# Free access to GPU runtime **URL:** \ **Category:** Discussion & Solutions\ **Tags:** solved\ **Created:** [September 16, 2026, 11:10am UTC](https://onehack.st/t/free-access-to-gpu-runtime/325636 "2026-09-16T11:10:49Z")\ **Posts on this page:** 4\ **Page:** 1
**Author:** ![Nity](https://onehack.st/user_avatar/onehack.st/nity/32/178023_2.png) [@Nity](https://onehack.st/u/Nity)\ **Post date:** [September 16, 2026, 11:10am UTC](https://onehack.st/t/free-access-to-gpu-runtime/325636/1 "2026-09-16T11:10:49Z")
Can anyone help me find a website where I can get access to free GPU for AI or other things without needing a credit card? I have tried many of them already but each of them limit is to 24 GB almost, so big models canโ€™t be load in it. [modal.com](http://modal.com) , lightning.ai , google-colab , kaggle , saga (by Amazon) โ† i have tried all of these but only get atmost 24 GB of VRAM by modal L4 GPU and 30 GB of VRAM from kaggle dual T4 GPU ---
**Author:** ![SRZ](https://onehack.st/user_avatar/onehack.st/srz/32/146132_2.png) [@SRZ](https://onehack.st/u/SRZ)\ **Post date:** [September 16, 2026, 4:18pm UTC](https://onehack.st/t/free-access-to-gpu-runtime/325636/2 "2026-09-16T16:18:41Z")
**Free graphics cards past 24GB with no credit card** ๐Ÿ–ฅ **Bigger cards, zero card โ€” free graphics memory past the 24GB wall, every step clickable.** **โฑ Tonight, 2 minutes, no signup** - **ZeroGPU Spaces** โ€” duplicate a Space, type a prompt: 48GB card, 96 in xlarge, ~5 free minutes a day. ๐Ÿ”— [docs](https://huggingface.co/docs/hub/spaces-zerogpu) ยท [space list](https://huggingface.co/spaces/enzostvs/zero-gpu-spaces) - **LMArena** โ€” type a prompt: frontier models on datacenter cards. ๐Ÿ”— [arena.ai](https://arena.ai/) - **GMI Cloud** โ€” H100/H200 70B models in the Playground. ๐Ÿ”— [developers](https://www.gmicloud.ai/en/developers) - **AI Horde** โ€” anonymous art and text on volunteersโ€™ cards. ๐Ÿ”— [mission](https://stablehorde.net/mission) ยท [ArtBot](https://artbot.site/) **๐Ÿ“ง Email, 5 minutes** - **Anyscale** โ€” $100 free credit: ~20h A100-80, ~10h H100. ๐Ÿ”— [pricing](https://www.anyscale.com/pricing) - **Baseten** โ€” $30 free credit: ~7h A100-80. ๐Ÿ”— [pricing](https://www.baseten.co/pricing) - **Beam** โ€” A100-80 around 13โ€“20h from a $30 refresh. ๐Ÿ”— [pricing](https://www.beam.cloud/pricing) - **Saturn Cloud** โ€” 150 compute hours monthly with a card-free GPU. ๐Ÿ”— [try](https://saturncloud.io/try/) - **AIgateway** โ€” team email on the page, $500 code within an hour. ๐Ÿ”— [hackathons](https://aigateway.sh/hackathons) **๐Ÿ“ฑ Phone check, 10 minutes** - **Lightning AI** โ€” up to 80 free GPU hours: A100-80, H200-141, RTX 6000-96. ๐Ÿ”— [pricing](https://lightning.ai/pricing) - **Kaggle** โ€” join a competition notebook, pick 96GB or H100 shapes; T4x2 baseline 30h a week. ๐Ÿ”— [efficient-gpu](https://www.kaggle.com/docs/efficient-gpu-usage) - **Modal** โ€” $30 monthly refresh: A100-80 ~12h, H100 ~7.6h. ๐Ÿ”— [signup](https://modal.com/signup) - **Hugging Face** โ€” a 30-day-old free account hosts 2 Spaces, 48โ€“96GB. **๐Ÿงพ One web form, short wait** - **AMD Cloud** โ€” $100 on MI300X 192GB (~25h+), 30 days to use. ๐Ÿ”— [cloud-access](https://www.amd.com/en/developer/resources/cloud-access/amd-developer-cloud.html) ยท [member-perks](https://developer.amd.com/member-perks/) - **Zylora** โ€” $500 H100, rolling 48h review. ๐Ÿ”— [free-gpu-credits](https://zylora.dev/free-gpu-credits/) - **LaunchPad** โ€” 2โ€“4h A100/H100/H200 labs. ๐Ÿ”— [nvidialaunchpad](https://www.nvidia.com/en-us/launchpad/ai) - **TRC** โ€” free TPU clusters, open signup. ๐Ÿ”— [sites.research.google/trc](https://sites.research.google/trc/) - **TensorPool** โ€” $20 after a star, Discord, and a screenshot. ๐Ÿ”— [free-credits](https://tensorpool.dev/free-credits) - **Hackathons** โ€” emma (H100/H200/B300, solo-friendly) ๐Ÿ”— [emma](https://hackathon.emma.ms/) ยท gpu.ai (50 teams) ๐Ÿ”— [gpu.ai](https://gpu.ai/buildathon) ยท Featherless (+$25 code IMPACT26) ๐Ÿ”— [featherless](https://featherless.ai/hackathon-grant) ยท Nosana $70 ๐Ÿ”— [decentralizeai](https://decentralizeai.tech/claim/nosana) - **Institution labs** โ€” school/employer portal GPU time; public-library AI kiosks. ๐Ÿ”— [ncshare guide](https://userguide.ncshare.org/guides/gpu/) ยท [libraries](https://www.publicai.network/libraries.html) **๐ŸŽ“ No student status?