"The advantage of swarm inference, the company says, is that frontier AI models often become less accurate when " reasoning" - the process by which models solve complex problems by breaking them into a series of smaller steps. Swarm inference supposedly helps avoid this problem by considering responses from multiple smaller models and ranking them by quality to obtain a better answer. Also, it's supposedly more affordable because it runs on distributed consumer hardware instead of in billion-dollar datacenters."
""Inference through the swarm is up to three times cheaper than frontier reasoning models from OpenAI and Anthropic on a per-token basis," Ivan Nikitin, co-founder and CEO, told The Register in an email. "Actual cost depends on task complexity." Nikitin told The Register in a phone interview that he and his co-founders turned to decentralization not for the sake of novelty, but to address a practical issue: the shortage of centralized computing resources."
"On Friday, the company published benchmark results claiming that its swarm inference scheme outperformed OpenAI's GPT-5, Google Gemini 2.5 Pro, Anthropic Claude Opus 4.1, and DeepSeek R1 on reasoning tests, specifically GPQA Diamond, MATH-500, AIME 2024, and LiveCodeBench."
Fortytwo was founded last year to use a decentralized swarm of small AI models running on personal computers to achieve scaling and cost advantages over centralized AI services. Benchmark results reported that the swarm inference scheme outperformed GPT-5, Google Gemini 2.5 Pro, Anthropic Claude Opus 4.1, and DeepSeek R1 on reasoning tests including GPQA Diamond, MATH-500, AIME 2024, and LiveCodeBench. Swarm inference aims to mitigate accuracy degradation during complex multi-step reasoning by aggregating and ranking responses from multiple smaller models. The approach is presented as markedly more affordable by leveraging distributed consumer hardware and addressing centralized compute shortages and rate limits.
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