Can I run all-MiniLM-L6-v2 locally?
sentence-transformers/all-MiniLM-L6-v2all-MiniLM-L6-v2 is a embeddings model with about 23M parameters. The practical minimum to run it is roughly 2.8 GB of GPU or system memory, and a GPU with ≥ 3 GB VRAM runs it comfortably. Here's exactly what it needs and how to run it.
Check YOUR exact machineall-MiniLM-L6-v2 memory & VRAM requirements
How much memory all-MiniLM-L6-v2 needs at each quantization level (weights plus ~20% runtime overhead). Lower-bit quantization dramatically reduces VRAM at a small quality cost.
| Precision | Memory needed | Notes |
|---|---|---|
| Default precision | 2.8 GB | — |
Will all-MiniLM-L6-v2 run on your GPU?
Verdicts for common setups — from CPU-only laptops to an RTX 4090 and data-center GPUs. Click a GPU to see everything it can run.
| Your setup | Verdict | Needs | Best way to run |
|---|---|---|---|
| No GPU (16 GB RAM) | Runs | 2.8 GB | Run on CPU or GPU with the standard library |
| RTX 4060 (8 GB) | Runs | 2.8 GB | Run on CPU or GPU with the standard library |
| RTX 3060 (12 GB) | Runs | 2.8 GB | Run on CPU or GPU with the standard library |
| RTX 4070 Super (12 GB) | Runs | 2.8 GB | Run on CPU or GPU with the standard library |
| RTX 4080 Super (16 GB) | Runs | 2.8 GB | Run on CPU or GPU with the standard library |
| RTX 4090 (24 GB) | Runs | 2.8 GB | Run on CPU or GPU with the standard library |
| RTX 3090 (24 GB) | Runs | 2.8 GB | Run on CPU or GPU with the standard library |
| Apple M-series (18 GB unified) | Runs | 2.8 GB | Run on CPU or GPU with the standard library |
| Apple M Max (64 GB unified) | Runs | 2.8 GB | Run on CPU or GPU with the standard library |
| A100 (80 GB) | Runs | 2.8 GB | Run on CPU or GPU with the standard library |
How to run all-MiniLM-L6-v2 locally
Recommended method: Run on CPU or GPU with the standard library using sentence-transformers / Transformers.
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
print(model.encode(["hello world"]).shape)all-MiniLM-L6-v2 — frequently asked questions
How much memory does all-MiniLM-L6-v2 need?
all-MiniLM-L6-v2 needs roughly 2.8 GB of GPU or system memory to run, including runtime overhead. The exact amount depends on precision, context length, and framework.
Can I run all-MiniLM-L6-v2 on CPU without a GPU?
Yes — all-MiniLM-L6-v2 can run on CPU using system RAM (best with a quantized GGUF build via llama.cpp or Ollama), but generation will be noticeably slower than on a GPU.
What's the best way to run all-MiniLM-L6-v2 locally?
The easiest path is usually sentence-transformers / Transformers. Run on CPU or GPU with the standard library. This page's "How to run it" section has copy-paste commands.
What GPU do I need to run all-MiniLM-L6-v2?
A GPU with at least 3 GB of VRAM runs all-MiniLM-L6-v2 comfortably at 4-bit quantization. On Apple Silicon, unified memory of that size works too.