Kolosal AI

Kolosal AI

Product Hunt

Train and run LLMs on your device

Share card

Actual performance

159upvotes
8comments
Made the leaderboard

Traction signals

Makers1

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos
80%80% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
BetaListSuited for BetaList early-adopters · Missing: web3, chat, crypto
78%78% predicted probability of success on BetaList, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
57%57% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released
39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Correct prediction on native model

Similar products

An
AnyModal – Train Your Own Multimodal LLMs62%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

AnyModal – Train Your Own Multimodal LLMs

Hacker News8
Lo
LoongForge-Train LLMs, VLMs, diffusion and embodied models, faster73%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

LoongForge-Train LLMs, VLMs, diffusion and embodied models, faster

Hacker News2
Ru
Run LLMs on the Browser74%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Run LLMs on the Browser

Hacker News6
Tr
Train against procrastination42%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Train against procrastination

Hacker News9
Tr
Train Stable Diffusion Dreambooth on 1080ti60%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Train Stable Diffusion Dreambooth on 1080ti

Hacker News2
Ru
Run LLMs locally with WebGPU55%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Run LLMs locally with WebGPU

Hacker News2
GP
GPTCache – Redis for LLMs69%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

GPTCache – Redis for LLMs

Hacker News7
pr
prompttest – pytest for LLMs34%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

prompttest – pytest for LLMs

Hacker News2
Frontdoors
Frontdoors47%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

An AI device for doors

Indie Hackers2ai
Ca
Calculate VRAM Requirements to Train/Inference with Your LLMs40%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Calculate VRAM Requirements to Train/Inference with Your LLMs

Hacker News1