Crypto Market Makers: Liquidity Saviors or Sinners?

Posted on by Werner

In cryptocurrency trading few things matter more than speed and information. Maybe one thing does: market liquidity. Ever traded a shitcoin with a small market cap, and seen its price crater (or moon) in a matter of minutes? In most cases, that is the result of some whale executing a big order, driving the price up or down by supply or demand. 

This is where so-called market makers come in. They play a vital role in ensuring exchanges run smoothly by lubricating them with liquidity. But what exactly does liquidity mean, and why are market makers so crucial to crypto markets? Let’s dive in and explore the essential role they play.

What is a Crypto Market Maker?

A crypto market maker can be defined this way: it’s a person or financial entity who stands by to buy and sell a particular cryptocurrency on a continuous basis at publicly quoted prices. These are the prices found on the order books of centralized and decentralized exchange. Market makers place orders on exchanges to buy and sell cryptocurrencies like Bitcoin, Ethereum, or smaller coins. When they do this, they ensure there is sufficient liquidity – meaning other traders can easily enter or exit positions without causing drastic price movements. 

Market makers profit by capitalizing (some say exploiting) the difference between the bidding price and the asking price. This difference is known as the bid-ask spread.

For instance, a market maker might quote a bid-ask spread of $99/$100 for Bitcoin. This means he is willing to buy Bitcoin at $99 and sell it at $100. If a buyer and seller transact with the market maker as a counterparty, the market maker earns $1, minus their transaction fee. Multiply such small profits by thousands or even millions of transactions, and it becomes a very lucrative business model.

To manage their risk, market makers run (and fine-tune) advanced algorithms and technology to continuously adjust prices based on factors like trading volume, price fluctuations, and market volatility. This high-frequency trading allows them to capitalize on fleeting price discrepancies across different exchanges and currency pairs.

Why are Crypto Market Makers Important?

In the crypto space, market makers serve several important functions. 
First and foremost, by providing continuous liquidity, they make it much easier for buyers and sellers to trade without causing sudden, drastic price movements. Imagine if you had to wait for another trader who wants to take the exact opposite position to yours every time you wanted to buy or sell a coin. The market would move extremely slowly.

Additionally, market makers help to stabilize prices and reduce volatility by stepping in to buy when prices fall, and to sell when prices rise. Without them, crypto price charts would resemble seismographs, with prices jumping all over. By tightening spreads and balancing order flow, market makers ‘smooth out’ price action.

Several types of entities engage in crypto market making. Some are individual traders looking for profits from bid-ask spreads. Others are professional market making firms hired by exchanges or projects themselves to ensure sufficient liquidity. 

Many exchanges also act as market makers, either openly or behind the scenes. High-frequency trading (HFT) firms and arbitrageurs also contribute to liquidity through their rapid, algorithm-driven strategies. Finally, some token issuers and crypto projects provide their own liquidity to facilitate trading of their coin or token.

In employing market-making strategies, firms typically rely on a combination of advanced statistical models and formulas, ultrafast computers, and automated trading systems. 

Popular strategies include:

How Market Makers Make Their Money

So how exactly do market makers profit? The most obvious way is through the bid-ask spread. Every time they complete a round-trip transaction (i.e. buying and selling an equal quantity), they earn the spread between their buy and sell prices. 

Many market makers also receive commissions or rebates from exchanges for the liquidity they provide. Finally, some market makers generate profits by opening their own directional trading positions based on anticipated market movements. 

Example: DWFLabs and $Floki

One of the most well-known crypto market makers are DWF Labs, who usually drive up the price of their client’s assets significantly. Follow their portfolio here on CoinMarketCap. Their most recent success is the $GUMMY token, which peaked at a $250 million market cap. 

By accumulating $FLOKI tokens quietly over several months, DWFLabs was able to establish a strong position. They then orchestrated a massive 772% price pump in just three weeks, showcasing their ability to generate substantial returns quickly.

