The 1 Bit LLM is a new innovative way of training and performing inference on an LLM Model through the process of quantization proposed by Microsoft. In this blog article, we will be breaking down how and why exactly this usage of very small bits for training and inference of LLM Models is a huge boon moving forward for the LLM eco-system as a whole.

Why do we need this?

First off, let's understand the purpose and the why behind this new innovative way of building and training LLMs.

In the case of a 1-bit LLM, we mainly focus on reducing the weights required to train an LLM model from a huge floating point number to bits in general. This can help provide extremely fast inference aka model outputs along with very low amounts of storage costs.

Traditionally, LLM Models are extremely bloated and take a while depending on their context length of course to perform inference and provide an output to the end user. And, the main problem over here is the huge amounts of floating point values used for the weights of the LLM Model which can prove to be taxing both to perform inference on and to store as well and this is where this new innovative strategy of using 1 Bit LLMs tends to shine.

Now, The 1 Bit LLM models in general have ternary weights {-1,0,1} as opposed to the traditional LLM Models that have weights in the floating point range which are in general much more computationally taxing to both store and perform inference on. Now, why is this model magic? Due to the fact that we drop the weights, we are essentially gaining a speed boost and memory boost of over 1.5-2x in general at least as per the benchmarks released by Microsoft without any drop in the quality of the output results received from the LLM Model after inference. We will also be exploring how exactly it manages to reduce the amount of performance needs it has while maintaining the quality of the results as compared to a traditional LLM in this blog.

Now, Why is this magical? This model when compared to the traditional LLM Models which require a lot of both GPUs and electricity does not need as much. Due to its inherent nature of being able to work with low quantity weights in contrast to traditional LLMs, a 1 Bit-LLM can provide wings to both individual developers or resource-constrained companies due to the fact that it can be trained and can also perform inference even using a limited set of compute / GPU resources.

This would also be very beneficial to society due to how much energy ChatGPT and other LLM Models alike, in general, tend to consume and would also solve the problems of modern LLMs becoming thirsty in nature thus preserving energy on a global scale as well and not causing harm to the natural resources.

Introduction

Now, we will be discussing the origins of this model. This model was released by Microsoft and the company's 1 Bit LLM Model variant is called the BitNet b1.58 model which is currently in its research stages with it not being released yet however stay tuned as it can show up any time soon.
This model has been mainly created to optimize the amount of memory resources and the time taken to both train and get the output of an LLM Model and has been created as a solution to the large amounts of computing traditional LLM Models tend to require in general through the use of very small value of the weights used in general for LLMs.

This model only uses the ternary weights/parameters with the values {-1,0,1} as compared to traditional LLM Models that use 8 or 16-bit values which are in the floating point range and are generally computationally expensive to compute and get the inference.

This model is mainly based on the BitNet Architecture and The core of this advancement is the 1-bit quantization technique, where each parameter in the model, known as weights, is encoded using only 1.58 bits. Unlike conventional LLMs that typically use 16-bit floating-point values (FP16) for weights, the 1-bit LLM confines each weight to one of three values: -1, 0, or 1.
This reduction in bit utilization is a fundamental aspect of the model.

This technology marks a departure from traditional 8-bit storage, offering a single parameter storage of only 1.58 bits. Such improvements are anticipated to enhance training compute performance and speed, with potential applications in bolstering edge AI. The introduction of 1-Bit LLM technology is seen as a technological breakthrough that could significantly impact the future of AI systems and their deployment across various industries.

The BitNet b1.58 model introduces a compelling alternative to traditional LLM architectures, providing a blend of high efficiency, reduced computational cost, and maintained performance. It pushes the boundaries of creating more energy-efficient models, addressing a critical concern in deploying sizable LLMs. The diminished resource requirements potentially lower the barrier to deploying advanced NLP capabilities on edge and mobile devices, broadening the application horizon of LLMs. Furthermore, it opens avenues for designing specialized hardware optimized for 1.58-bit or ternary architectures, hinting at more cost-efficient AI accelerators in the pipeline.

