Free access to GPU runtime

Discussion & Solutions
1

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 can’t be load in it.

modal.com , lightning.ai , google-colab , kaggle , saga (by Amazon) ← 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

2

Free graphics cards past 24GB with no credit card

:desktop_computer: Bigger cards, zero card — free graphics memory past the 24GB wall, every step clickable.

:stopwatch: Tonight, 2 minutes, no signup

  • ZeroGPU Spaces — duplicate a Space, type a prompt: 48GB card, 96 in xlarge, ~5 free minutes a day. :link: docs · space list
  • LMArena — type a prompt: frontier models on datacenter cards. :link: arena.ai
  • GMI Cloud — H100/H200 70B models in the Playground. :link: developers
  • AI Horde — anonymous art and text on volunteers’ cards. :link: mission · ArtBot

:e_mail: Email, 5 minutes

  • Anyscale — $100 free credit: ~20h A100-80, ~10h H100. :link: pricing
  • Baseten — $30 free credit: ~7h A100-80. :link: pricing
  • Beam — A100-80 around 13–20h from a $30 refresh. :link: pricing
  • Saturn Cloud — 150 compute hours monthly with a card-free GPU. :link: try
  • AIgateway — team email on the page, $500 code within an hour. :link: hackathons

:mobile_phone: Phone check, 10 minutes

  • Lightning AI — up to 80 free GPU hours: A100-80, H200-141, RTX 6000-96. :link: pricing
  • Kaggle — join a competition notebook, pick 96GB or H100 shapes; T4x2 baseline 30h a week. :link: efficient-gpu
  • Modal — $30 monthly refresh: A100-80 ~12h, H100 ~7.6h. :link: signup
  • Hugging Face — a 30-day-old free account hosts 2 Spaces, 48–96GB.

:receipt: One web form, short wait

:graduation_cap: No student status?

:repeat_button: Keep a card under you (a paper calendar works)
Kaggle resets Sunday 00:00 UTC · Colab best early-morning UTC · Baidu 8/day (needs a Chinese ID) · ZeroGPU 5/day · ModelScope 36h one-off A10. :link: colab · modelscope · baidu doc

:abacus: What actually fits — the number that changes the plan

You do not need an 80GB card. You need the right quantisation:

24 GB card   →  a 32B model at Q4  (~20 GB)  fits, with room for context
2 × 16 GB    →  a 70B at Q4 (~40 GB)  fits, split across both cards
48 GB        →  a 70B at Q5 or Q6, or a 120B at Q4
96 GB        →  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 — the official multi-GPU doc says exactly when to reach for it: the model does not fit, or you want throughput.

:puzzle_piece: Run a big model without owning a big card

  • Petals — 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 — mesh your own devices: phone, laptop, PC and a spare Mac become one machine, tinygrad under the hood
  • pooled.run — the wild one: every device runs its share inside a browser tab, so the “cluster” is a page you leave open
  • llama.cpp --split-mode — 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 — licence, network model and privacy claim in one table

:stopwatch: 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 “was too small” fit
  • Batch everything you can. One session with 40 prompts beats 40 sessions with a log-on each
  • Keep the model warm — 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

:high_voltage: Start: ZeroGPU duplicate tonight → email credits tomorrow → phone hours next → one form. → big model, zero card.

3

:hammer_and_wrench: The reply above is now a better version, fixed up by the Core-Community AI.

4

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 Pro’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 — 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.