Paperspace Gradient Free Tier (by DigitalOcean)
Source: https://www.paperspace.com/pricing
Description
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+ 1 Paperspace Gradient (now part of DigitalOcean) offers free GPU-powered Jupyter notebooks for machine learning experimentation. The free tier includes access to NVIDIA GPUs — the reliably available Quadro M4000 (8 GB VRAM) plus nominally free Ampere GPUs (A4000, A5000, A6000) that are heavily constrained by availability. You get 5 GB of persistent storage, 6-hour session limits, and unlimited session restarts. No weekly GPU hour cap. Credit card required for account verification.
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Reality check: While Paperspace advertises free access to high-end Ampere GPUs, in practice the M4000 is the GPU you will reliably get. Free A4000/A5000/A6000 instances exist but are frequently unavailable due to demand. The A100 is not free — it requires a paid plan plus $3.09/hr. Treat this as a free M4000 notebook platform with occasional access to better hardware.
Reality check: While Paperspace advertises free access to high-end Ampere GPUs, in practice the M4000 is the GPU you will reliably get. Free A4000/A5000/A6000 instances exist but are frequently unavailable due to demand. The A100 is not free — it requires a paid plan plus $3.09/hr. Treat this as a free M4000 notebook platform with occasional access to better hardware.
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+ 9 1. Go to paperspace.com and click Sign Up
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+ 10 2. Create an account with email or sign in via Google/GitHub
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+ 11 3. Enter a valid credit card for verification (required even for the free tier; Paperspace uses Stripe and accepts Visa, MasterCard, Amex, Discover)
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+ 12 4. Navigate to Gradient > Notebooks from the dashboard
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+ 13 5. Click Create Notebook, choose a runtime template (e.g., PyTorch, TensorFlow, or Start from Scratch)
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+ 14 6. Under Machine, select one of the Free GPU/CPU options (Free-GPU M4000, Free-CPU C4, or check if Free-A4000/A5000/A6000 are available)
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+ 15 7. Click Start Notebook — your Jupyter environment launches in-browser
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+ 21 InstanceGPUVRAMCPU CoresRAMAvailability
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+ 22 Free-GPU (M4000)NVIDIA Quadro M40008 GB88 GBReliably available
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+ 23 Free-CPU (C4)None (CPU only)—88 GBReliably available
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+ 24 Free-P4000NVIDIA Quadro P40008 GB88 GBLimited
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+ 25 Free-P5000NVIDIA Quadro P500016 GB816 GBLimited
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+ 26 Free-RTX4000NVIDIA RTX40008 GB88 GBLimited
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+ 27 Free-RTX5000NVIDIA RTX500016 GB816 GBLimited
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+ 28 Free-A4000NVIDIA RTX A400016 GB845 GBRarely available
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+ 29 Free-A5000NVIDIA RTX A500024 GB845 GBRarely available
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+ 30 Free-A6000NVIDIA RTX A600048 GB845 GBRarely available
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+ 31 Free-A100-80GNVIDIA A10080 GB1290 GBRarely available
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+ 33 The M4000 uses the older Maxwell architecture. For reference, it benchmarks roughly 16x slower than an A100 on transformer workloads like BERT. The M4000 is comparable to or slightly slower than Google Colab's free T4.
