Bonsai – A Competitive Ternary Weight LLM

Hacker News

Introducing Bonsai 0.5B, one of the first ternary-weight LLMs to rival full-precision models of similar size, such as Qwen 2.5 0.5B and MobileLLM 0.5B. Trained on just 3.8B tokens, using 1,000x less data than other models, Bonsai redefines what’s possible for ultra-efficient training in low-bit models. Next, we're building larger and more powerful ternary-weight models for the edge. Technical Report: https://github.com/deepgrove-ai/Bonsai/blob/main/paper/Bonsa... Model (Unpacked): https://huggingface.co/deepgrove/Bonsai Reach us: team@deepgrove.ai

Visit product View on Hacker NewsMar 21, 2025

Share card

Actual performance

11points

Made the leaderboard

Launch Intel predictions

Analyze your own launch →

Product HuntOn track for Day 1 leaderboard · Strong signals: model, models, using · Missing: mac, agents, macos

77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: efficient · Missing: plus, platform, intuitive

51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

50%50% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: 000, io · Missing: https docs, excited, just released

48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

Similar products

When Will I Weigh? Weight Trajectory38%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

When Will I Weigh? Weight Trajectory

Hacker News1

A1161 - Competitive Writing34%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

A1161 - Competitive Writing

Hacker News1

Competitive Analysis and LLMO29%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Competitive Analysis and LLMO

Hacker News1

Redis-LLM – Redis module integrates LLM with Redis45%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Redis-LLM – Redis module integrates LLM with Redis

Hacker News2

LitLLM the Spiciest LLM Wrapper34%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

LitLLM the Spiciest LLM Wrapper

Hacker News1

LLM Reasonsers46%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

LLM Reasonsers

Hacker News2

Resilient LLM23%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Resilient LLM

Hacker News1

Hegelion – Force your LLM to argue with itself before answering58%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Hegelion – Force your LLM to argue with itself before answering

Hacker News1

I Stopped Hoping My LLM Would Cooperate49%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

I Stopped Hoping My LLM Would Cooperate

Hacker News3

LLM Hotkey39%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

TrustMRROther

Browse all products Analyze your launch