BitNet b1.58 2B4T
Ranked 4 of 4 quantized language models by recommenders
Fast, memory-efficient model using about 400 MB of RAM and less training data; critics say factors left out of the paper make it less practical.
Fast, memory-efficient model using about 400 MB of RAM and less training data; critics say factors left out of the paper make it less practical.
6 people gave an opinion
- recommend it
- 5
- criticize it
- 1
- approval share
- 83%
Recommended in 4 of 13 months
- November 2025: not recommended
- December 2025: not recommended
- January 2026: not recommended
- February 2026: recommended
- March 2026: recommended
- April 2026: not recommended
- May 2026: not recommended
- June 2026: not recommended
- July 2026: recommended
- August 2026: recommended
- September 2026: not recommended
- October 2026: not recommended
- November 2026: not recommended (month in progress)
Why it doesn’t carry the badge
- 5 people recommend itNeeds 10 or more, not met.
- 5 recommenders for every criticNeeds 2 to 1 or better, met.
- Recommended in 4 of 13 monthsNeeds 4 or more, met.
The same three rules apply to every product, and the badge can’t be bought. How the badge works
What people say about it
The reasons people give, with the number of comments that give each one. Open a reason to read what they said.
Why people recommend it
Each mark is one comment
Low memory use during inference
“it takes 1/6th as much memory during inference, and was trained on less than 1/4 the number of tokens”
Comment from 11 Mar 2026 in “Microsoft BitNet: 100B Param 1-Bit model for local CPUs” “the model itself is super fast and efficient, and only takes about 400 MB of RAM”
Comment from 2 Feb 2026 in “Claude is autonomously livecoding a major port (CPP –> WebGPU)” Impressive benchmark results
“The new model they just released has impressive benchmark results”
Comment from 11 Mar 2026 in “Microsoft BitNet: 100B Param 1-Bit model for local CPUs” Fast and efficient
“the model itself is super fast and efficient, and only takes about 400 MB of RAM”
Comment from 2 Feb 2026 in “Claude is autonomously livecoding a major port (CPP –> WebGPU)” Trained on fewer tokens
“it takes 1/6th as much memory during inference, and was trained on less than 1/4 the number of tokens”
Comment from 11 Mar 2026 in “Microsoft BitNet: 100B Param 1-Bit model for local CPUs”
What people warn about
Each mark is one comment
Less practical than paper suggests
“there are factors not considered in the paper which make it less practical than it seemed”
Comment from 21 Jul 2026 in “How to pack ternary numbers in 8-bit bytes”
Other quantized language models people recommend
The three most recommended other quantized language models, with the same counts for each, so you can compare like for like. The tick on each bar marks the 2 to 1 line the badge needs.
| Product | Recommend | Criticize | Approval | Months | Owned | Badge |
|---|---|---|---|---|---|---|
| Microsoft BitNet | 16 | 9 | 64% | 4 | — | — |
| Ternary Bonsai | 14 | 8 | 64% | 3 | — | — |
| Maple-Preview | 6 | 1 | 86% | 1 | — | — |
| BitNet b1.58 2B4TThis page | 5 | 1 | 83% | 4 | — | — |
What people said
5 of the 6 comments that mention it, newest first. Each links to the original comment.
“Isn't BitNet b1.58 2B4T what you are looking for?”
“there are factors not considered in the paper which make it less practical than it seemed”
“it takes 1/6th as much memory during inference, and was trained on less than 1/4 the number of tokens”
“The new model they just released has impressive benchmark results”
“the model itself is super fast and efficient, and only takes about 400 MB of RAM”
Counts come from 1,795,640 Hacker News comments, November 2025 to November 2026. How we count. Report a mistake.