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Recent advancements in diffusion models indicate promising performance that rivals traditional models, even in terms of speed. Research from LLaDA highlights that their 8 billion parameter model demonstrates comparable capabilities to LLaMA3 8B across multiple benchmarks, delivering noteworthy results in areas such as MMLU, ARC, and GSM8K.
Mercury, on the other hand, claims substantial enhancements in processing speed. The Mercury Coder Mini has achieved an impressive 88.0 percent accuracy on the HumanEval benchmark and 77.1 percent on MBPP, figures that are competitive with GPT-4o Mini. However, what sets Mercury apart is its operational speed of 1,109 tokens per second—significantly faster than GPT-4o Mini, which operates at only 59 tokens per second. This translates to approximately a 19-fold speed advantage without compromising performance on coding tasks.
According to Mercury’s documentation, their models run at speeds exceeding 1,000 tokens per second on Nvidia H100s—an achievement previously attainable only through specialized chips from companies like Groq, Cerebras, and SambaNova. When compared to other speed-optimized models, the Mercury Coder Mini outpaces Gemini 2.0 Flash-Lite by roughly 5.5 times (201 tokens per second) and Claude 3.5 Haiku by around 18 times (61 tokens per second).
Opening a potential new frontier in LLMs
While diffusion models offer substantial benefits, they also come with certain drawbacks. Unlike conventional models that generate output in a single pass, diffusion models require multiple forward passes to produce a complete response. However, their ability to process all tokens in parallel allows them to achieve a higher overall throughput, mitigating the potential downsides of this architecture.
The implications of these speed improvements are significant, particularly for code completion tools where fast response times can enhance developer efficiency. Additionally, applications in conversational AI, mobile environments with limited resources, and any use case requiring rapid responses would benefit from these advancements.
If diffusion-based language models can sustain high-quality outputs while also increasing processing speed, they might revolutionize AI text generation as we know it. The openness among AI researchers to explore alternative approaches is a clear indication of the possibilities that lie ahead.
Independent AI expert Simon Willison commented on the evolving landscape, stating, “I love that people are experimenting with alternative architectures to transformers; it illustrates just how much of the LLM space still remains unexplored.”
On social media platform X, former OpenAI researcher Andrej Karpathy shared his thoughts on Inception, stating, “This model has the potential to be different, possibly showcasing unique strengths and weaknesses. I encourage people to try it out!“
As researchers continue to investigate the capabilities of larger diffusion models, important questions remain about their ability to match the performance of established models like GPT-4o and Claude 3.7 Sonnet, particularly in tackling increasingly sophisticated reasoning tasks. For the moment, these diffusion models present a viable alternative for smaller AI language implementations without significant compromises on performance.
To explore the Mercury Coder, you can try it yourself on Inception’s demo site. Additionally, you can download code for LLaDA or check out a demo available on Hugging Face.
Source
arstechnica.com