Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/nebius/nebius-physical-ai/gpu-selectionnpx skills add nebius/nebius-physical-ai --skill gpu-selectiongit clone --depth 1 https://github.com/nebius/nebius-physical-aiWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nebius/nebius-physical-ai/gpu-selection)<a href="https://agentmods.dev/skills/nebius/nebius-physical-ai/gpu-selection"><img src="https://agentmods.dev/badge/skills/nebius/nebius-physical-ai/gpu-selection.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00028 | $0.01150 |
| Opus 5 | $0.00014 | $0.00575 |
| Sonnet 5 | $0.00006 | $0.00230 |
| Haiku 4.5 | $0.00003 | $0.00115 |
Grade A, and why
gpu-selection scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GPU Selection
When To Use
Use this skill when a task asks which GPU family to use, changes workflow resources, updates image routing, or reviews render/training placement.
Procedure
- Identify whether the workload needs RT cores, tensor throughput, multi-GPU scaling, or only CPU resources.
- Check the tool-specific skill for hard constraints.
- Encode the choice in CLI flags, SDK config, or workflow YAML env/resources.
- Keep image variants aligned with GPU selection.
- For direct-Kubernetes Jobs, discover
nvidia.com/gpu.productlabels and construct an ordered, compatible candidate list. Move to the next product only for concrete scheduler evidence (Unschedulable, insufficient GPU resource, or no matching product/affinity); runtime, pull, credential, checkpoint, and application failures are not placement failures.
Three-Tier Contract
- CLI: commands expose GPU choices through flags such as
--gpu-type,--gpu-preset,--runtime, or tool-specific image variant options. - SDK: runtime config and request builders should carry GPU type/count rather than deriving it from private environment names.
- YAML: workflow resources and env vars must express the GPU target explicitly enough for reviewers to validate routing.
Current Defaults
- H100: general training, CLIP embedding, detection, MJLab, Cosmos inference, LeRobot training smoke, and non-render throughput.
- L40S: Isaac Lab and SONIC render validation on VM hosts.
- RTX PRO 6000 Blackwell: Isaac Lab and SONIC render validation on Kubernetes with mounted NVIDIA GPU Operator drivers.
- B200 / B300: headless, state-based training and inference only.
- CPU: Retargeting and many dataset curation/import steps.
Blackwell Is Two Different Targets
"Blackwell" spans two CUDA majors, and their binaries are mutually incompatible:
| GPU | Compute capability | SM | Nebius platform |
|---|---|---|---|
| RTX PRO 6000 Blackwell | 12.0 | sm_120 |
gpu-rtx6000 |
| B200 | 10.0 | sm_100 |
gpu-b200-sxm (us-central1) |
| B300 (Blackwell Ultra) | 10.3 | sm_103 |
gpu-b300-sxm |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 100 lines · 28 tokens per session scan A 3e3415c25152
gpu-selection is a skill published in the GitHub repository nebius/nebius-physical-ai (27 stars, last pushed today), licensed Apache-2.0. It adds 28 tokens to every session and 1,150 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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