** - **UoPeople** โ€” $60 app, online from anywhere. ๐Ÿ”— [become-student](https://www.uopeople.edu/become-student/) - **ASU courses** โ€” $25, records verifiers accept. ๐Ÿ”— [how-to-enroll](https://ulc.asu.edu/how-to-enroll/) - **Foothill** โ€” ~$75, .edu mail. ๐Ÿ”— [how-to-apply](https://foothill.edu/international/prospective/admission-requirements/how-to-apply.html) - **Azure Students** โ€” .edu โ†’ $100 credit, no card. ๐Ÿ”— [azure students](https://azure.microsoft.com/en-us/free/students) **๐Ÿ” Keep a card under you (a paper calendar works)** Kaggle resets Sunday 00:00 UTC ยท Colab best early-morning UTC ยท Baidu 8/day (needs a Chinese ID) ยท ZeroGPU 5/day ยท ModelScope 36h one-off A10. ๐Ÿ”— [colab](https://colab.research.google.com/) ยท [modelscope](https://modelscope.cn/brand/view/GPU) ยท [baidu doc](https://ai.baidu.com/ai-doc/AISTUDIO/nk39v9kec) **๐Ÿงฎ What actually fits โ€” the number that changes the plan** You do not need an 80GB card. You need the right quantisation: ```auto 24 GB card โ†’ a 32B model at Q4 (~20 GB) fits, with room for context 2 ร— 16 GB โ†’ a 70B at Q4 (~40 GB) fits, split across both cards 48 GB โ†’ a 70B at Q5 or Q6, or a 120B at Q4 96 GB โ†’ a 120B at Q5, or a 235B MoE at Q4 ``` llama.cpp stays faster than VRAM when the model spills, so **fit first and split second** โ€” the official [multi-GPU doc](https://github.com/ggml-org/llama.cpp/blob/master/docs/multi-gpu.md) says exactly when to reach for it: the model does not fit, or you want throughput. **๐Ÿงฉ Run a big model without owning a big card** - **[Petals](https://github.com/bigscience-workshop/petals)** โ€” a public swarm. You load a slice, strangers serve the rest: **Llama 70B at ~6 tokens/sec, Falcon 180B at ~4** , on your laptop, over the internet, free - **[exo](https://github.com/exo-explore/exo)** โ€” mesh your own devices: phone, laptop, PC and a spare Mac become one machine, tinygrad under the hood - **[pooled.run](https://pooled.run)** โ€” the wild one: every device runs its share **inside a browser tab** , so the โ€œclusterโ€ is a page you leave open - **[llama.cpp `--split-mode`](https://github.com/ggml-org/llama.cpp/blob/master/docs/multi-gpu.md)** โ€” the honest single-machine answer when you have two small cards instead of one big one - **[Petals vs Exo vs Kalavai vs SharedLLM, compared in 2026](https://sharedllm.org/blog/sharedllm-vs-petals-vs-exo.html)** โ€” licence, network model and privacy claim in one table **โฑ Make a free hour behave like a real hour** - **Checkpoint, do not finish.** Free tiers are cut mid-run. Save every N steps and resume; a 4-hour run becomes four 1-hour runs - **Quantise the weights, not the ambition.** Q4 halves the load time and lets a card that โ€œwas too smallโ€ fit - **Batch everything you can.** One session with 40 prompts beats 40 sessions with a log-on each - **Keep the model warm** โ€” a session that re-downloads weights has already spent your allowance - **Pick the shape before you queue** : Kaggle resets Sunday 00:00 UTC, Colab is best early-morning UTC, ZeroGPU is ~5 minutes a day. The calendar in the block above is the whole strategy **โšก Start:** ZeroGPU duplicate tonight โ†’ email credits tomorrow โ†’ phone hours next โ†’ one form. โ†’ big model, zero card. ---
**Author:** ![system](https://onehack.st/user_avatar/onehack.st/system/32/165705_2.png) [@system](https://onehack.st/u/system)\ **Post date:** [Oktober 23, 2026, 2:36am UTC](https://onehack.st/t/free-access-to-gpu-runtime/325636/3 "2026-10-23T02:36:20Z")
> [@SRZ](#): > > Free graphics cards past 24GB with no credit card ๐Ÿ–ฅ Bigger cards, zero card โ€” free graphics memory past the 24GB wall, every step clโ€ฆ **๐Ÿ›  The reply above is now a better version, fixed up by the Core-Community AI.** ---
**Author:** ![Grace2077](https://onehack.st/user_avatar/onehack.st/grace2077/32/179796_2.png) [@Grace2077](https://onehack.st/u/Grace2077)\ **Post date:** [Oktober 23, 2026, 2:33pm UTC](https://onehack.st/t/free-access-to-gpu-runtime/325636/4 "2026-10-23T14:33:45Z")
One more angle worth trying: if the goal is inference rather than training, you may not need a big card at all. Quantized 32B models run well on a single 24GB GPU at Q4, and llama.cpp can offload layers to CPU or even split across two smaller GPUs when VRAM is tight. For training, Google Colab Proโ€™s free tier combined with gradient checkpointing and LoRA can handle surprisingly large fine-tunes if you checkpoint aggressively and resume after the session resets. Also check whether your school or employer has an institutional cluster โ€” many universities give students free GPU hours through a portal most people never discover. Finally, set up idle alerts: free tiers often free up overnight, so queuing jobs during off-peak hours dramatically improves your hit rate.