Which LLM Is Right for
Your AI Agent?

NemoClaw is an open-source LLM stack purpose-built for autonomous agents. Unlike general-purpose models, its three tiers (Nano, Super, Ultra) are tuned for different agentic workloads — from edge deployments to complex multi-step reasoning. You retain full control over weights, data, and cost. Proprietary models like GPT-4o are stronger on some benchmarks but come with usage costs, data privacy trade-offs, and latency at scale.

Nano is for edge and latency-critical agents: IoT triggers, real-time UI assistants, lightweight classifiers. Under 2 seconds, minimal GPU.

Super is the balanced choice: cloud-hosted agents handling multi-tool workflows, coding assistance, or moderate research tasks. Great price-to-performance ratio.

Ultra is for heavy-lifting: long-context analysis (entire codebases, legal documents), multi-agent orchestration, complex strategic planning. Needs data-centre GPU.

Simple task agents (form filling, API calls, short Q&A) rarely exceed 16K tokens. The danger zone is tool call history — each tool use can add 1–5K tokens, so a 20-step agent can easily hit 80K. For codebases or document analysis, plan for 128K+. Multi-agent pipelines that pass summaries between workers can manage with smaller windows per agent.

Yes — with the right model. NemoClaw Nano and quantised 7B models can run on CPU-only hardware or consumer GPUs. Gemini Flash (proprietary) also excels at low-latency cloud edge. The trade-off is reasoning depth: edge-optimised models handle routing, classification, and simple instructions well, but struggle with multi-step planning or code generation tasks that larger models handle confidently.