Recherche GPU sur Vast.ai

VérifiéSûr

Recherche des machines GPU disponibles sur Vast.ai selon des critères précis (modèle, VRAM, budget, région). Aide à comparer les offres et à sélectionner la machine la plus adaptée aux besoins de location de GPU.

Spar Skills Guide Bot
Data & IAIntermédiaire
5002/06/2026
Claude Code
#gpu#vast-ai#cloud-computing#machine-learning#pricing

Recommandé pour

Notre avis

Recherche des offres de GPU sur Vast.ai pour la location de machines adaptées à des besoins spécifiques (modèle de GPU, VRAM, budget, région).

Points forts

  • Accès à un large inventaire de GPU (RTX 4090, A100, H100, etc.)
  • Filtres précis (VRAM, nombre de GPU, prix, fiabilité, localisation)
  • Comparaison rapide des prix on-demand, spot et réservés

Limites

  • Nécessite la connaissance des paramètres de requête spécifiques à Vast.ai
  • Les résultats dépendent de la disponibilité en temps réel des offres
  • Ne gère pas la réservation ni le lancement d'instances
Quand l'utiliser

Lorsque vous devez trouver rapidement le meilleur GPU louable pour un projet d'apprentissage automatique ou de calcul intensif.

Quand l'éviter

Lorsque vous cherchez à acheter du matériel GPU ou à utiliser des services cloud gérés comme AWS SageMaker.

Analyse de sécurité

Sûr
Score qualité90/100

The skill uses Bash only to run vastai search offers with a query constructed from a predefined mapping of user requirements to query fields. No raw user input is directly injected into shell commands, and no destructive or exfiltrating actions are instructed.

Aucun point d'attention détecté

Exemples

Cheap single RTX 4090
Find a single RTX 4090 GPU for under $1 per hour with good reliability.
8x H100 for large training
Search for 8 H100 GPUs in a datacenter for large model training.
Budget multi-GPU with at least 24GB VRAM
I need at least 2 GPUs with at least 24GB VRAM each, total cost under $1.50/hr.

name: search-gpus description: "Search for available GPU machines on Vast.ai. Use when looking for GPUs to rent, comparing GPU pricing, or finding machines that match specific requirements." argument-hint: "[gpu-type or requirements]" allowed-tools: Bash

Search Vast.ai GPU Offers

Help the user find the best GPU offers on Vast.ai.

User Request

$ARGUMENTS

Instructions

  1. Understand requirements: Key dimensions to determine:

    • GPU model (RTX 4090, A100, H100, etc.)
    • Number of GPUs
    • VRAM requirements
    • Budget ($/hr)
    • Region preferences
    • Reliability needs
    • Pricing type (on-demand vs spot/interruptible vs reserved)
  2. Build the search query from the mapping below.

  3. Run the search and present results clearly.

  4. Help interpret: Highlight best options based on user priorities.

Query Construction

| User says | Query field | |-----------|-------------| | "4090", "RTX 4090" | gpu_name=RTX_4090 | | "A100 80GB" | gpu_name=A100 gpu_ram>=80 | | "H100" | gpu_name=H100 | | "4 GPUs", "multi-GPU" | num_gpus>=4 | | "at least 48GB VRAM" | gpu_total_ram>=48 | | "under $1/hr", "cheap" | dph_total<1.0 | | "reliable" | reliability>0.95 | | "US only" | geolocation=US | | "datacenter" | datacenter=true | | "spot", "interruptible" | add -b flag | | "reserved" | add -r flag | | "NVLink" | bw_nvlink>0 | | "fast internet" | inet_down>500 |

Always include reliability>0.9 unless the user wants cheaper unreliable machines.

Default sort: -o 'dph_total' (cheapest first).

Examples

# Cheap single 4090
vastai search offers 'gpu_name=RTX_4090 num_gpus=1 reliability>0.9' -o 'dph_total'

# 8x H100 for large training
vastai search offers 'gpu_name=H100 num_gpus=8 reliability>0.95' -o 'dph_total'

# Any GPU with 80GB+ VRAM in US datacenters
vastai search offers 'gpu_total_ram>=80 geolocation=US datacenter=true reliability>0.95' -o 'dph_total'

# Budget multi-GPU fine-tuning
vastai search offers 'num_gpus>=2 gpu_ram>=24 dph_total<1.5 reliability>0.9' -o 'dph_total'

# Spot pricing for H100s
vastai search offers 'gpu_name=H100 num_gpus>=4' -b -o 'dph_total'

Presenting Results

After running the search:

  1. Summarize top 3-5 options in a clear table with: Offer ID, GPU, # GPUs, VRAM, $/hr, Reliability, Location
  2. Highlight the offer ID prominently — user needs it to launch
  3. Note trade-offs (price vs reliability vs perf)
  4. Mention spot pricing if it would save significantly
  5. If no results, suggest relaxing constraints
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