Beam Cloud Free GPU Tier - 10 Hours Serverless GPU
Source: https://www.beam.cloud/
Description
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+ 1 Beam Cloud is an open-source serverless GPU platform (powered by the beta9 runtime) that gives you 10 free GPU compute hours on signup. No credit card required. The platform supports T4, A10G, RTX 4090, A100, and H100 GPUs with pay-per-second billing, cold starts of 2-3 seconds, and warm starts as fast as 50ms. Deploy AI models as serverless REST APIs, run async task queues, or launch Jupyter notebooks -- all with instant autoscaling and no idle costs.
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+ 7 1. Go to beam.cloud and click Get Started
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+ 8 2. Sign up with your email or GitHub account
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+ 9 3. No credit card is required -- you immediately get 10 hours of free GPU compute
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4. Install the Beam CLI: pip install beam-client (or curl https://raw.githubusercontent.com/beam-cloud/beta9/main/bin/install.sh -sSfL | bash)
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5. Authenticate with beam login and enter your API key from the dashboard
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6. Write your first function using the @endpoint decorator and deploy with beam deploy
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+ 14 Important:
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+ 15 • The free tier is limited to 3 concurrent applications
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+ 16 • Free credits are one-time; after exhausting them, you move to pay-per-second billing
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+ 17 • No upfront commitments or long-term contracts
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+ 23 GPU TypeVRAMApprox. Price/Hour
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+ 24 NVIDIA T416 GB$0.15
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+ 25 NVIDIA A10G24 GB$0.29 - $1.05
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+ 26 NVIDIA RTX 409024 GB$0.19 - $0.69
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+ 27 NVIDIA A100 (40-80GB)40-80 GB$0.63 - $3.50
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+ 28 NVIDIA H10080 GB$0.97 - $7.15
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+ 30 Pricing varies based on CPU, RAM, and GPU configuration. GPU charges are combined with separate CPU core ($0.19/core/hr) and RAM ($0.02/GB/hr) costs. All billed per-second.
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+ 36 ResourceLimit
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+ 37 Free GPU hours10 hours (one-time on signup)
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+ 38 Concurrent applicationsUp to 3
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+ 39 Cold start time2-3 seconds typical
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+ 40 Warm start time~50 ms
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+ 41 File storageIncluded free
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+ 42 Credit card requiredNo
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+ 43 Billing modelPay-per-second after free credits
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+ 49 • Serverless REST APIs: Deploy any Python function as an auto-scaling endpoint
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+ 50 • Task queues: Async job processing with automatic retries
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+ 51 • Scheduled jobs: Cron-style scheduling for recurring workloads
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+ 52 • Jupyter notebooks: GPU-powered notebook environments in the cloud
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+ 53 • Custom Docker images: Bring your own container for full environment control
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+ 54 • Fast cloud storage: Integrated storage for model weights and datasets
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+ 55 • Local debugging: Test locally, deploy to cloud with the same CLI
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+ 56 • Multi-region: Infrastructure in US, Europe, and Asia
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+ 57 • Multi-GPU support: Available by request via Slack
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+ 63 • Open-source self-hosting: Beam's core runtime (beta9) is fully open-source on GitHub. You can self-host it on your own GPU cluster for free, avoiding any usage limits entirely
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• GPU fallback ordering: You can specify multiple GPU types as a priority list (e.g., ["A10G", "T4"]), and Beam will allocate the first available GPU from your list
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+ 65 • No vendor lock-in: The same CLI and code works across Beam's managed cloud, your on-prem infrastructure, and hybrid setups
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+ 66 • Cold start optimization: Keep functions warm by setting a minimum replica count (costs money but eliminates cold starts for production workloads)
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+ 67 • Compare with Modal: Beam and Modal are direct competitors. Beam's advantage is open-source portability; Modal's advantage is deeper Python integration and a larger free tier ($30/month)
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+ 68 • Combine free tiers: Use Beam's 10 free hours for serverless API deployment and pair with Kaggle/Colab for interactive notebook training
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+ 72 Sources:
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+ 73 • Beam Cloud Official Site
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+ 74 • Beam Cloud Pricing
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+ 75 • Beam Cloud GPU Pricing - CloudGPUPrices
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+ 76 • Beam Cloud GPU Documentation
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+ 77 • Beta9 GitHub Repository
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+ 78 • Serverless GPU Platforms Comparison - Introl
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+ 79 • Top Serverless GPU Clouds 2026 - RunPod
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