Working

Traditional transformer models and the 1-bit LLM Model also perform inference mainly through the process of matrix multiplication and in traditional cases, every single value of the matrix would be in the 8-bit or the 16-bit ranges aka float and sometimes even the double-datatype ranges making the already complicated and slow process of matrix multiplication much more larger. However, the beauty of the 1 Bit LLM Model is its ternary weight.

Due to the usage of these ternary weights, this model will perform matrix multiplication on only values in the range between -1 <= 1 where any value related to zero is dropped as well and all of these are integer values as compared to the previously used floating point values making this model much faster in nature in the process of performing inference and storing these 3 ternary numbers is a very easy task as compared to storing 8-bit or 16-bit numbers in general.

The model's design also incorporates key elements like RMSNorm and SwiGLU, akin to LLaMA, with a specific emphasis on system-level enhancement through a quantization function. This amalgamation and optimization result in accelerated processing speeds and decreased GPU memory usage as compared to the traditional/conventional large language models.

Throughput is another area where BitNet b1.58 excels, offering much higher throughput at a lower maximum batch size compared to traditional models. This improvement in throughput is crucial for handling large datasets and performing multiple operations simultaneously 1.

In summary, the computation in 1-bit LLMs like BitNet b1.58 is characterized by a shift towards integer-based operations, reduced energy consumption, and enhanced memory and latency performance. These advancements not only make the models more efficient but also open up new possibilities for deploying LLMs in resource-constrained environments like edge and mobile devices.

Finally, the reason why the inference process that happens through the matrix multiplication approach in this model is very fast as compared to the traditional LLM Models is because of the presence of "0" in this model. Due to the presence of 0, matrix multiplication which is a combination of first multiplication and addition, remains unaffected for the most part it as this value is ignored in general and the multiplication / addition of -1 or 1 is also not a very costly operation as compared to the multiplication / addition of floating point numbers and this is known as the Pareto Efficiency.

Quantization Process in the 1-Bit LLM Model

The quantization process in BitNet b1.58, which replaces floating-point numbers with ternary weights, is a groundbreaking approach that significantly reduces computational requirements while maintaining or improving performance. This process is based on the idea that, in the context of deep learning, we don't need precision at all; we only need three symbols to represent every value, namely {-1, 0, 1}.

The quantization technique involves replacing all parameter floating-point values representing real numbers with ternary values in the matrices used. This is a radical departure from traditional quantization methods, which often aim to reduce the precision of weights to lower bit widths (e.g., 8-bit integers) while still maintaining acceptable accuracy levels. The shift to ternary values is not just about reducing precision but about eliminating it altogether, focusing instead on the sign of the values.

In matrix multiplications, where the bulk of computation in LLMs occurs, the quantization process transforms elementwise products in each dot product (e.g., a₁b₁ + a₂b₂ ...) into elementwise additions (a₁+b₁ + a₂+b₂ ...), where the signs depend on each value. This change is possible because multiplication is not as crucial with ternary values as it is with floating-point numbers. The shift to ternary values allows for the execution of matrix multiplications via cheaper tritwise operations in hardware, which is a more natural fit given the limited range of ternary values.

Quantization Function used in the 1-Bit LLM Model

The quantization function in BitNet b1.58 is a critical component that enables the model to operate efficiently with significantly reduced computational resources.

This model employs a very special form of quantization function to achieve the required result from the ternary operators. The way this function works is very simple.

The model. BitNet b1.58 employs an absmean quantization function that scales and rounds the weight matrices. This function is designed to convert the conventional model weights, which are typically represented in 16-bit floating-point values, to ternary values {-1, 0, 1}. Simply put, we find the closest ternary value to the original 16-bit weights used for the traditional LLM Models.

This conversion effectively reduces the bits per parameter to 1.58, a key feature that distinguishes BitNet b1.58 from traditional LLMs.

The absmean function used in the 1-bit LLM model involves calculating the average absolute value of the weights in a weight matrix. Here is a detailed breakdown of how the function works in the context of this model:

1. Calculate Absolute Values: First, the absolute values of all weights in the weight matrix are computed.

2. Find Average: The average of these absolute values is then calculated to determine the mean absolute value.

3. Normalization: This mean absolute value is used to normalize the weights within a certain range suitable for ternary operators.

