From vastai-pack
Optimizes Vast.ai GPU rental costs using cost-per-TFLOP selection, spot instance analysis, Python auto-destroy timers, and Bash idle detection.
How this skill is triggered — by the user, by Claude, or both
Slash command
/vastai-pack:vastai-cost-tuningThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Minimize Vast.ai GPU cloud costs by choosing the right GPU for your workload, leveraging interruptible (spot) instances, eliminating idle compute, and implementing auto-destroy safeguards. Vast.ai pricing is dynamic and varies significantly: RTX 4090 ($0.15-0.30/hr), A100 80GB ($1.00-2.00/hr), H100 SXM ($2.50-4.00/hr).
Minimize Vast.ai GPU cloud costs by choosing the right GPU for your workload, leveraging interruptible (spot) instances, eliminating idle compute, and implementing auto-destroy safeguards. Vast.ai pricing is dynamic and varies significantly: RTX 4090 ($0.15-0.30/hr), A100 80GB ($1.00-2.00/hr), H100 SXM ($2.50-4.00/hr).
vastai CLI installed# Compare cost-per-TFLOP across GPU types
GPU_SPECS = {
"RTX_4090": {"fp16_tflops": 82.6, "vram": 24},
"A100": {"fp16_tflops": 77.97, "vram": 80},
"H100_SXM": {"fp16_tflops": 267, "vram": 80},
"RTX_3090": {"fp16_tflops": 35.6, "vram": 24},
"A6000": {"fp16_tflops": 38.7, "vram": 48},
}
def cost_per_tflop(gpu_name, dph):
specs = GPU_SPECS.get(gpu_name, {"fp16_tflops": 1})
return dph / specs["fp16_tflops"]
# Often RTX 4090 is the best value for inference
# A100 is best for training large models needing >24GB VRAM
# H100 is best only when wall-clock time justifies 10x price premium
# Interruptible (spot) instances are 30-60% cheaper
vastai search offers 'num_gpus=1 gpu_name=RTX_4090 rentable=true' \
--order dph_total --limit 5
# Compare interruptible vs on-demand pricing
# Use interruptible for: batch inference, checkpointed training
# Use on-demand for: final training epochs, production inference
import time, subprocess, json
def auto_destroy_after(instance_id, max_hours=4):
"""Destroy instance after max_hours to prevent cost overruns."""
max_seconds = max_hours * 3600
time.sleep(max_seconds)
subprocess.run(["vastai", "destroy", "instance", str(instance_id)], check=True)
print(f"Instance {instance_id} auto-destroyed after {max_hours}h")
# Run in background thread when provisioning
import threading
watchdog = threading.Thread(target=auto_destroy_after, args=(inst_id, 4), daemon=True)
watchdog.start()
#!/bin/bash
# Find and destroy idle instances (GPU util < 10% for >10 min)
vastai show instances --raw | python3 -c "
import sys, json
for inst in json.load(sys.stdin):
if inst.get('actual_status') == 'running':
gpu_util = inst.get('gpu_util', 0)
if gpu_util < 10:
print(f'IDLE: Instance {inst[\"id\"]} GPU util={gpu_util}% '
f'(\${inst.get(\"dph_total\", 0):.3f}/hr)')
"
def daily_cost_report():
"""Calculate current daily burn rate from running instances."""
result = subprocess.run(
["vastai", "show", "instances", "--raw"],
capture_output=True, text=True)
instances = json.loads(result.stdout)
total_hourly = 0
for inst in instances:
if inst.get("actual_status") == "running":
dph = inst.get("dph_total", 0)
total_hourly += dph
print(f" {inst['id']}: {inst.get('gpu_name')} ${dph:.3f}/hr")
print(f"\nTotal: ${total_hourly:.3f}/hr = ${total_hourly * 24:.2f}/day")
--order dph_total to find cheapest offers| Error | Cause | Solution |
|---|---|---|
| Unexpected $50+ bill | Forgot to destroy instances | Implement auto-destroy watchdog |
| GPU idle at $2/hr | Waiting for data download | Pre-stage data before provisioning GPU |
| Spot preemption mid-job | Cheapest instance reclaimed | Checkpoint frequently; auto-recover |
For reference architecture, see vastai-reference-architecture.
Budget cap: Set dph_total<=0.25 in search queries and auto_destroy_after(inst_id, 4) to cap any single job at $1.00.
GPU comparison: Run the same workload on RTX 4090 ($0.20/hr) vs A100 ($1.50/hr). If the A100 finishes in less than 1/7th the time, it's cheaper overall.
npx claudepluginhub jeremylongshore/claude-code-plugins-plus-skills --plugin vastai-packOrchestrates Vast.ai GPU instances for distributed training with spot preemption recovery and cost optimization across multiple machines.
Creates, edits, and optimizes skills for Claude Code, including drafting, evaluating with test prompts, iterating on performance, and improving skill descriptions for better triggering accuracy.