Agentic AI is altering the way in which customers get work carried out. Following the success of OpenClaw, the neighborhood is embracing new open supply agentic frameworks. The most recent is Hermes Agent, which crossed 140,000 GitHub stars in underneath three months and, as of final week, is essentially the most used agent on the earth in line with OpenRouter.
Developed by Nous Analysis, Hermes is designed for reliability and self-improvement — two qualities which have traditionally been arduous to attain with brokers. It’s provider- and model-agnostic by design, and optimized for always-on native use, making NVIDIA RTX PCs, NVIDIA RTX PRO workstations and NVIDIA DGX Spark the best {hardware} to run it at full velocity, across the clock.
Qwen 3.6, a brand new sequence of high-performance, open weight massive language fashions (LLMs) from Alibaba, are perfect for working native brokers like Hermes. The Qwen 3.6 27B and 35B parameter fashions are outperforming their previous-generation 120B and 400B parameter mannequin counterparts and run on NVIDIA RTX and DGX Spark for accelerated agentic AI.
Hermes: Native AI Agent Capabilities Accelerated
Like different widespread brokers, Hermes integrates with messaging apps, can entry native recordsdata and functions, and runs 24/7. However 4 standout capabilities set it aside:
- Self-Evolving Abilities: Hermes writes and refines its personal expertise. Each time the agent encounters a fancy activity or receives suggestions, it saves its learnings as a ability so it may adapt and enhance over time.
- Contained Sub-Brokers: Hermes treats sub-agents as short-lived, remoted staff devoted to a sub-task — with a centered context and set of instruments. This retains activity group tidy, minimizes confusion for the agent and permits Hermes to run with smaller context home windows, which is right for native fashions.
- Reliability by design: Nous Analysis curates and stress-tests each ability, device and plug-in that ships with Hermes. The consequence: Hermes simply works — even with 30 billion-parameter-class native fashions — with out the fixed debugging that almost all different agent frameworks require.
- Identical mannequin, higher outcomes: Developer comparisons utilizing similar fashions throughout frameworks persistently present stronger leads to Hermes. The distinction is the framework: Hermes is an energetic orchestration layer, not a skinny wrapper, enabling persistent, on-device brokers as a substitute of task-by-task execution.
Each the Hermes agent and the underlying LLM are constructed to run domestically — which implies the standard of {hardware} immediately determines the standard of a person’s expertise. NVIDIA RTX GPUs are purpose-built for this sort of workload.
Qwen 3.6: Information Middle-Stage Intelligence, Regionally
The most recent Qwen 3.6 fashions construct on the acclaimed Qwen 3.5 sequence to ship one other leap ahead for native AI brokers. The brand new Qwen 3.6 35B mannequin runs on roughly 20GB of reminiscence whereas surpassing 120 billion-parameter fashions, which require 70GB+ of reminiscence.
As well as, Qwen 3.6 27B is a brand new, dense mannequin with extra energetic parameters — matching the accuracy of 400 billion-parameter fashions like Qwen 3.5 397B whereas being one-sixteenth the dimensions. Operating on high-end RTX GPUs offers the mannequin the computing energy it wants for a speedy expertise.
These fashions are perfect for native brokers like Hermes, and NVIDIA GPUs and DGX Spark are the quickest technique to run them. NVIDIA Tensor Cores speed up AI inference to ship increased throughput and decrease latency — so Hermes can work by means of a multistep activity or refine one in every of its personal expertise in seconds relatively than minutes.
DGX Spark: The At all times-On Agentic Pc
Brokers like Hermes are constructed to run repeatedly — responding to requests, planning multistep duties, executing autonomously and self-improving. NVIDIA DGX Spark is the best companion — a compact, environment friendly standalone machine constructed for sustained, all-day agentic workflows.
With 128GB of unified reminiscence and 1 petaflop of AI efficiency, NVIDIA DGX Spark can run 120 billion-parameter mixture-of-experts fashions all day. And the brand new Qwen 3.6 35B mannequin delivers equal intelligence in a leaner footprint — working quicker and giving customers the capability to run concurrent workloads.
To maximise efficiency and ease of use, learn the Hermes DGX Spark playbook. Plus, register for upcoming hands-on periods in NVIDIA’s “Construct It Your self” agentic AI sequence to learn to construct autonomous AI brokers with NemoClaw and OpenShell.
NVIDIA DGX Spark is accessible to order from NVIDIA’s manufacturing companions — go to {the marketplace}.
Getting Began With Hermes on NVIDIA {Hardware}
Operating Hermes domestically on NVIDIA {hardware} is simple.
Go to the Hermes GitHub repository to get began, and pair it with a most popular native mannequin and runtime. Run Hermes alongside Qwen 3.6 through llama.cpp, LM Studio or Ollama. Hermes Agent ships with LM Studio and Ollama assist out of the field for the best path to an area agent.
Whether or not for an area AI fanatic exploring the frontier of private brokers or a developer constructing native tooling for his or her workflows, Hermes on NVIDIA {hardware} presents a uniquely succesful and dependable basis.
Keep tuned for extra updates from RTX AI Storage on the most recent open fashions and brokers optimized for NVIDIA RTX {hardware}.
#ICYMI: The Newest From RTX AI Storage
✨ NVIDIA RTX PRO GPUs ship as much as 3x quicker token technology working Qwen 3.6 fashions with llama.cpp. Get the real-time responsiveness wanted for native AI, the place brokers can sort out multistep duties and refine their expertise to maintain workflows seamless.
Google’s Gemma 4 26B and 31B fashions now accessible as NVFP4 checkpoints for even quicker efficiency on NVIDIA Blackwell GPUs. Pair the NVFP4 checkpoints with Google’s new Multi-Token Prediction drafters to rise up to 3x quicker inference at similar output high quality, enabling frontier-class reasoning to run domestically on NVIDIA GPUs.
Mistral Medium model 3.5, additionally launched in April, contains compatibility updates with llama.cpp and Ollama, enabling customers to run on NVIDIA RTX PRO and DGX Spark methods.
🦞 NVIDIA just lately launched NVIDIA NemoClaw, an open supply stack that optimizes OpenClaw experiences on NVIDIA gadgets by growing safety and supporting native fashions. NemoClaw now helps Home windows Subsystem for Linux (WSL2), bringing assist to fanatics and builders on Microsoft’s platform. Get began with NemoClaw on DGX Spark with this step-by-step playbook.
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