nomic-embed-text-v1.5
Will it run on your hardware?
Pick your GPU memory - see which quantizations fit, and the cheapest card for the rest
Need an exact figure for your context length? Use the VRAM calculator.
Run it locally
Copy-paste - running in under a minute
vllm serve nomic-ai/nomic-embed-text-v1.5New to this? Start with Ollama · serve to many users with vLLM.
Deep dive
Notes, sources, and the full write-up
nomic-embed-text-v1.5 is an open-weight text embedding model from nomic-ai, published on Hugging Face in February 2024. It has 140M parameters and is released under the apache-2.0 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
nomic-embed-text-v1.5 had 11,809,681 downloads on Hugging Face in the last month and has 993 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 nomic-embed-text-v1.5 need?
nomic-embed-text-v1.5 with 0.14B parameters needs approximately 1 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.
Is nomic-embed-text-v1.5 better than other nomic-ai models?
nomic-embed-text-v1.5 has 0.14B parameters with 2,048 context - a strong choice for embeddings.
What license is nomic-embed-text-v1.5 under?
nomic-embed-text-v1.5 is released under the apache-2.0 license, making it suitable for most commercial and personal projects.
What hardware runs nomic-embed-text-v1.5 well?
With 0.14B parameters, nomic-embed-text-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 nomic-embed-text-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 nomic-embed-text-v1.5?
nomic-embed-text-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