Alternatives
Products that do what Recurser lib reduces GPT2-XL VRAM usage by 25% and runs it on Colab does
- 1RD
2016 · github.com
- 2L3
Hi everyone, I'm kinda involved in some retrogaming and with some experiments I ran into the following question: "It would be possible to run transformer models bypassing the cpu/ram, connecting the gpu to the nvme?" This is the result of that question itself and some weekend vibecoding (it has the linked library repository in the readme as well), it seems to work, even on consumer gpus, it should work better on professional ones tho
Feb 2026 · github.com
- 3AS
2019 · github.com
- 4EP
2017 · instaguide.io
- 51T
2020 · youtube.com
- 6FT
Aug 2026 · github.com
- 7

- 8AM
2017 · github.com
- 9AL
2019 · github.com
- 10GA
Sep 2025 · github.com
- 11RG
2025 · github.com
- 12OS
2018 · github.com
- 13GPGPU PaaS▲6
2021 · calcify.io
- 14AE
2016 · github.com
- 15OI
Nov 2025 · github.com
- 163M
2020 · qvault.io
- 17GB
2016 · paperspace.com
- 18GK
Sep 2025 · github.com
- 19DA
Jun 2026 · github.com
- 20OS
Posted before, but wanted to share if you want an open source alternative to OpenAI fine-tuning, give Unsloth a try! Phi 3.5 was just released, and is distilled from GPT4. Unsloth makes finetuning 2x faster, uses 70% less VRAM + has no accuracy degradations. We rewrite all backprop steps and reduce FLOPs and write everything in Triton (JIT low level CUDA). If you want to own the weights after fine-tuning, give Unsloth a spin! I have free Colabs and Kaggle notebooks as well at https://github.com/unslothai/unsloth
2024 · colab.research.google.com
- 21RF
2017 · github.com
- 22NG
Jun 2026 · github.com
- 23ZA
Jun 2026 · github.com
- 24VV
2021 · vktracer.com
Ranked by how close each launch is in meaning, then by votes. Refine with a description →