Which Open LLM Should
Power Your Agent?
NemoClaw Nano, Super, or Ultra — the tier you pick shapes your cost, latency, and quality ceiling. This guide walks you through every decision axis so you get it right the first time.
The Three Tiers at a Glance
Pick the tier that matches your latency budget, hardware, and task complexity — then verify with the decision tree below.
NemoClaw Nano
Edge-firstDesigned for edge devices, browser inference, and latency-critical tasks where every millisecond counts. Runs comfortably on a single consumer GPU or modern edge accelerator with no server infra.
- Real-time assistants (<100 ms SLA)
- Edge / IoT / offline inference
- Browser/WebGPU agents
- High-QPS cost-sensitive APIs
- Simple classification + extraction
- Complex multi-step reasoning
- Long context (>16K tokens)
- Legal / medical accuracy requirements
NemoClaw Super
Production-gradeThe sweet spot for most production agents. Strong enough for coding assist, customer support, data extraction, and multi-step pipelines — without the infrastructure cost of running 70B+ weights.
- Coding assist + code review
- Customer support automation
- Structured data extraction
- RAG + retrieval-augmented pipelines
- Multi-step workflows (5–15 steps)
- B2B SaaS agents at scale
- Sub-50 ms latency requirements
- Very long context (>64K tokens) on budget
- Maximum quality for high-stakes domains
NemoClaw Ultra
Maximum qualityFor tasks where quality is the only metric that matters. Frontier open-weight performance at proprietary-rivalling accuracy — but requires serious GPU infrastructure and accepts higher latency.
- Legal review & contract analysis
- Medical/clinical decision support
- Complex research synthesis
- Advanced code generation (>500-line PRs)
- High-stakes financial modelling
- Deep multi-agent orchestration (15+ steps)
- Latency-sensitive UX (<200 ms)
- Small teams without MLOps
- Budget under $5K/month infra
Decision Tree: Find Your Tier in 5 Questions
Answer each question to narrow down the right NemoClaw tier. Or use our interactive selector for a ranked recommendation across all open and proprietary models.
1. What is your latency requirement?
2. Where will the model run?
3. What is the task complexity?
4. What is your monthly token volume?
5. How strict are your compliance requirements?
Deployment Context Matrix
Which tier fits which infrastructure context — at a glance.
| Deployment Context | Nano | Super | Ultra | Notes |
|---|---|---|---|---|
| Edge / IoT device | Nano is the only viable option at <8 GB VRAM | |||
| Browser / WebGPU | WebLLM supports 4B and smaller models only | |||
| Single A10G (24 GB VRAM) | Super (13B–30B) runs comfortably here | |||
| Single A100 (80 GB VRAM) | Partial | Ultra at 70B works; 100B+ needs multi-GPU | ||
| Multi-GPU cluster (4–8× H100) | Full Ultra (70B–405B) runs here | |||
| Managed inference API (Together, Fireworks) | All tiers available via serverless; check rate limits | |||
| AWS Bedrock / GCP Vertex (via API) | Limited | Managed serving with IAM; check model availability | ||
| Air-gapped / on-prem | All tiers — ensure hardware meets VRAM requirements | |||
| Kubernetes with GPU operator | vLLM or TensorRT-LLM recommended for serving |
Task Complexity → Tier Mapping
Real agent tasks mapped to the tier that handles them reliably in production.
The Hybrid Strategy: Route by Complexity
The best production systems don't pick one tier — they route tasks to the cheapest model that can handle them reliably.
Example: Coding Agent Pipeline
Frequently Asked Questions
NemoClaw is an open-source agent framework from NVIDIA's NeMo project. The Nano/Super/Ultra naming describes deployment tiers: Nano targets small models (1B–7B) optimised for edge and latency; Super targets mid-range (13B–70B) for production; Ultra targets the largest open-weight models (70B+) for maximum quality. The framework itself is the same — only the model backend and hardware requirements differ.
Yes. NemoClaw uses a pluggable model backend. Switching tiers means updating the model endpoint in your config — the agent orchestration code, tool definitions, and prompt templates stay identical. This is a key advantage over proprietary SDKs where switching providers requires rewriting the integration layer.
On structured agentic tasks (tool calling, JSON output, multi-step code generation), Nemotron-Ultra-253B and Llama 3.1-405B are competitive with GPT-4o. GPT-4o still leads on broad world knowledge (fresher training), nuanced creative writing, and very long context (128K tokens natively). Ultra wins on data privacy, cost at scale, and the ability to run completely offline.
vLLM is the most popular open-source serving stack for NemoClaw deployments — it supports PagedAttention for efficient VRAM use, tensor parallelism for multi-GPU, and OpenAI-compatible API endpoints. TensorRT-LLM is preferred for NVIDIA hardware when maximum throughput matters. For managed inference, Together.ai, Fireworks.ai, and Anyscale all host NemoClaw-compatible models with pay-per-token billing.
Yes, but capability varies by model. Llama 3.2-3B-Instruct and Phi-3-mini support basic function calling with JSON schemas. For complex nested tool use or multi-tool orchestration, Super (13B+) is more reliable. Nano is best-suited to single-tool calls, classification, and constrained output tasks.
The NemoClaw framework itself is Apache 2.0 — fully free for commercial use. The underlying models have separate licenses: Llama models use Meta's community license (free for most commercial use under 700M monthly active users), Mistral models are Apache 2.0, and NVIDIA's Nemotron models use NVIDIA Open Model License. Check the specific model's license before production deployment at enterprise scale.
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Still not sure which tier?
Use our interactive LLM Selector. Answer 5 questions, get 7 ranked model recommendations — NemoClaw Nano, Super, Ultra, plus GPT-4o, Claude, Gemini, and more.
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