Nomic Embed Text (class): Hardware & Business Fit
- Embedding
A small, fast, openly-licensed embedding model — a sensible default for getting document search working cheaply. Runs alongside your chat model, not instead of it.
- Parameters
- ~0.14B
- Context
- ~8K tokens
- Deployment
- local
What Nomic Embed Text (class) is good for
- ▸Document search (RAG)
- ▸Cheap on-prem embeddings
Best quantization choices
Approximate memory per quantization (weights + KV cache at modest context). Treat as ±.
| Quant | ~Memory | When to use |
|---|---|---|
| FP16 | ~1GB | Full precision; largest footprint, best quality. |
Run Nomic Embed Text (class) locally
Pull and run with Ollama, or grab the weights from Hugging Face.
$ ollama run nomic-embed-textnomic-ai/nomic-embed-text-v1.5Compatible hardware
Devices from our catalog graded for Nomic Embed Text (class), best fit first.
- NVIDIA B200 (placeholder)NVIDIA · Datacenter GPUs
Fits at FP16 (~1GB) with ~168GB headroom — about 169 concurrent instances.
FP16 · ~1GBRuns well - Supermicro 8x H100 SuperServerSupermicro · AI Servers
Fits at FP16 (~1GB) with ~562.2GB headroom — about 563 concurrent instances.
FP16 · ~1GBRuns well - Dell PowerEdge XE9680Dell · AI Servers
Fits at FP16 (~1GB) with ~562.2GB headroom — about 563 concurrent instances.
FP16 · ~1GBRuns well - AMD Instinct MI300XAMD · Datacenter GPUs
Fits at FP16 (~1GB) with ~168GB headroom — about 169 concurrent instances.
FP16 · ~1GBRuns well - Cloud B200 (Blackwell profile, to verify)Cloud · Cloud GPU Profiles
Fits at FP16 (~1GB) with ~157.4GB headroom — about 158 concurrent instances.
FP16 · ~1GBRuns well - NVIDIA H200 (141GB)NVIDIA · Datacenter GPUs
Fits at FP16 (~1GB) with ~123.1GB headroom — about 124 concurrent instances.
FP16 · ~1GBRuns well - Cloud H200 141GB (profile)Cloud · Cloud GPU Profiles
Fits at FP16 (~1GB) with ~123.1GB headroom — about 124 concurrent instances.
FP16 · ~1GBRuns well - NVIDIA H100 (80GB)NVIDIA · Datacenter GPUs
Fits at FP16 (~1GB) with ~69.4GB headroom — about 70 concurrent instances.
FP16 · ~1GBRuns well - Cloud H100 80GB (profile)Cloud · Cloud GPU Profiles
Fits at FP16 (~1GB) with ~69.4GB headroom — about 70 concurrent instances.
FP16 · ~1GBRuns well - NVIDIA RTX PRO 6000 BlackwellNVIDIA · Professional GPUs
Fits at FP16 (~1GB) with ~83.5GB headroom — about 84 concurrent instances.
FP16 · ~1GBRuns well
Use inside the AI Business OS
Nomic Embed Text (class) suits these AI Business OS agent archetypes:
A model is only the engine. Inside the AI Business OS it is wrapped with permissions, tools, connectors, RAG and audit so it can actually do business work safely — see how the AI Business OS works →
Frequently asked questions
What hardware do I need to run Nomic Embed Text (class)?+
At 4-bit you need roughly a few GB of usable memory. The minimum self-hostable option in our catalog is the NVIDIA GeForce RTX 3060 12GB. For a comfortable run we recommend the NVIDIA B200 (placeholder).
Which quantization should I use for Nomic Embed Text (class)?+
Q4_K_M is the usual default — the best size/quality trade-off. Step up to Q8_0 or FP16 if you have spare memory and want higher fidelity.
Should I run Nomic Embed Text (class) locally or in the cloud?+
Local-first is recommended for Nomic Embed Text (class). It fits comfortably on hardware you can own, keeping data private and costs predictable.
Related models
Similar picks — family siblings and nearest-size models of the same kind.
Use Nomic Embed Text (class) inside your AI Business OS
BrainOutput helps you run Nomic Embed Text (class) as a private business agent — wrapped with the tools, connectors, RAG and guardrails it needs to do real work on hardware you control.
Use this model in your AI Business OS