Credit: Werner V. via CoinMarketCap

However, market makers like DWFLabs often employ controversial tactics to manipulate prices. By artificially creating new lows, they can starve out retail investors and accumulate tokens at discounted prices. This cycle of pumps and dumps creates a challenging environment for average token holders, who may struggle to navigate the volatility created by market maker activity.

Therefore, when analyzing a crypto asset, make sure to take a look at both the price and the market cap. The higher the market cap, the more liquid the coin will be, and the less volatile in price.

Credit: Tesfu Assefa

The Good and Bad of Market Makers

An important question arises – are market makers good or bad for the average crypto investor? As with most things, there are two sides to this debate.

On the positive side, the liquidity market makers provide is undeniably valuable. Without it, crypto markets would be highly illiquid, slow-moving, and volatile. Their activity makes it much easier for investors to enter and exit trades seamlessly. Furthermore, competition between market makers keeps spreads tight, allowing investors to trade at fair prices close to the market price. 

However, some argue that market makers – particularly large, sophisticated firms – can take advantage of their role for manipulative purposes. 

Market makers have a range of ways they can potentially manipulate asset prices. With their large capital reserves and advanced algorithms, they can engage in practices like front-running, where they exploit advance knowledge of pending orders to make trades ahead of other investors. They might also employ quote-stuffing, placing large numbers of buy or sell orders they don’t intend to execute in order to create a false impression of demand or supply.

Another tactic is wash-trading, simultaneously buying and selling the same asset to generate fake trading volume and momentum. Market makers’ ability to set bid-ask spreads also gives them some control over short-term price movements. 

The opacity of some market makers’ operations, and their access to information not available to regular traders raises concerns about asymmetric advantages. While not all market makers act maliciously, they all follow the profit motive. This comes with an inherent potential for price manipulation that necessitates robust oversight and regulation.

Conclusion

Crypto market makers are essential actors in the cryptocurrency ecosystem. While not without controversy, these entities – through their combination of advanced technology and trading expertise – help form the circulatory system of the crypto markets we know today.

While they can manipulate markets with their tactics (especially small illiquid markets), most would agree that the benefits of liquidity and more efficient markets outweigh the potential drawbacks. And in most jurisdictions, there are rules and regulations in place to prevent market abuse. While imperfect, market makers are arguably a necessary presence to ensure crypto markets run smoothly.

By providing continuous liquidity, tightening spreads, and balancing order flow, they serve the crucial role of facilitating efficient trading and more stable prices. For any investor to be profitable long-term, understanding how they operate will help them unlock access to the inner workings of crypto trading.

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The Era of 1.58-bit Large Language Models: A Breakthrough in Efficiency

Posted on by Kedist

As large language models (LLMs) continue to grow in capabilities, their increasing computational demands have raised concerns about efficiency, cost, and environmental impact. In a groundbreaking development, researchers at Microsoft Research have introduced BitNet b1.58, a novel 1.58-bit variant of LLMs that could usher in a new era of high-performance, cost-effective language models.

The Era of 1-bit LLMs

The field of AI has witnessed a rapid expansion in the size and power of LLMs, but this growth has come at a significant computational cost. Post-training quantization techniques have aimed to reduce the precision of weights and activations, but a more optimal solution was needed. Recent work on 1-bit model architectures, such as BitNet, has paved the way for a promising new direction in reducing the cost of LLMs while maintaining their performance.

BitNet b1.58: The 1.58-bit LLM Variant

BitNet b1.58 represents a significant advancement in this area, introducing a unique quantization approach that constrains every parameter (weight) of the LLM to ternary values of {-1, 0, 1}. This innovative technique, combined with efficient computation paradigms and LLaMA-alike components for better open-source integration, enables BitNet b1.58 to achieve remarkable results.

Results: Matching Performance, Reducing Cost

In a comprehensive evaluation, BitNet b1.58 demonstrated its ability to match the perplexity and end-task performance of full-precision (FP16) LLM baselines, starting from a model size of 3 billion parameters. As the model size scales up, the benefits of BitNet b1.58 become even more pronounced, with substantial reductions in memory usage, latency, throughput, and energy consumption compared to FP16 LLMs.

