1,011 models · refreshed nightly
Models that run on RTX 4090 · 24 GB
Every model in the catalog with its licence, estimated VRAM and daily-tracked downloads. Filters update the URL — share any view.
| # | Model | Params | Context | Commercial use | 30d | On RTX 4090 · 24 GB |
|---|---|---|---|---|---|---|
| 151 | parakeet-tdt-0.6b-v3 | 630 M | — | ✓ cc-by-4.0 | 1.4 M | 3.4 GB |
| 152 | snowflake-arctic-embed-l-v2.0 | 570 M | 8 K | ✓ apache-2.0 | 1.4 M | 3.1 GB |
| 153 | gemma-4-26B-A4B-it-GGUF | — | — | ✓ apache-2.0 | 1.4 M | 11.4 GB |
| 154 | wav2vec2-xls-r-300m-cs-250 | 320 M | — | ✓ apache-2.0 | 1.4 M | 1.9 GB |
| 155 | w2v-bert-2.0 | 580 M | — | ✓ mit | 1.4 M | 3.1 GB |
| 156 | Qwen3-VL-Embedding-2B | 2.1 B | — | ✓ apache-2.0 | 1.4 M | 5.5 GB |
| 157 | Llama-3.1-Nemotron-Nano-VL-8B-V1 | 8.7 B | — | other | 1.3 M | 21.0 GB |
| 158 | Qwen3.5-9B-AWQ | 9.7 B | — | ✓ apache-2.0 | 1.3 M | 15.6 GB |
| 159 | Mistral-7B-Instruct-v0.2 | 7.2 B | 33 K | ✓ apache-2.0 | 1.3 M | 17.5 GB |
| 160 | Phi-3.5-vision-instruct | 4.2 B | 131 K | ✓ mit | 1.3 M | 10.2 GB |
| 161 | bert-base-NER | 110 M | 512 | ✓ mit | 1.3 M | 1.0 GB |
| 162 | h2ovl-mississippi-800m | 830 M | — | ✓ apache-2.0 | 1.3 M | 2.4 GB |
| 163 | efficientnet_b0.ra_in1k | 10 M | — | ✓ apache-2.0 | 1.3 M | 0.5 GB |
| 164 | h2ovl-mississippi-2b | 2.2 B | — | ✓ apache-2.0 | 1.3 M | 5.6 GB |
| 165 | deepseek-vl2-tiny | 3.4 B | — | other | 1.3 M | 8.4 GB |
| 166 | gte-multilingual-base | 310 M | 8 K | ✓ apache-2.0 | 1.2 M | 1.2 GB |
| 167 | romanian-wav2vec2 | 320 M | — | ✓ apache-2.0 | 1.2 M | 1.9 GB |
| 168 | table-transformer-detection | 30 M | 1 K | ✓ mit | 1.2 M | 0.6 GB |
| 169 | Qwen3.6-27B-MTP-GGUF | — | — | ✓ apache-2.0 | 1.2 M | 14.3 GB |
| 170 | falcon-7b | 7.2 B | — | ✓ apache-2.0 | 1.2 M | 17.5 GB |
| 171 | ast-finetuned-audioset-10-10-0.4593 | 90 M | — | ✓ bsd-3-clause | 1.2 M | 0.9 GB |
| 172 | Qwen3.5-4B-GGUF | — | — | ✓ apache-2.0 | 1.2 M | 2.8 GB |
| 173 | gte-Qwen2-1.5B-instruct | 1.8 B | 131 K | ✓ apache-2.0 | 1.2 M | 8.6 GB |
| 174 | bloomz-560m | 560 M | — | ⚠ bigscience-bloom-rail-1.0 | 1.2 M | 1.8 GB |
| 175 | resnet-50 | 30 M | — | ✓ apache-2.0 | 1.2 M | 0.6 GB |
| 176 | Qwen3-4B-Instruct-2507-FP8 | 4.4 B | 262 K | ✓ apache-2.0 | 1.2 M | 6.9 GB |
| 177 | SmolVLM2-500M-Video-Instruct | 510 M | — | ✓ apache-2.0 | 1.2 M | 2.8 GB |
| 178 | surya-ocr-2 | 690 M | — | ⚠ openrail | 1.2 M | 2.1 GB |
| 179 | Qwen3-8B-AWQ | 8.2 B | 41 K | ✓ apache-2.0 | 1.1 M | 8.4 GB |
| 180 | e5-base-v2 | 110 M | 512 | ✓ mit | 1.1 M | 1.0 GB |
| 181 | bge-micro-v2 | 20 M | 512 | ✓ mit | 1.1 M | 0.5 GB |
| 182 | Phi-3.5-mini-instruct | 3.8 B | 131 K | ✓ mit | 1.1 M | 9.5 GB |
| 183 | Wav2Vec2-large-xlsr-hindi | 320 M | — | unknown | 1.1 M | 1.9 GB |
| 184 | clipseg-rd64-refined | 150 M | — | ✓ apache-2.0 | 1.1 M | 1.2 GB |
| 185 | all-roberta-large-v1 | 360 M | 512 | ✓ apache-2.0 | 1.1 M | 2.1 GB |
| 186 | SmolVLM-256M-Instruct | 260 M | — | ✓ apache-2.0 | 1.1 M | 1.1 GB |
| 187 | 1 | — | 2 K | unknown | 1.1 M | 0.5 GB |
| 188 | TinyLlama-1.1B-Chat-v0.3-GPTQ | 1.1 B | 2 K | ✓ apache-2.0 | 1.1 M | 1.5 GB |
| 189 | distiluse-base-multilingual-cased-v2 | 130 M | 512 | ✓ apache-2.0 | 1.1 M | 1.1 GB |
| 190 | wav2vec2-large-voxrex-swedish | 320 M | — | ✓ cc0-1.0 | 1.1 M | 1.9 GB |
| 191 | bge-small-en-v1.5 | 30 M | 512 | ✓ mit | 1.1 M | 0.7 GB |
| 192 | llava-onevision-qwen2-0.5b-ov-hf | 890 M | — | ✓ apache-2.0 | 1.1 M | 2.6 GB |
| 193 | nemotron-3.5-asr-streaming-0.6b | 640 M | — | other | 1.0 M | 1.4 GB |
| 194 | Qwen2.5-Coder-7B | 7.6 B | 33 K | ✓ apache-2.0 | 1.0 M | 18.4 GB |
| 195 | jina-embeddings-v2-small-en | 30 M | 8 K | ✓ apache-2.0 | 1.0 M | 0.6 GB |
| 196 | distiluse-base-multilingual-cased-v1 | 130 M | 512 | ✓ apache-2.0 | 1.0 M | 1.1 GB |
| 197 | Qwen3.5-9B-GGUF | — | — | ✓ apache-2.0 | 1.0 M | 5.2 GB |
| 198 | cohere-transcribe-03-2026 | 2.1 B | — | ✓ apache-2.0 | 1.0 M | 5.4 GB |
| 199 | cohere-transcribe-03-2026-gguf | — | — | ✓ apache-2.0 | 1,000 K | 2.2 GB |
| 200 | Qwen2-0.5B | 490 M | 131 K | ✓ apache-2.0 | 998 K | 1.7 GB |
VRAM figures are estimates for the smallest available quantization at 8K context — see /methodology. Downloads refresh nightly from the Hugging Face API.