all-MiniLM (class): Hardware & Business Fit
- Embedding
A very small, fast sentence-embedding model. Retrieval quality is below larger embedders, but it is hard to beat for cost and speed at high volume.
- Parameters
- ~0.023B
- Context
- ~0.5K tokens
- Deployment
- local
What all-MiniLM (class) is good for
- ▸Cheap on-prem embeddings
- ▸High-volume retrieval
- ▸On-device search
Best quantization choices
Approximate memory per quantization (weights + KV cache at modest context). Treat as ±.
| Quant | ~Memory | When to use |
|---|---|---|
| FP16 | ~0.2GB | Full precision; largest footprint, best quality. |
Run all-MiniLM (class) locally
Pull and run with Ollama, or grab the weights from Hugging Face.
$ ollama run all-minilmsentence-transformers/all-MiniLM-L6-v2Compatible hardware
Devices from our catalog graded for all-MiniLM (class), best fit first.
- NVIDIA B200 (placeholder)NVIDIA · Datacenter GPUs
Fits at FP16 (~0.2GB) with ~168.8GB headroom — about 845 concurrent instances.
FP16 · ~0.2GBRuns well - Supermicro 8x H100 SuperServerSupermicro · AI Servers
Fits at FP16 (~0.2GB) with ~563GB headroom — about 2816 concurrent instances.
FP16 · ~0.2GBRuns well - Dell PowerEdge XE9680Dell · AI Servers
Fits at FP16 (~0.2GB) with ~563GB headroom — about 2816 concurrent instances.
FP16 · ~0.2GBRuns well - AMD Instinct MI300XAMD · Datacenter GPUs
Fits at FP16 (~0.2GB) with ~168.8GB headroom — about 845 concurrent instances.
FP16 · ~0.2GBRuns well - Cloud B200 (Blackwell profile, to verify)Cloud · Cloud GPU Profiles
Fits at FP16 (~0.2GB) with ~158.2GB headroom — about 792 concurrent instances.
FP16 · ~0.2GBRuns well - NVIDIA H200 (141GB)NVIDIA · Datacenter GPUs
Fits at FP16 (~0.2GB) with ~123.9GB headroom — about 620 concurrent instances.
FP16 · ~0.2GBRuns well - Cloud H200 141GB (profile)Cloud · Cloud GPU Profiles
Fits at FP16 (~0.2GB) with ~123.9GB headroom — about 620 concurrent instances.
FP16 · ~0.2GBRuns well - NVIDIA H100 (80GB)NVIDIA · Datacenter GPUs
Fits at FP16 (~0.2GB) with ~70.2GB headroom — about 352 concurrent instances.
FP16 · ~0.2GBRuns well - Cloud H100 80GB (profile)Cloud · Cloud GPU Profiles
Fits at FP16 (~0.2GB) with ~70.2GB headroom — about 352 concurrent instances.
FP16 · ~0.2GBRuns well - NVIDIA RTX PRO 6000 BlackwellNVIDIA · Professional GPUs
Fits at FP16 (~0.2GB) with ~84.3GB headroom — about 422 concurrent instances.
FP16 · ~0.2GBRuns well
Use inside the AI Business OS
all-MiniLM (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 all-MiniLM (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 all-MiniLM (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 all-MiniLM (class) locally or in the cloud?+
Local-first is recommended for all-MiniLM (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 all-MiniLM (class) inside your AI Business OS
BrainOutput helps you run all-MiniLM (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