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/nyosegawa/agentic-bench/gpu-runnernpx skills add nyosegawa/agentic-bench --skill gpu-runnergit clone --depth 1 https://github.com/nyosegawa/agentic-benchWrote 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/nyosegawa/agentic-bench/gpu-runner)<a href="https://agentmods.dev/skills/nyosegawa/agentic-bench/gpu-runner"><img src="https://agentmods.dev/badge/skills/nyosegawa/agentic-bench/gpu-runner.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 | $0.00107 | $0.01463 |
| Opus 5 | $0.00053 | $0.00732 |
| Sonnet 5 | $0.00021 | $0.00293 |
| Haiku 4.5 | $0.00011 | $0.00146 |
Grade A, and why
gpu-runner 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 4d 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 — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GPU Runner
You are executing model inference on the appropriate GPU cloud provider.
Your Goal
Given a model, its requirements, and a chosen provider:
- Write inference code tailored to the model
- Execute it on the selected provider
- Collect outputs (text, images, audio, metrics)
- Handle errors and retry with alternatives if needed
Provider Selection (if not pre-selected)
Check .env for available credentials, then sort by cheapest hourly cost:
- HF Inference API — Free with HF Pro. Requires
HF_TOKEN. Catalog models only. - HF Inference Endpoints — Any HF model on dedicated GPU.
HF_TOKENonly. $0.50–2.50/hr. - Colab Pro — Chrome MCP. No token needed. $9.99/month subscription. Up to ~30B.
- Modal — Requires
MODAL_TOKEN_ID+MODAL_TOKEN_SECRET. $30/month free tier. $0.59–3.95/hr. - beam.cloud — Requires
BEAM_TOKEN. Existing credit. $0.54–3.50/hr. - Vast.ai — Requires
VAST_API_KEY. Marketplace pricing (cheapest GPUs). $0.10–2.00/hr. - RunPod — Requires
RUNPOD_API_KEY. Pods + Serverless. $0.34–2.69/hr.
Token availability check: If a provider's env vars are not set, skip it.
Provider-Specific Guides
Before executing, read the relevant provider reference:
| Provider | Reference | When to Use |
|---|---|---|
| HF Inference API | (inline below) | Model on HF, API-supported, free |
| HF Inference Endpoints | references/hf-endpoints.md |
Any HF model, cheapest dedicated GPU |
| Colab Pro | references/colab-chrome-mcp.md |
Up to ~30B, interactive debugging |
| Modal | references/modal.md |
30B+, serverless, reliable GPUs |
| beam.cloud | references/beam-cloud.md |
Dedicated endpoints, existing credit |
| Vast.ai | references/vast.md |
Cheapest GPUs, marketplace pricing |
| RunPod | references/runpod.md |
Pods (persistent VMs), balanced price/reliability |
HF Inference API (inline — simple enough)
import os
from huggingface_hub import InferenceClient
client = InferenceClient(token=os.environ["HF_TOKEN"])
# Text generation
response = client.text_generation("Hello, ", model="MODEL_ID", max_new_tokens=100)
# Image generation
image = client.text_to_image("A cat", model="MODEL_ID")
image.save("output.png")
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 4d ago First seen · 141 lines · 107 tokens per session scan A a27b00db294f
gpu-runner is a skill published in the GitHub repository nyosegawa/agentic-bench (5 stars, last pushed 6mo ago), licensed MIT. It adds 107 tokens to every session and 1,463 once invoked, about $0.0005 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 2026-08-31.
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