Nous Recherche

Nous Research is an AI research company that describes its mission as creating and spreading open access to intelligence. It is best known for two lines of work. The first is the Hermes series of large language models, which take open base models released by other labs and fine-tune them into chat assistants, publishing the resulting weights for anyone to download. The second is research and infrastructure for decentralized training, meaning training one model across many independently operated computers connected by the ordinary internet rather than inside a single data center. Today the company also offers Hermes Agent, an assistant it describes as self-improving, and a hosted service called Nous Portal.

Fine-tuning is the core of the Hermes work. A base model, trained by another lab on trillions of words, knows a great deal but does not reliably follow instructions or hold a conversation. Fine-tuning continues training on curated examples of the desired behavior, such as multi-turn chats, tool calls and reasoning traces. Hermes models use the ChatML prompt format and add dedicated system prompts for function calling and JSON-only structured output, which makes them convenient for developers building agents. The Hermes 3 report calls it a neutrally aligned generalist instruct and tool-use model, a stance that leaves more of the behavior to the user's and developer's instructions.

The Hermes 3 technical report, by Ryan Teknium, Jeffrey Quesnelle and Chen Guang, was posted on August 15, 2024. Its largest model, built on Meta's Llama 3.1 405B, was described on its model card as the first full-parameter fine-tune of that base model, meaning every weight was updated rather than a small adapter. The report claims state-of-the-art results among open-weight models on several public benchmarks, and the model card describes Hermes 3 as competitive with, and in some areas better than, Meta's own Llama 3.1 Instruct models. The practical cost of such a model is large: the card notes FP16 inference needs over 800 GB of GPU memory, so an FP8 version of about 430 GB is offered.

Hermes 4 followed with a technical report dated August 25, 2025, describing a family of hybrid reasoning models that can either answer directly or reason step by step inside think tags. Its 405B model again builds on Llama 3.1 405B, with smaller 70B and 14B variants. According to its model card, the training set grew to roughly 5 million samples and 60 billion tokens, from about 1 million samples and 1.2 billion tokens for Hermes 3. Nous Research also created RefusalBench, a test of whether a model helps with requests that many models refuse, and the model card says Hermes 4 achieves state-of-the-art results on it. That framing reflects the company's emphasis on user control, which also means deployers must add their own safety policies where needed.

The decentralized training work tackles a specific bottleneck. In standard data-parallel training, every GPU must share its full gradients with every other GPU after each step, which requires the very fast links found inside data centers. In August 2024 Nous Research published a preliminary report on DisTrO, a family of distributed optimizers it says reduces inter-GPU communication by three to four orders of magnitude. The underlying research, DeMo (Decoupled Momentum Optimization), was posted in November 2024 by Bowen Peng, Jeffrey Quesnelle, Diederik P. Kingma and colleagues. Each GPU keeps its own momentum, converts it with a discrete cosine transform, and shares only the largest components. On 300M and 1B-parameter language models, the paper reports up to 85 times less data per GPU than standard AdamW training with comparable loss and accuracy.

Psyche, announced on May 14, 2025, turns those optimizers into a network. A coordinator runs as a smart contract on the Solana blockchain, tracking participants, assigning data and synchronizing training steps, while GPUs exchange compressed updates directly peer to peer. Psyche adds overlapped training, so nodes begin the next step while results transfer, and 1-bit sign quantization of updates. Its first run, Consilience, is a 40-billion-parameter dense model using the Multi-head Latent Attention design from DeepSeek V3, trained on 20 trillion tokens drawn from FineWeb, FineWeb-2 and The Stack V2. Nous described it as the largest pretraining run over the internet to that date. A December 2024 test trained a 15-billion-parameter model.

The company's funding is tied to this crypto-coordinated approach. In April 2025 SiliconANGLE reported a 50 million dollar Series A almost entirely funded by the crypto-focused firm Paradigm, following seed rounds totaling 20 million dollars, and cited a Fortune report that the round implied a 1 billion dollar token valuation. Cofounder Karan Malhotra described contributed compute as a transaction rather than a donation, with crypto rewards compensating contributors.

Compared with large closed labs, Nous Research works mostly on top of other organizations' base models and publishes weights openly, so its models are inexpensive to obtain but inherit the licenses and limits of their bases; the Hermes 3 and Hermes 4 405B models carry the Llama 3 license. Compared with other open fine-tuners, its distinctive bets are the neutral alignment stance and decentralized training. The limitations are concrete. Running the largest Hermes models still needs hundreds of gigabytes of GPU memory. Decentralized training remains early, and the Psyche announcement itself notes that verifying that participants submitted honest results is an open problem. Benchmark claims in technical reports come from the authors and are best checked against independent evaluations for a given use.