4. Quantization: After normalization, the weights are quantized to -1, 0, or +1 based on their relationship to this normalized range.

The main core steps of Scaling and Rounding will now be broken down -

• Scaling: It first scales the weights in the 16-bit format to the average absolute value possible thus making the weights to be in that range of the ternary operators.

In case that was too complicated, let's break it down.

We will first be considering a simple neural network layer with weights represented in a 16-bit format. The weights are scaled to the average absolute value possible, which means they are normalized within a certain range. In the 1-bit LLM model, these weights would then be quantized to either -1 or +1.

For instance, if we have a weight matrix like:

[[0.5, -0.3],
[-0.8, 0.7]]

After scaling aka the first step in the quantization process of the 1-bit LLM model, it might become:

Normalized Weight Matrix:

[[0.5/Max_abs_value, -0.3/Max_abs_value],
[-0.8/Max_abs_value, 0.7/Max_abs_value]]

where the Max_abs_value denotes the maximum absolute value among all the weights in the weight matrix

This binary representation simplifies the computations during inference while still allowing the neural network to make predictions effectively.

• Rounding : After scaling, every weight value on the normalized matrix is then rounded off to the nearest integer value among -1, 0, and +1. This translates the scaled weights into the discrete ternary system.

So, in the case of our example, the weights finally becomes,

Normalized Weight Matrix:

[[0.5/Max_abs_value, -0.3/Max_abs_value],
[-0.8/Max_abs_value, 0.7/Max_abs_value]]

After rounding off the normalized weights, 1-bit LLM model, it might become:

[[+1, -1],
[-1, +1]]

The implications of this quantization approach are profound. It not only reduces the computational resources required for training and inference but also opens up possibilities for specialized hardware optimized for ternary operations. This could lead to more energy-efficient AI accelerators and a new wave of hardware specifically designed for ternary computations. The shift to ternary weights also aligns with a broader trend towards more efficient and sustainable AI practices, where reducing the bit width of model parameters can lead to significant reductions in energy consumption and computational requirements.

This quantization function is particularly beneficial for BitNet b1.58 because it simplifies the implementation and system-level optimization while introducing negligible performance impacts. It allows for the execution of matrix multiplications via cheaper tritwise operations in hardware, which is a more natural fit given the limited range of ternary values. This approach not only reduces the computational resources required for training and inference but also maintains or even improves the model's performance.

To summarize, the quantization process takes place through the absmean quantization function which proves to be crucial for the conversion of the 16/8-bit floating point values to the ternary operator values that this model can perform matrix multiplication.

Why does it specifically use 1.58?

Now, you must be wondering why it's called the BitNet b1.58 model and we will now be exploring the significance of this specific number used.

The "1.58" in the name BitNet b1.58 refers to the average bits per parameter used in the model, which is calculated by considering the three possible values (-1, 0, +1) and the fact that one of these values (0) is a placeholder for zero causes the value to increase from 1 to 1.58 bits.

And, in case it wasn't clear, the 1.58 bits per parameter signifies the effective bit width used to represent the weights in the model, compared to the 16 bits used in traditional LLMs. This naming highlights the model's efficiency and the innovative approach to reducing the computational resources required for training and inference without compromising performance.

Key Elements of the 1-Bit LLM Model

Now, let's circle back and understand the key elements of this model other than the quantization function and more.

The BitNet b1.58 model, a pioneering 1-bit Large Language Model (LLM), integrates several key components and architectural adjustments to achieve significant reductions in computational requirements while maintaining or even improving performance. These elements/components, which are similar to those found in the LLaMA model architecture, include:

1. RMSNorm: This is a normalization technique used to stabilize the training process. It works by normalizing the activations of each layer to have zero mean and unit variance, which can help stabilize the learning process and prevent vanishing or exploding gradients. This normalization method is crucial for training deep neural networks effectively.

2. SwiGLU: SwiGLU is an activation function that offers efficiency advantages over traditional activation functions like ReLU or GELU. It's designed to be more computationally efficient, especially in the context of 1-bit LLMs where computational resources are limited. SwiGLU is particularly effective in reducing the number of multiplication operations required during the forward and backward passes of the model, contributing to the model's overall efficiency.