At the 70 billion parameter scale, BitNet b1.58 is up to 4.1 times faster, uses up to 7.2 times less memory, achieves up to 8.9 times higher throughput, and consumes up to 41 times less energy than its FP16 counterparts. These astounding results demonstrate the potential of 1.58-bit LLMs to provide a Pareto improvement over traditional models, delivering both high performance and cost-effectiveness.

Credit: Tesfu Assefa

Discussion and Future Work: Enabling New Possibilities

The development of 1.58-bit LLMs like BitNet b1.58 opens up a world of possibilities and exciting future research directions. One intriguing prospect is the potential for further cost reductions through the integration of efficient Mixture-of-Experts (MoE) architectures. Additionally, the reduced memory footprint of BitNet b1.58 could enable native support for longer sequence lengths, a critical demand in the era of LLMs.

Perhaps most significantly, the exceptional efficiency of 1.58-bit LLMs paves the way for deploying these models on edge and mobile devices, unlocking a wide range of applications in resource-constrained environments. Furthermore, the unique computation paradigm of BitNet b1.58 calls for the design of specialized hardware optimized for 1-bit operations, which could further enhance the performance and efficiency of these models.

Conclusion

In the rapidly evolving landscape of large language models, BitNet b1.58 represents a groundbreaking achievement, introducing a new era of 1.58-bit LLMs that combine state-of-the-art performance with unprecedented efficiency. By addressing the computational challenges associated with traditional LLMs, this research paves the way for more sustainable and cost-effective scaling, enabling the deployment of these powerful models in a wider range of applications and environments. As the field continues to advance, BitNet b1.58 stands as a testament to the innovative potential of quantized LLMs and the exciting possibilities that lie ahead.

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Inside the Mind of Ben Ditto | Mindplex Podcast S2EP17

Posted on by Mindplex

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Revolutionizing Language Models: The Emergence of BitNet b1.58

Posted on by Tensae

In recent years, the field of Artificial Intelligence has witnessed an unprecedented surge in the development of Large Language Models (LLMs), fueled by breakthroughs in deep learning architectures and the availability of vast amounts of text data. These models, equipped with powerful Transformer architectures, have demonstrated remarkable proficiency across a plethora of natural language processing tasks, from language translation to sentiment analysis. However, this rapid growth in the size and complexity of LLMs has brought about a host of challenges, chief among them being the staggering energy consumption and memory requirements during both training and inference phases.

To address these challenges, researchers have ventured into various techniques aimed at optimizing the efficiency of LLMs, with a particular focus on post-training quantization. This approach involves reducing the precision of model parameters, thereby curtailing memory and computational demands. While post-training quantization has proven effective to some extent, it remains suboptimal, especially for large-scale LLMs.

In response to this limitation, recent endeavors have explored the realm of 1-bit model architectures, epitomized by BitNet. These models leverage a novel computation paradigm that drastically reduces energy consumption by eschewing floating-point arithmetic in favor of integer operations, particularly beneficial for the matrix multiplication operations inherent in LLMs. BitNet, in its original form, has demonstrated promising results, offering a glimpse into a more energy-efficient future for LLMs.

Building upon the foundation laid by BitNet, researchers have introduced BitNet b1.58, a significant advancement in the realm of 1-bit LLMs. Unlike its predecessors, BitNet b1.58 adopts a ternary parameterization scheme, with model weights constrained to {-1, 0, 1}, thereby achieving a remarkable compression ratio of 1.58 bits per weight. This innovative approach retains all the advantages of the original BitNet while introducing enhanced modeling capabilities, particularly through explicit support for feature filtering.

Credit: Tesfu Assefa

BitNet b1.58 represents a paradigm shift in LLM architecture, offering a compelling alternative to traditional floating-point models. Notably, it matches the performance of full-precision baselines, even surpassing them in some cases, while simultaneously offering significant reductions in memory footprint and inference latency. Furthermore, its compatibility with popular open-source software ensures seamless integration into existing AI frameworks, facilitating widespread adoption and experimentation within the research community.

