Vast.ai

@vast.ai find me the 5 cheapest RTX 4090 offers with at least 24GB VRAM and 100GB disk space available right now

Found 5 RTX 4090 offers matching your criteria. The cheapest starts at $0.34/hour (offer #12847563) with 24GB VRAM, 120GB disk, located in US-West datacenter. Next cheapest is $0.38/hour (offer #12901442) with identical specs in EU-Central. All five offers are verified and currently online. Note these are snapshots — availability changes minute-by-minute as other users rent capacity.

This example shows the MCP's core search capability for GPU compute. The tool returns offer identifiers and pricing but does NOT rent the instance — you'll need to complete rental through Vast.ai's web dashboard or CLI. Offers can disappear between search and rental attempt during high-demand periods.

@vast.ai search for launch templates tagged 'pytorch' that support multi-GPU setups, show me the top 3 by popularity

Found 3 popular PyTorch templates supporting multi-GPU: 'pytorch-distributed-2.1' (template ID: pt_dist_v21) configured for 2-8 GPUs with NCCL, 'fastai-multigpu' (template ID: fastai_mg) optimized for vision workloads across 4+ GPUs, and 'huggingface-trainer' (template ID: hf_train) with DeepSpeed integration for 2-16 GPU training. Each template includes pre-configured CUDA drivers and framework-specific distributed libraries.

This demonstrates template discovery for pre-built environments. Templates save setup time but the MCP only searches metadata — it cannot inspect the actual Docker image contents or verify software versions. Always validate the template matches your framework version requirements before renting compute.

@vast.ai compare storage volume pricing across US and EU regions for 500GB capacity, then draft a cost estimate for a 3-month ML experiment storing checkpoints

US regions average $0.08/GB/month for 500GB volumes ($40/month), while EU regions run $0.10/GB/month ($50/month). For your 3-month checkpoint storage scenario: US-based volume would cost ~$120 total, EU-based ~$150 total. However, consider data egress costs if your compute instances are in a different region — transferring 500GB between regions could add $15-30 in bandwidth fees. Recommend co-locating storage and compute in the same region to avoid transfer costs.

This example pairs the MCP's volume search with AI reasoning to synthesize a cost analysis. The MCP provides raw pricing data but cannot predict your actual usage patterns or account for bandwidth between regions. Treat this as a planning estimate, not a billing guarantee — actual costs depend on your data transfer and snapshot behavior.

When Vast.ai wins for one-off model training budgets

A 3-person ML team needs to fine-tune a vision model but doesn't want to commit to a monthly GPU instance. They use the Search GPU Offers tool in Switchy to compare hourly rates across 40+ providers in real time, filtering by VRAM and CUDA version. The MCP returns compact pricing data fast enough to check during a standup call. This works when you train sporadically—maybe twice a month—and your runs finish in under 48 hours. If you're training daily or need guaranteed availability, a reserved instance beats spot-market hunting. The API key setup takes two minutes; after that, your team can price-check without leaving the chat thread.

When this MCP helps scope GPU-heavy client work

A 5-person agency pitching a computer vision project needs to estimate compute costs before signing the contract. They use Search GPU Offers to pull current A100 and H100 rates, then Search Templates to see what launch configs other teams use for similar workloads. The tool returns enough detail to build a line-item budget without opening the Vast.ai dashboard. This scenario works because the search happens once per proposal, not once per day. If your team runs continuous inference or needs SLA guarantees, Vast.ai's spot market isn't the right fit and this MCP won't change that. For pre-sales scoping, though, it cuts 20 minutes of tab-switching into a single Switchy query.

When Search Volume Offers clarifies storage trade-offs

A 2-person research team has 800GB of training data and wants to compare Vast.ai volume pricing against S3 before committing to a storage strategy. They use Search Volume Offers in Switchy to see per-GB rates across regions, then cross-reference egress costs in the same thread. The MCP returns location and capacity details but doesn't provision anything, so the team can debate options without accidentally spinning up infrastructure. This works when you're in the planning phase and storage decisions happen quarterly, not hourly. If you're already locked into a cloud provider's ecosystem, this MCP won't justify the context switch. For teams evaluating decentralized storage as a cost hedge, it surfaces the data you need to make the call.