3. Rotary Embeddings: This method is used for representing words and positions within the model. Rotary embeddings introduce a circular, or rotary, pattern into the model, which can be particularly useful for tasks that require understanding the order or position of words, such as translation or summarization. By incorporating rotary embeddings, BitNet b1.58 can more effectively capture the sequential nature of language.

4. Removal of Biases: In traditional LLMs, bias terms are often used to adjust the output of neurons, but in BitNet b1.58, these bias terms are removed. This simplification of the model architecture can lead to further reductions in computational complexity and memory usage, contributing to the model's efficiency.

Capability to handle long sequences of text

The BitNet b1.58 model, in particular, matches the performance of full-precision (FP16 or BF16) Transformer LLMs in terms of perplexity and end-task performance, while being significantly more cost-effective in terms of latency, memory, throughput, and energy consumption. This model represents a new scaling law and recipe for training high-performance and cost-effective LLMs. The introduction of 1-bit LLMs opens the door for designing specific hardware optimized for these models, potentially enabling their deployment on edge devices with limited resources.

One of the key advantages of 1-bit LLMs is their ability to handle long sequences of text with the same memory budget as shorter sequences. This is achieved through BitNet's compressed activations, which allow for the handling of double the sequence length for the same memory budget, bringing native support for long text comprehension within reach. This feature is particularly beneficial for applications that require processing large volumes of text, such as document summarization, long-form conversational agents, and comprehensive question-answering systems.

Moreover, the 1-bit LLMs' reduced computational demands and memory requirements open up new possibilities for deploying AI models on consumer devices, potentially making sophisticated AI assistants more accessible and affordable. This could lead to a democratization of AI technology, where powerful dialog agents can operate on everyday devices, reducing reliance on cloud services and offering privacy benefits by processing conversational data locally.

Benefits of using the 1-Bit LLM Model

The BitNet b1.58 model offers several compelling benefits that make it an attractive choice for various applications, from research to deployment in real-world scenarios. Here's a detailed look at the key benefits and why you might consider using this model:

1. Enhanced Efficiency and Reduced Resource Requirements: BitNet b1.58 operates with significantly reduced computational resources compared to traditional 16-bit Large Language Models (LLMs). It achieves this by using ternary parameters (-1, 0, 1) instead of 16-bit floating-point values, leading to up to 2.71 times faster processing and 3.55 times less memory consumption.

2. Lower Latency and Improved Memory Efficiency: The model's efficiency translates into reduced latency, making it suitable for applications that require swift responses. Additionally, the memory efficiency is improved, which is crucial for edge devices with limited computational resources and mobile applications.

3. Environmental and Economic Benefits: By significantly reducing energy consumption and computational demands, BitNet b1.58 holds promise for mitigating the environmental and economic concerns associated with large-scale language models like them consuming very high amounts of water resources for example. This could lead to a more sustainable approach to AI applications.

4. Potential for Specialized Hardware: The research suggests the development of dedicated hardware optimized for 1-bit LLMs, which could revolutionize the hardware landscape. Customized hardware could further amplify the efficiency gains achieved by the model, potentially opening new avenues for innovation in AI computing.

5. Comparable Performance to Traditional Models: Despite the reduction in precision, BitNet b1.58 achieves performance levels comparable to traditional full-precision LLMs. This makes it an attractive option for developers and researchers looking for high-performance models without the associated high costs and resource demands.

6. Future Directions and Innovation: BitNet b1.58 represents a significant leap forward in the field of AI, paving the way for a harmonious coexistence of high-performance models and resource-efficient computational frameworks. As researchers continue to explore the potential of 1.58-bit LLMs, the future promises further advancements in efficiency, sustainability, and the capabilities of AI systems.

7. Scalability and Versatility: The model introduces a novel scaling law and training recipe for LLMs, suggesting that high-performance, cost-effective models can be achieved at larger scales. This is a departure from traditional scaling constraints, making BitNet b1.58 a versatile tool for a wide range of AI applications.