Beyond its immediate impact on model performance and efficiency, BitNet b1.58 holds immense promise for a wide range of applications, particularly in resource-constrained environments such as edge and mobile devices. The reduced memory and energy requirements of BitNet b1.58 pave the way for deploying sophisticated language models on devices with limited computational resources, unlocking new possibilities for on-device natural language understanding and generation.

Looking ahead, the development of dedicated hardware optimized for 1-bit LLMs could further accelerate the adoption and proliferation of BitNet b1.58, ushering in a new era of efficient and high-performance AI systems. As the field continues to evolve, BitNet b1.58 stands as a testament to the ingenuity and perseverance of researchers striving to push the boundaries of AI technology.

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Introduction to the Parameter Server Framework for Distributed Machine Learning

Posted on by Tibebe S.

The advancement of machine learning applications in various domains necessitates the development of robust frameworks that can handle large-scale data efficiently. To address this challenge, a paper titled “Implementing and Benchmarking a Fault-Tolerant Parameter Server for Distributed Machine Learning Applications” (which sounds like a mouthful but is a pretty simple concept once you break down the words) introduces a powerful Parameter Server Framework specifically designed for large-scale distributed machine learning. This framework not only enhances efficiency and scalability but also offers user-friendly features for seamless integration into existing workflows. Below, we detail the key aspects of the framework, including its design, efficiency, scalability, theoretical foundations, and real-world applications.

Key Features of the Parameter Server Framework

User-Friendly Interface

The framework allows easy access to globally shared parameters for local operations on client nodes, simplifying the complexities often encountered in distributed environments. A notable attribute of this framework is its focus on user accessibility, achieved through the streamlined implementation of asynchronous communication and the support for flexible consistency models. This design choice facilitates a balance between system responsiveness and rapid algorithm convergence, making it an attractive solution for practitioners and researchers alike.

Enhanced Efficiency

Efficiency is at the core of the framework’s design, leveraging asynchronous communication coupled with advanced consistency models like the “maximal delayed time” model and a “significantly-modified” filter. These features are crucial in enabling the system to converge to a stationary point under predetermined conditions. The framework’s asynchronous nature permits substantial improvements in processing speeds, effectively addressing the latency issues typically associated with large-scale data processing.

Scalability and Fault Tolerance

Designed to be elastically scalable, the framework supports dynamic additions and subtractions of nodes, thereby accommodating varying computational demands effortlessly. It also integrates fault tolerance mechanisms that ensure stable long-term deployment, even in the face of potential hardware failures or network issues. This level of reliability is essential for enterprises that depend on continual data processing and analysis.

Credit: Tesfu Assefa

Applications and Theoretical Foundation

The Parameter Server Framework is not only practical but also grounded in solid theoretical principles. It supports complex optimization problems, including nonconvex and nonsmooth challenges, using proximal gradient methods. This theoretical backing is crucial for tasks such as risk minimization, distributed Gibbs sampling, and deep learning. The structure of the framework is designed around server nodes that manage globally shared parameters and client nodes that perform computations asynchronously, thus optimizing the workload distribution.

Implementation Details

Server Nodes: These nodes are responsible for managing global parameters efficiently.
Client Nodes: Client-side operations are executed asynchronously, enhancing overall system performance.

Experimental Validation

The framework has been tested on real-world datasets, including L1-regularized logistic regression and Reconstruction Independent Component Analysis (RICA), demonstrating its capability to handle complex, data-intensive tasks. The results show linear scalability with the increase in the number of client nodes, indicating a substantial speedup that validates the framework’s effectiveness in large-scale settings.

Conclusion

The Parameter Server Framework offers a sophisticated solution to the challenges of large-scale distributed machine learning. With its user-friendly interface, high efficiency, scalability, fault tolerance, and solid theoretical foundation, the framework is poised to significantly impact the field of machine learning. The experimental results underscore its practicality and effectiveness, making it an invaluable tool for researchers and practitioners aiming to leverage the full potential of distributed computing in machine learning.

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