Diffusion Technology Achieves 10x Faster Text Generation

Inception Labs has introduced the Mercury diffusion language model family, a new approach to speed up text generation. Unlike traditional sequential (autoregressive) language models, Mercury uses diffusion technology, promising significant improvements in speed and efficiency. While currently focused on code generation, this technology could transform the entire field of text generation.

How Diffusion Models Work

Diffusion models gradually recover clean, meaningful information from noisy data. The process has two steps:

  • Forward Process: Noise is added step by step to real data until it becomes random noise.

  • Reverse Process: The model learns to remove the noise, eventually producing high-quality data.

Based on principles from non-equilibrium thermodynamics, diffusion models offer advantages like more stable training, easier parallel processing, and flexible design. This helps them outperform traditional GANs or autoregressive models in tasks like generation.

Inception Labs’ Mercury Models

Unlike traditional models (which generate text left-to-right), Mercury uses a “coarse-to-fine” approach. Starting with pure noise, it refines the output over multiple steps.

Its main application today is code generation. Mercury Coder provides an interactive preview of generated code, improving developers’ workflows by showing how random characters evolve into functional code. The model can generate thousands of tokens per second—up to 10 times faster than traditional methods. Mercury is also available in downloadable versions, making it easy for businesses to integrate into their systems.

Potential Impact of Diffusion Technology

  • Speed & Efficiency: Runs on standard GPUs, speeding up development cycles and application response times.

  • Lower Cost: Works with existing infrastructure, reducing the need for specialized hardware.

  • New Research Opportunities: Combining diffusion and autoregressive models could advance tasks requiring structured logic, like coding or math problem-solving. 

Share this post
Artificial intelligence, space, and humanity
Elon Musk, founder and CEO of SpaceX, Tesla, Neuralink, and xAI, shared his thoughts on the possible directions of the future in a recent interview, with a particular focus on artificial intelligence, space exploration, and the evolution of humanity.
Real-time music composition with Google Magenta RT
The use of artificial intelligence in music composition is not a new endeavor, but real-time operation has long faced significant obstacles. The Google Magenta team has now unveiled a development that could expand both the technical and creative possibilities of the genre. The new model, called Magenta RealTime (Magenta RT for short), generates music in real time and is accessible to anyone thanks to its open source code.
What would the acquisition of Perplexity AI mean for Apple?
Apple has long been trying to find its place in the rapidly evolving market of generative artificial intelligence. The company waited strategically for decades before directing significant resources into artificial intelligence-based developments. Now, however, according to the latest news, the Cupertino-based company may be preparing to take a bigger step than ever before: internal discussions have begun on the possible acquisition of a startup called Perplexity AI.
The new AI chip that is revolutionizing medicine and telecommunications makes decisions in nanoseconds
As more and more devices connect to the internet and demand grows for instant, high-bandwidth applications such as cloud-based gaming, video calls, and smart homes, the efficient operation of wireless networks is becoming an increasingly serious challenge. The problem is further exacerbated by the fact that the wireless spectrum—the available frequency band—is limited. In their search for a solution, engineers are increasingly turning to artificial intelligence, but current systems are often slow and energy-intensive. A new development that brings data transmission and processing up to the speed of light could change this situation.
This is how LLM distorts
With the development of artificial intelligence (AI), more and more attention is being paid to so-called large language models (LLMs), which are now present not only in scientific research but also in many areas of everyday life—for example, in legal work, health data analysis, and computer program coding. However, understanding how these models work remains a serious challenge, especially when they make seemingly inexplicable mistakes or give misleading answers.
MiniMax-M1 AI model, targeting the handling of large texts
With the development of artificial intelligence systems, there is a growing demand for models that are not only capable of interpreting language, but also of carrying out complex, multi-step thought processes. Such models can be crucial not only in theoretical tasks, but also in software development or real-time decision-making, for example. However, these applications are particularly sensitive to computational costs, which are often difficult to control using traditional approaches.

Linux distribution updates released in the last few days