Cerebrium is a serverless GPU platform for deploying voice agents, video models and LLMs with sub-second cold starts and pay-per-second pricing, with no Kubernetes to manage 1. Its indexed action covers deploying Python code to serverless GPU or CPU and writing or fixing cerebrium.toml 1. If you want alternatives for serverless GPU inference, Runpod 2, Modal 6 and Runware 4 offer similar serverless GPU deployment.
- 1
Cerebriumcerebrium.aiverified and signed
Deploys Python code to serverless GPU or CPU infrastructure with sub-second cold starts, configured through cerebrium.toml, and offers an MCP server 1.
official:MCP server · Cerebrium Docs
for agents: cerebrium
- 2
Runpodrunpod.ioverified and signed
Runs serverless GPU and CPU endpoints where you write Python locally and run it on remote Runpod GPUs 2.
+10 community tools built for it
for agents: serverless-deploy · flash · runpod
- 3
Modalmodal.comverified and signed
Deploys and scales AI inference and training workloads on cloud containers with serverless GPUs 6.
+20 community tools built for it
for agents: serverless · infrastructure · cloud
- 4
Runwarerunware.aifound in code, not yet checked
Provides serverless GPUs and model APIs for running AI inference workloads 4.
+4 community tools built for it
for agents: videoInference · imageInference · modelSearch
Sources
- 1Serverless GPU Infrastructure for Real-Time AI |Â Cerebriumcerebrium.ai · read 2026-10-29
- 2The AI Developer Cloud | Runpodrunpod.io · read 2026-10-29
- 4Runware | AI Infrastructure Rebuilt to Cost Lessrunware.ai · read 2026-10-29
- 6Modal: The platform for production AImodal.com · read 2026-10-29
For agents: the 10 actions behind this answer
cerebrium · Cerebrium verified and signed
Use for any Cerebrium task: deploying Python code to serverless GPU or CPU, writing or fixing cerebrium.toml, choosing hardware and regions, calling deployed endpoints (REST, streaming, WebSocket, async), autoscaling and concurrency, cold starts, secrets, CI/CD, and debugging a build or a running ap
serverless-deploy · serverless-deploy verified and signed
Bring any source (a HuggingFace repo, a ComfyUI workflow, a Hub release, a custom container image, a GGUF repo, a Civitai asset, or a community template) to a proven Runpod Serverless endpoint: pick the engine for the modality and model format, size the GPU from the model's real metadata, provision,
inference · NVIDIA NIM for GenMol verified and signed
Task: Inference
videoInference · Runware MCP Server found in code, not yet checked
gke-inference · gke-inference verified and signed
Deploys and optimizes AI/ML inference workloads on GKE, using GPUs, TPUs, and model servers. Use when deploying GKE inference servers, configuring GKE GPU resources for inference, or deploying LLMs on GKE. Don't use for migrating existing AI workloads to GKE (use google-cloud-solution-guided-gke-ai-
serverless · modal verified and signed
get_compatible_platforms · dell-ai found in code, not yet checked
List platforms and GPU configurations compatible with a model.
list_models · LocalAI (Activepieces) verified and signed
List Models — List the models installed on your LocalAI server.
Deploy a model · AmpleRun verified and signed
Rent a GPU for a template (an inference server with pinned weights, or PyTorch/Jupyter) within a hard budget cap; get the endpoint or SSH access; stop it anytime. e.g. Deploy qwen3-8b-awq for two hours with a 5 USDC cap
radar-get-ai-inference-timeseries-group-by-model · Cloudflare API verified and signed
Get Workers AI models time series
Labels are the instruments' verdict and are never changed by ranking. Agents and robots get this same answer as JSON at /api/v1/find?q=…&answer=1 and through the MCP server.