Nomic Embed Text

What Nomic Embed Text is for

Nomic Embed Text is an embedding model, not a chat model: it turns text into vectors so a retrieval system can find the passages worth showing a language model. Nomic AI released it in 2024-02 under Apache 2.0, with a single 137M size and 8K of context. It is the piece you run alongside a generator in a local RAG stack, and the catalogue records its strength as default embedding model for RAG.

Sizes and memory

Our catalogue carries no per-size memory breakdown for Nomic Embed Text, and at 137M it does not need one: the record puts its floor at CPU inference, with 1 GB named as a comfortable allocation if you do have a card. That is the whole memory story for a model this small.

Hardware that runs Nomic Embed Text

Nomic Embed Text needs no GPU. Its recorded floor is CPU inference, with 1 GB recommended if you do have a card to put it on — which for an embedding model usually means keeping it resident next to a generator rather than needing the memory for itself. Any entry in our hardware guide runs it, and so does a machine with none of them.

What people use it for

The catalogue lists 3 recorded uses for Nomic Embed Text: RAG, retrieval, semantic search. It is tagged embedding, retrieval, RAG.

Where Nomic Embed Text sits in the Nomic family

Nomic Embed Text is the only Nomic entry in our catalogue, so there is no in-family alternative to weigh it against; the comparison to make is against the models listed at the foot of this page.

The Apache 2.0 licence puts no usage ceiling on it, which matters if the thing you are building has users. That is the practical difference between this and a vendor community licence.

Context window: 8K

8K tokens comes to roughly 6,000 words at the usual three-quarters-of-a-word-per-token ratio: a long chat, a single source file, one chapter. It is the band where a retrieval step stops being optional, because the interesting documents do not fit. The recommended VRAM figure above assumes a working context rather than the maximum, and filling this one will push past it.

How to run Nomic Embed Text locally

Our catalogue does not record registry tags, so look the current tag up in the Ollama library or on Hugging Face before pasting these. The commands below are the shape of the workflow, not a copy-and-paste recipe.

Option 1: Ollama (simplest)

# find the tag for Nomic Embed Text at ollama.com/library
ollama run <tag>

Option 2: Mullama (production)

mullama pull <tag>
mullama run <tag>

Option 3: llama.cpp (CLI)

# download a GGUF from Hugging Face, searching for "Nomic Embed Text GGUF"
./llama-cli -m model.Q4_K_M.gguf -p "Hello, AI!"

Option 4: Python with Mullama or llama-cpp-python

from mullama import Model, Context
model = Model.load("model.Q4_K_M.gguf", n_gpu_layers=99)
ctx = Context(model, n_ctx=4096)
print(ctx.generate("Hello, AI!", 256))

Sources

Model record from src/data/models.json (2026-06-29); card capacities from src/data/gpus.json (2026-06-29). Upstream: Curated from ollama.com/library + community benchmarks + paperswithcode + huggingface.