vastai-performance-tuning
Optimize Vast.ai GPU instance selection, startup time, and training throughput. Use when optimizing instance selection, reducing startup latency, or maximizing GPU utilization on rented hardware. Trigger with phrases like "vastai performance", "optimize vastai", "vastai slow", "vastai gpu utilization", "vastai throughput". '
Profile →npx skills add jeremylongshore/claude-code-plugins-plus-skills --skill vastai-performance-tuning --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Vast.ai Performance Tuning ## Overview Optimize GPU instance selection, startup time, and training throughput on Vast.ai. Key levers: Docker image caching, GPU selection by dlperf score, data pipeline optimization, and multi-GPU scaling. ## Prerequisites - Vast.ai account with active or planned instances - Understanding of GPU compute bottlenecks - Profiling tools (nvidia-smi, torch.profiler) ## Instructions ### Step 1: Optimize Instance Selection by Performance ```bash # Sort by dlperf (deep learning performance benchmark) instead of price vastai search offers 'num_gpus=1 gpu_ram>=24 reliability>0.95' \ --order 'dlperf-' --limit 10 # The dlperf field measures actual GPU compute throughput # Higher dlperf = faster training even at same GPU model # Variance within same GPU model can be 20-30% ``` ```python def select_by_performance_per_dollar(offers): """Select the offer with best performance per dollar.""" for o in offers: o["perf_per_dollar"] = o.get("dlperf", 0) / max(o["dph_total"], 0.01) return max(offers, key=lambda o: o["perf_per_dollar"]) ``` ### Step 2: Reduce Instance Startup Time ```bash # Use smaller, pre-cached Docker images # FAST: nvidia/cuda:12.1.1-runtime-ubuntu22
- Overview
- Prerequisites
- Instructions
- Step 1: Optimize Instance Selection by Performance
- Step 2: Reduce Instance Startup Time
- Step 3: Data Pipeline Optimization
- Step 4: GPU Memory Optimization
- Step 5: Multi-GPU Scaling
- GPU Performance Reference
- Output
- Error Handling
- Resources
- Next Steps
- Examples
Sort by dlperf (deep learning performance benchmark) instead of price vastai search offers 'num_gpus=1 gpu_ram>=24 reliability>0.95' \ The dlperf field measures actual GPU compute throughput Higher dlperf = faster training even at same GPU model Variance within same GPU model can be 20-30% Use smaller, pre-cached Docker images Pre-install deps in the image, not in onstart BAD (slow startup): vastai create instance $ID --image pytorch/pytorch:latest \ GOOD (fast startup):
What does the vastai-performance-tuning skill do?
Optimize Vast.ai GPU instance selection, startup time, and training throughput. Use when optimizing instance selection, reducing startup latency, or maximizing GPU utilization on rented hardware. Trigger with phrases like "vastai performance", "optimize vastai", "vastai slow", "vastai gpu utilization", "vastai throughput". '
How do I install it?
Run `npx skills add jeremylongshore/claude-code-plugins-plus-skills --skill vastai-performance-tuning --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From jeremylongshore/claude-code-plugins-plus-skills, a repository with 2,630 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.