8. Smaller Scale Training: This model mainly proves to be a very big win for smaller communities / individual developers or indiehackers who oftentimes do not have the compute and the resources as compared to bigger companies to train their own LLM Models often stifling new innovation and this model could also potentially allow users to train LLM Models on smaller form factor machines like mobile phones in the future.

In summary, the BitNet b1.58 model offers a combination of efficiency, performance, and sustainability that makes it a compelling choice for both research and practical applications. Its potential to reduce computational and environmental costs, coupled with the promise of future innovations and scalability, makes it a model worth considering for anyone looking to leverage the power of AI in a cost-effective and environmentally friendly manner.

Benchmarks

The BitNet b1.58 model showcases impressive benchmarks that highlight its efficiency and performance compared to traditional Large Language Models (LLMs). Here's a detailed overview of the benchmarks:

1. Performance Matching : BitNet b1.58 matches the full-precision (FP16 or BF16) Transformer LLM in terms of both perplexity and end-task performance. This indicates that despite the significant reduction in bit precision, the model maintains competitive performance levels with traditional models.

2. Cost-Effectiveness : The model is significantly more cost-effective in terms of latency, memory, throughput, and energy consumption. This is a crucial aspect, especially for applications where computational resources are limited or where efficiency is a priority.

3. Memory and Throughput : At 3.9B parameters, BitNet b1.58 uses 3.3x less memory and is 2.4x faster than the 3B LLaMA LM. This efficiency is further highlighted by its ability to handle up to 11x higher batch size and 8.9x higher throughput compared to baselines. These metrics underscore the model's ability to process large volumes of data more efficiently.

4. Energy Efficiency : The model demonstrates up to 41x lower energy consumption than full-precision models. This is a significant achievement, particularly in the context of deploying AI models in data centers or cloud environments where energy efficiency is a critical consideration.

5. Scalability and Future Hardware : BitNet b1.58 introduces a new scaling law and recipe for training new generations of LLMs that are both high-performance and cost-effective. It also opens the door for designing specific hardware optimized for 1-bit LLMs, which could revolutionize the hardware landscape for AI applications.

6. Practical Impact : Beyond energy savings, BitNet b1.58 has implications that extend to deploying advanced LLMs in resource-constrained environments, including mobile devices and edge computing platforms. This highlights the model's practical applications and its potential to revolutionize content creation and enable real-time AI interactions on consumer electronics.

7. Community and Future Directions : The model has garnered positive feedback from the AI community, with discussions around its compatibility with mixed setups and potential for further optimizations. There's also interest in exploring the training of larger models and the implications of the model's ternary representation on performance and efficiency.

These benchmarks and discussions around BitNet b1.58 underscore its potential to significantly impact the field of AI, offering a pathway towards more efficient, sustainable, and cost-effective LLMs. The model's performance and efficiency metrics, combined with its scalability and potential for future hardware optimizations, make it a compelling choice for both research and practical applications in AI.

Computation with Groq

The paper also discusses about Groq as a significant development in the field of hardware for 1-bit Large Language Models (LLMs). Groq represents a promising step towards the design of specific hardware, such as LPUs (Large Parameter Units), optimized for LLMs. This development is particularly relevant in the context of BitNet, which introduces a new computation paradigm enabled by 1-bit LLMs. The paper highlights the potential of Groq in building hardware that is specifically tailored for the computational requirements and efficiency gains of 1-bit LLMs, such as BitNet b1.58 1.

The integration of Groq into the ecosystem of LLMs is envisioned as a way to further optimize the performance and efficiency of these models. By designing hardware that is specifically optimized for the 1-bit computation paradigm, it is expected that Groq can significantly enhance the speed, memory usage, and overall performance of 1-bit LLMs. This could include improvements in latency, throughput, and energy consumption, making these models more accessible and cost-effective for a wide range of applications 1.

The mention of Groq in the paper underscores the importance of hardware optimization in the development of efficient LLMs. As computational demands continue to grow, the need for specialized hardware that can support the unique requirements of 1-bit LLMs becomes increasingly relevant. The potential of Groq to revolutionize the hardware landscape for AI applications, making them more sustainable and accessible, is a testament to the ongoing advancements in this field.

