Cerebrium Free Serverless GPU Credits - $10 Free + Up to $1,000 for Startups

Free GPU & Compute | Amount: $10 free GPU credits (up to $1,000 for qualified startups). 9 GPU types from T4 ($0.59/hr) to H200 ($3.30/hr). | AI-generated | 1/5 InstantSignup and get credits instantly — no credit card, no approval active
2026-02-26
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Source: https://www.cerebrium.ai/

Description

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+ 1 Cerebrium is a serverless AI infrastructure platform (YC-backed) that gives new accounts $10 in free GPU credits with access to 9 GPU types from T4 to H200. The platform features pay-per-second billing with no idle costs, cold starts as low as 2-4 seconds, and automatic scaling from zero to thousands of requests. Qualified startups can apply for up to $1,000 in credits with dedicated engineering support. No credit card required for the free Hobby tier.

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+ 2  

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+ 3

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+ 4  

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+ 5 Registration (Step-by-Step)

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+ 6  

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+ 7 1. Go to cerebrium.ai and click Get Started

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+ 8 2. Create an account with your email

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+ 9 3. You'll immediately receive $10 in free GPU credits on the Hobby plan

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+ 10 4. Install the CLI: pip install cerebrium

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+ 11 5. Authenticate: cerebrium login and enter your API key

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+ 12 6. Initialize a project: cerebrium init my-project

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+ 13 7. Configure GPU in cerebrium.toml under [cerebrium.hardware] and deploy with cerebrium deploy

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+ 14  

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+ 15 For startup credits ($1,000):

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+ 16 • Apply through Cerebrium's startup program on their website

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+ 17 • Includes face-time with Cerebrium engineers for onboarding

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+ 18 • Must be a qualified startup (criteria not publicly detailed)

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+ 19  

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+ 20

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+ 21  

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+ 22 GPU Hardware & Specs

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+ 23  

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+ 24 GPU ModelIdentifierVRAMPrice/HourPlan Required

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+ 25 NVIDIA T4TURING_T416 GB$0.59Hobby+

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+ 26 NVIDIA L4ADA_L424 GB$0.80Hobby+

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+ 27 NVIDIA A10AMPERE_A1024 GB$1.10Hobby+

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+ 28 NVIDIA A100 (40GB)AMPERE_A100_40GB40 GB$1.45Enterprise

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+ 29 NVIDIA L40SADA_L4048 GB$1.95Hobby+

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+ 30 NVIDIA A100 (80GB)AMPERE_A100_80GB80 GB$2.06Enterprise

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+ 31 NVIDIA H100HOPPER_H10080 GB$2.21Enterprise

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+ 32 NVIDIA H200141 GB$3.30Enterprise

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+ 33 AWS Inferentia 2INF232 GBHobby+

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+ 34 AWS TrainiumTRN132 GBHobby+

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+ 35  

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+ 36 All GPUs support up to 8x multi-GPU configurations for distributed workloads.

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+ 37  

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+ 38

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+ 39  

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+ 40 Free Tier Limits

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+ 41  

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+ 42 ResourceLimit

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+ 43 Free credits$10 (new accounts)

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+ 44 Startup creditsUp to $1,000 (qualified startups)

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+ 45 Hobby plan cost$0/month

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+ 46 GPUs on Hobby planT4, L4, A10, L40S, Inferentia, Trainium

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+ 47 GPUs requiring EnterpriseA100, H100, H200

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+ 48 Block storage (free)First 100 GB included

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+ 49 Additional storage$5/month per additional block

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+ 50 Cold start time2-4 seconds typical

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+ 51 Billing modelPay-per-second, no idle costs

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+ 52 Credit card requiredNo (Hobby plan)

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+ 53  

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+ 54

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+ 55  

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+ 56 Plan Tiers

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+ 57  

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+ 58 FeatureHobby ($0/mo)Standard ($100/mo)Enterprise (Custom)

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+ 59 Free credits$10Up to $1,000

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+ 60 GPU accessT4, L4, A10, L40SAll standard GPUsAll GPUs including H100, A100, H200

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+ 61 Multi-GPUUp to 8xUp to 8xUp to 8x

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+ 62 Storage100 GB freeIncludedCustom

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+ 63 SupportCommunityPriorityDedicated engineering

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+ 64  

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+ 65

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+ 66  

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+ 67 Configuration Example

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+ 68  

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+ 69

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+ 70 cerebrium.toml

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+ 71 [cerebrium.hardware]

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+ 72 compute = "AMPERE_A10"

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+ 73 gpu_count = 1

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+ 74 cpu = 4

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+ 75 memory = 16

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+ 76

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+ 77  

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+ 78 Or via CLI:

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+ 79

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+ 80 cerebrium deploy --compute AMPERE_A10 --gpu-count 1

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+ 81

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+ 82  

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+ 83

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+ 84  

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+ 85 Additional Tips

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+ 86  

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+ 87 $10 goes further than you think: At T4 pricing ($0.59/hour billed per-second), $10 buys about 17 hours of GPU compute. If your inference calls take 5 seconds each, that's roughly 12,000 API calls

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+ 88 Hobby plan is genuinely useful: Unlike many "free tiers" that are crippled, the Hobby plan gives you access to solid GPUs (T4, L4, A10, L40S) and 100 GB storage

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+ 89 Cold starts are fast: 2-4 second cold starts are competitive with Modal and faster than most alternatives. Code at the top level of your module runs only on first container spin-up

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+ 90 VRAM calculation: Use the formula modelVRAM = parameters x bytes_per_dtype x 1.5 to estimate GPU requirements. The 1.5x buffer accounts for runtime overhead

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+ 91 Multi-GPU for large models: You can run up to 8 GPUs in a single deployment, making it possible to serve 70B+ parameter models with tensor parallelism

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+ 92 YC-backed: Cerebrium is a Y Combinator company, which may give you confidence in platform longevity and may help if you're also a YC company

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+ 93 Mistral integration: Cerebrium has official Mistral AI deployment guides, making it easy to self-host Mistral models

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+ 94 Compare with Modal: Modal offers $30/month in free credits vs Cerebrium's one-time $10. But Cerebrium's per-second GPU pricing is often cheaper for sustained workloads

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+ 95 AWS chip support: Cerebrium uniquely offers AWS Inferentia 2 and Trainium chips on the Hobby plan, which can be significantly cheaper for inference and training respectively

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+ 96  

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+ 97

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+ 98  

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+ 99 Sources:

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+ 100 Cerebrium Official Site

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+ 101 Cerebrium Pricing

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