can lead to substantial speedups in machine learning tasks while maintaining accuracy, as seen with formats like FP16 and BFloat16.

Nvidia's Blackwell announcement showcased new formats FP6 and FP4 for further accelerating math workloads.

FP6 and FP4 formats aim to perform more operations by using fewer bits, which is crucial for low-power devices.

A floating-point number format with only four bits presents challenges, such as representing the sign and handling infinities.

With only two bits for the magnitude of a number in FP4 format, the range and accuracy of calculations are significantly limited.

Despite limitations, the goal is to use these reduced precision formats for machine learning inference on devices with limited computational power.

Research is ongoing to ensure the accuracy of low precision formats like FP6 and FP4 for everyday use.

Nvidia's announcement also introduced microscaling, a technique to enhance the utility of reduced precision formats.

Microscaling involves using an additional eight bits as a scaling factor to adjust the range of numbers effectively.

Microsoft Research previously introduced a similar concept with their MSFP12 format, which scales 12 FP4 values with one eight-bit scaling factor.

Nvidia's approach can support 32, 64, or even 10,000 FP4 values with a single eight-bit scaling factor, improving efficiency.

Scaled regions of interest can be adjusted along the number line to focus computational accuracy where it's needed most.

Tesla Dojo and Microsoft's Maya AI 100 chip also support scaling factors in their processors, setting a new industry standard.

The industry faces challenges in maintaining consistency in mathematics across different architectures due to the variety of reduced precision formats.

Standards bodies like IEEE work on establishing norms for different number formats, but the pace is slow compared to the rapid advancements in machine learning.

The need for clear guidelines and understanding of reduced precision formats is crucial for programmers and the industry as a whole.

Frameworks like TensorFlow and PyTorch abstract some complexity, but extracting maximum performance may require more nuanced approaches.

The industry must come together to effectively communicate and standardize the use of reduced precision formats for the benefit of all.