bge-base-en-v1.5
BAAImittext

bge-base-en-v1.5

Updated Nov 13, 2026
Parameters
0.11B
Context
512
License
mit
Updated
Nov 13, 2026

Will it run on your hardware?

Pick your GPU memory - see which quantizations fit, and the cheapest card for the rest

Runs on your 24 GB - best at FP16
4 of 4 quantizations fit bge-base-en-v1.5 with real runtime overhead.
Q4_K_M
1 GB
Q5_K_M
1 GB
Q8_0
1 GB
FP16
1 GB
fits tight too big

Need an exact figure for your context length? Use the VRAM calculator.

Run it locally

Copy-paste - running in under a minute

vLLMOpenAI-compatible API
vllm serve BAAI/bge-base-en-v1.5

New to this? Start with Ollama · serve to many users with vLLM.

Deep dive

Notes, sources, and the full write-up

bge-base-en-v1.5 is an open-weight text embedding model from BAAI, published on Hugging Face in September 2023. It has 110M parameters and is released under the mit license.

HardwareLink to this section

At full FP16 precision the weights need about 1 GB of memory; 8-bit quantization brings that to roughly 1 GB. That is small enough for most consumer GPUs, Apple Silicon Macs, and many CPUs.

PopularityLink to this section

bge-base-en-v1.5 had 8,434,743 downloads on Hugging Face in the last month and has 542 likes.

Run it locallyLink to this section

The weights are on Hugging Face and load with sentence-transformers. Follow the model card for the exact setup and license terms.

Frequently asked

Quick answers to common questions

How much VRAM does bge-base-en-v1.5 need?

bge-base-en-v1.5 with 0.11B parameters needs approximately 1 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.

Is bge-base-en-v1.5 better than other BAAI models?

bge-base-en-v1.5 has 0.11B parameters with 512 context - a strong choice for embeddings.

What license is bge-base-en-v1.5 under?

bge-base-en-v1.5 is released under the mit license, making it suitable for most commercial and personal projects.

What hardware runs bge-base-en-v1.5 well?

With 0.11B parameters, bge-base-en-v1.5 requires adequate VRAM. High-end GPUs like the RTX 4090 (24GB), RTX 5090 (32GB), or Mac Studio with unified memory are good options. Check our hardware directory for specific recommendations.

What is the best quantization for bge-base-en-v1.5?

Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Q5_K_M (~1 GB) is an option if you have spare VRAM. Use our VRAM calculator to compare.

What models compete with bge-base-en-v1.5?

bge-base-en-v1.5 competes with other models in its class. Browse our model directory for comparisons, benchmarks, and community reviews to find the best fit.

Nearby options

Similar models and compatible hardware by spec