What is NemoClaw?
At GTC 2026, NVIDIA announced NemoClaw — an enterprise-grade security and performance stack built on top of OpenClaw. Think of it as OpenClaw with a full NVIDIA powerhouse underneath: a hardened runtime environment, a purpose-built 120-billion-parameter reasoning model, a distributed inference engine, and a privacy layer that keeps sensitive data off the wire.
While base OpenClaw is designed for individual users and developers, NemoClaw targets enterprises, regulated industries, and anyone who needs auditable, policy-enforced AI agents. Healthcare, finance, legal, and government deployments are the primary use cases.
OpenShell Runtime
🐚 OpenShell — Hardened Execution Environment
OpenShell replaces OpenClaw's default Python subprocess execution with a fully sandboxed runtime. Every agent action runs inside an isolated container with explicit capability grants. No action can touch files, network resources, or processes outside its declared scope.
Key OpenShell features include filesystem namespacing (each task gets a scoped view of the filesystem), network egress policies (outbound connections must be whitelisted in the policy file), and a full audit trail — every tool call, file read, and network request is cryptographically logged.
For developers familiar with Docker, OpenShell feels similar but is lighter-weight, with sub-100ms container spin-up. It's not a full VM — it uses Linux namespaces and seccomp profiles for isolation while keeping latency low.
Nemotron-3-Super-120B
🧠 Nemotron-3-Super-120B — The Purpose-Built Reasoning Model
NemoClaw ships with Nemotron-3-Super-120B, NVIDIA's 120-billion-parameter model fine-tuned specifically for agentic tasks: multi-step planning, tool use, policy compliance, and enterprise document processing.
What makes it well-suited for OpenClaw workloads? Standard LLMs are trained primarily on conversation. Nemotron-3-Super is fine-tuned on long-horizon task graphs — sequences of actions that span dozens of steps, require backtracking, and must satisfy explicit constraints throughout.
Benchmark highlights from the GTC 2026 announcement include a 94.2% task completion rate on the AgentBench enterprise suite, 40% fewer tool-call errors vs GPT-4o on multi-step document workflows, and native support for the OpenClaw tool-call format without an adapter layer.
The model requires a minimum of one NVIDIA H100 or two A100 80GB GPUs for inference. For organisations without on-prem hardware, NVIDIA Cloud serving is available.
Dynamo Inference Engine
⚡ Dynamo — Distributed Inference for High-Throughput Agents
Dynamo is NVIDIA's inference serving layer that sits between NemoClaw's orchestrator and the model. It handles request routing, KV-cache sharing across agent instances, and dynamic batching to maximise GPU utilisation.
In a multi-agent deployment where dozens of sub-agents are running in parallel, Dynamo intelligently routes requests to avoid redundant computation. If two agents need the same context prefix (a common occurrence in fan-out orchestration patterns), Dynamo serves the cached KV state rather than recomputing it — reducing latency by up to 60% in high-concurrency scenarios.
Dynamo also handles graceful degradation: if a GPU node goes offline mid-task, in-flight requests are checkpointed and rerouted automatically. For enterprise deployments where uptime is critical, this makes a significant difference.
Privacy Router
🔒 Privacy Router — PII Filtering and Data Residency
The Privacy Router sits at the ingress and egress points of the NemoClaw pipeline. It scans all inputs and outputs for personally identifiable information (PII), applies redaction or tokenisation based on your policy configuration, and enforces data residency rules.
Configuration lives in a YAML policy file. Here's an example that redacts email addresses and enforces EU data residency:
privacy:
pii_detection:
enabled: true
redact: [email, phone, ssn, credit_card]
action: tokenise # or: redact, block
data_residency:
allowed_regions: [eu-west-1, eu-central-1]
block_egress_outside_region: true
audit:
log_level: full
retention_days: 90The Privacy Router uses NVIDIA's NeMo Guardrails under the hood for PII detection, with custom model fine-tuning available for domain-specific sensitive data (e.g. medical record numbers, proprietary product codes).
Who is NemoClaw For?
NemoClaw is overkill for personal use. If you're an individual user or a small team, standard OpenClaw with careful SOUL.md limits is more than sufficient. NemoClaw makes sense when:
- You need regulatory compliance — HIPAA, GDPR, SOC 2, or similar frameworks that require audit trails and data controls
- Your agents handle sensitive data — patient records, financial data, legal documents
- You're running high-throughput deployments — dozens to hundreds of concurrent agent sessions
- You need air-gapped operation — Nemotron-3-Super-120B can run fully on-prem with no cloud dependency
For a detailed technical breakdown with stack diagrams, component comparison tables, and the full installation guide, see the dedicated NemoClaw page on this site.