Results of the Model

The paper's detailed comparison of BitNet b1.58 to a reproduced FP16 LLaMA Large Language Model (LLM) in various sizes provides a comprehensive view of the model's efficiency and performance across different scales. This comparison was conducted on the RedPajama dataset, which was used for pre-training both models on 100 billion tokens. The evaluation focused on zero-shot performance on a range of language tasks, including ARC-Easy, ARC-Challenge, Hellaswag, Winogrande, PIQA, OpenbookQA, and BoolQ. Additionally, the validation perplexity on the WikiText2 and C4 datasets was reported to provide a broader understanding of the models' performance.

Key Findings:

1.Performance Matching: BitNet b1.58 matches the full-precision LLaMA LLM in terms of perplexity, starting from a 3B model size. This indicates that despite the significant reduction in bit precision, the model maintains competitive performance levels with traditional models.

2.Cost Efficiency: The model is significantly more cost-effective in terms of latency, memory, throughput, and energy consumption. For instance, at 3.9B parameters, BitNet b1.58 uses 3.3x less memory and is 2.4x faster than the 3B LLaMA LM. This efficiency is further highlighted by its ability to handle up to 11x higher batch size and 8.9x higher throughput compared to baselines.

3.Efficiency at Different Sizes: As the model size increases, the performance gap between BitNet b1.58 and LLaMA LLM narrows. More importantly, BitNet b1.58 can match the performance of the full precision baseline starting from a 3B size. For instance, the 3.9B model size of BitNet b1.58 outperforms the 3B LLaMA LM with lower memory and latency cost, demonstrating that BitNet b1.58 is a Pareto improvement over the state-of-the-art LLM models.

4.Throughput: The comparison of throughput between BitNet b1.58 and LLaMA LLM with 70B parameters shows that BitNet b1.58 can support up to 11 times the batch size of LLaMA LLM, resulting in an 8.9 times higher throughput. This indicates that BitNet b1.58 is enabling a new scaling law with respect to model performance and inference cost.

5.Training with 2T Tokens: The paper also tested the scalability of BitNet b1.58 in terms of tokens by training a model with 2T tokens, following the data recipe of StableLM-3B. The results show that BitNet b1.58 achieves superior performance on all end tasks, indicating that 1.58-bit LLMs also have strong generalization capabilities 1.

These findings underscore the potential of BitNet b1.58 as a highly efficient and cost-effective alternative to traditional LLMs, offering significant benefits in terms of performance, efficiency, and scalability. The model's ability to match or even surpass the performance of full-precision models while using significantly fewer resources makes it a compelling choice for both research and practical applications in AI.

Conclusion

To conclude, The emergence of 1-bit Large Language Models (LLMs), exemplified by BitNet b1.58, represents a paradigm shift in digital modeling, offering a new approach to AI development. This innovation significantly reduces computational resources, making LLMs more accessible and affordable, while also being inherently more energy-efficient. The BitNet b1.58 model, with its unique ternary parameter representation (-1, 0, +1), showcases impressive performance metrics, including matching or even surpassing full-precision baselines in terms of both perplexity and end-task performance. This advancement not only democratizes access to advanced AI technology but also opens up new possibilities for running these models on a variety of platforms, including mobile and edge devices.

The 1-bit LLM concept, with its potential for further development and optimization, marks a pivotal moment in the evolution of AI technology. By redefining the scaling laws for LLMs, it enables models to be effectively run with significantly reduced hardware requirements, offering an approximate 8–15x improvement in efficiency. This transition not only simplifies the architecture, potentially reducing the need for sophisticated hardware like GPUs, but also encourages the development of new optimization techniques.

The future of 1-bit LLMs looks promising, with advancements in hardware and software, as well as algorithmic innovations, paving the way for more accessible, efficient, and sustainable AI. The potential for further development in this space is immense, and the advancements that build upon this groundbreaking work are eagerly anticipated. The 1-bit LLMs, with their impressive performance metrics, reduced hardware requirements, and energy efficiency, stand to revolutionize how LLMs are developed and deployed, opening up new avenues for application and research.