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+ 39 • 6-hour auto-shutdown — each session automatically terminates after 6 hours. You can restart immediately and run unlimited sessions
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+ 40 • 1 concurrent notebook — free tier allows only one running notebook at a time
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+ 41 • 5 GB persistent storage — persists between sessions. Overages cost $0.29/GB/month
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+ 42 • Up to 5 projects — free tier limits you to 5 Gradient projects
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+ 43 • Public notebooks only — free tier notebooks are publicly visible. Do not put API keys, credentials, or proprietary code in free notebooks
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+ 44 • No terminal access — free tier does not include notebook terminal/shell access
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+ 45 • GPU availability not guaranteed — free instances are shared. If demand is high, you may be queued or unable to start a session, especially for Ampere GPUs
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+ 46 • No Deployments or Workflows — free machines can only be used for Notebooks, not for Gradient Deployments or Workflows
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+ 52 FeatureFree ($0/mo)Pro ($8/mo)Growth ($39/mo)
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+ 53 Storage5 GB15 GB50 GB
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+ 54 ProjectsPublic onlyPrivatePrivate
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+ 55 Auto-shutdown6 hours (fixed)12 hours (configurable)12 hours (configurable)
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+ 56 Free GPUsM4000 + limited othersP4000, P5000, RTX4000, RTX5000All free-tier GPUs + priority
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+ 57 Concurrent notebooks1MultipleMultiple
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+ 58 Terminal accessNoYesYes
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+ 59 SupportStandardStandardExpert
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+ 60
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+ 61 Paid on-demand GPU pricing (in addition to subscription):
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+ 63 GPUHourly Rate
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+ 64 NVIDIA T4~$0.45/hr
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+ 65 NVIDIA A4000~$0.76/hr
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+ 66 NVIDIA A100 (40 GB)$3.09/hr
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+ 67 NVIDIA A100 (80 GB)$3.18/hr
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+ 68 NVIDIA H100$5.95/hr
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+ 70 Multi-GPU configurations (2x, 4x, 8x) are available for A4000, A5000, A6000, and A100.
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+ 76 RegionCodeLocation
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+ 77 US-EastNY2New York, USA
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+ 78 US-WestCA1California, USA
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+ 79 Europe-WestAMS1Amsterdam, Netherlands
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+ 81 Free GPU availability varies by region and time of day. A100 and H100 instances are concentrated in NY2.
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+ 87 FeaturePaperspace FreeGoogle Colab FreeKaggle Notebooks
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+ 88 GPUM4000 (8 GB)T4 (15 GB)T4 (15 GB) or P100
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+ 89 Session limit6 hours~12 hours (variable)12 hours
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+ 90 Weekly GPU limitNone~12 hours (soft)30 hours
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+ 91 Storage5 GB persistentLimited (Google Drive)20 GB
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+ 92 CPU cores822
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+ 93 PrivacyPublic onlyPrivatePrivate
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+ 94 Idle timeoutNone (runs full 6 hrs)~90 min idle disconnectNone
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+ 95
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+ 96 Key advantage: No weekly GPU hour cap and no idle disconnects. Key disadvantage: Weaker GPU (M4000 vs T4) and notebooks are public.
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+ 102 • Use environment variables carefully — free notebooks are public, so never hardcode API keys. Use Gradient Secrets if available on your plan, or pass credentials at runtime
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+ 103 • Save checkpoints to persistent storage — your /storage directory (5 GB) persists between sessions. Save model checkpoints there to avoid losing work at the 6-hour shutdown
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+ 104 • Check GPU availability before starting — in the notebook creation screen, unavailable free GPUs will be grayed out. If the M4000 is also unavailable, try a different region or time of day
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+ 105 • Pre-built templates save time — Paperspace offers templates for PyTorch, TensorFlow, Hugging Face, Stable Diffusion, and fast.ai that come pre-configured with CUDA and dependencies
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+ 106 • Consider Colab for better GPU — if you need a T4 (stronger than M4000) and can tolerate idle disconnects, Google Colab's free tier may be a better fit for GPU-intensive work
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+ 107 • Upgrade path — the Pro plan at $8/month is reasonable if you need private notebooks and access to P4000/P5000/RTX4000 GPUs at no extra hourly cost
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+ 108 • DigitalOcean integration — since Paperspace was acquired by DigitalOcean, billing is handled through DigitalOcean. Existing DigitalOcean customers can access Paperspace through their DO account
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+ 109 • Referral credits — Paperspace occasionally offers referral credits ($10-$15) that can be applied toward paid GPU usage
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+ 113 Sources:
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+ 114 • Paperspace Pricing
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+ 115 • DigitalOcean Paperspace Pricing Documentation
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+ 116 • Gradient Free GPU Benchmarks
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+ 117 • Gradient vs Kaggle Comparison
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+ 118 • Gradient Community Notebook Guide
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+ 119 • Free Instances Documentation (GitHub)
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+ 120 • Paperspace Datacenter Regions
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+ 121 • Paperspace Free GPU Launch Announcement
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