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Models / MiniLM-L6-H384-uncased
1 model · ↓ 213.5M downloads a month · from $0.08/h on GTX 1660 SUPER right now. Each model's versions do the same job; quantized ones (AWQ, FP8, 4-bit) need less GPU memory and run on cheaper machines.
| Version | Runtime | Weights | GPU memory | Cheapest machine now |
|---|---|---|---|---|
| all MiniLM L6 v2 (sentence-transformers)sentence-transformers/all-MiniLM-L6-v2 | Text Embeddings Inference | 50 MB | 1.6 GB | $0.08/h GTX 1660 SUPER |
| all MiniLM L6 v2 (Xenova)Xenova/all-MiniLM-L6-v2 | ONNX Runtime | 20 MB | 1 GB | $0.08/h GTX 1660 SUPER |
| zlm v1 iab classify edge (zgpu-ai)zgpu-ai/zlm-v1-iab-classify-edge | ONNX Runtime | 20 MB | 1 GB | $0.08/h GTX 1660 SUPER |
| all MiniLM L6 v2 ONNX (onnx-community)onnx-community/all-MiniLM-L6-v2-ONNX | ONNX Runtime | 30 MB | 1 GB | $0.08/h GTX 1660 SUPER |
| all MiniLM L6 v2 code search 512 (isuruwijesiri)isuruwijesiri/all-MiniLM-L6-v2-code-search-512 | Text Embeddings Inference | 50 MB | 1.6 GB | $0.08/h GTX 1660 SUPER |
| smart tab embedding (Mozilla)Mozilla/smart-tab-embedding | Text Embeddings Inference | 50 MB | 1.6 GB | $0.08/h GTX 1660 SUPER |
| all MiniLM L6 v2 (KnightsAnalytics)KnightsAnalytics/all-MiniLM-L6-v2 | ONNX Runtime | 90 MB | 1.1 GB | $0.08/h GTX 1660 SUPER |
| tradexcel assistant encoder (adiikj)adiikj/tradexcel-assistant-encoder | ONNX Runtime | 20 MB | 1 GB | $0.08/h GTX 1660 SUPER |
| pocket embed 22m 128 (pocket-mind)pocket-mind/pocket-embed-22m-128 | Text Embeddings Inference | 50 MB | 1.6 GB | $0.08/h GTX 1660 SUPER |
| all MiniLM L6 v2 onnx (monju-lab)monju-lab/all-MiniLM-L6-v2-onnx | ONNX Runtime | 20 MB | 1 GB | $0.08/h GTX 1660 SUPER |
Model details come from Hugging Face and are refreshed daily; prices follow the live offers. GPU memory is GridPup's estimate: the weights, room for an 8,192-token conversation (chat models) and the runtime's own memory. Large models are split across the GPUs of one machine ("4x RTX 4090").