1,000 models · refreshed nightly
Models that run on RTX 4070 · 16 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 4070 · 16 GB |
|---|---|---|---|---|---|---|
| 251 | gemma-2-2b-it | 2.6 B | — | ⚠ gemma | 663 K | 6.6 GB |
| 252 | Ternary-Bonsai-27B-gguf | — | — | ✓ apache-2.0 | 663 K | 1.2 GB |
| 253 | Qwen3-TTS-12Hz-0.6B-Base | 910 M | — | ✓ apache-2.0 | 655 K | 3.4 GB |
| 254 | stable-diffusion-v1-4 | 860 M | — | ⚠ creativeml-openrail-m | 650 K | 13.5 GB |
| 255 | audiobox-aesthetics | 100 M | — | ✓ cc-by-4.0 | 637 K | 1.0 GB |
| 256 | nli-mpnet-base-v2 | 110 M | 512 | ✓ apache-2.0 | 635 K | 1.0 GB |
| 257 | Nemotron-3-Embed-1B-BF16 | 1.1 B | 262 K | other | 633 K | 3.2 GB |
| 258 | all-roberta-large-v1 | 360 M | 512 | ✓ apache-2.0 | 632 K | 2.1 GB |
| 259 | Qwen2.5-Coder-7B-Instruct-AWQ | 7.6 B | 33 K | ✓ apache-2.0 | 623 K | 7.9 GB |
| 260 | RMBG-2.0 | 220 M | — | other | 622 K | 1.5 GB |
| 261 | MiniCPM-SALA-AWQ-8bit | 3.1 B | 524 K | ✓ apache-2.0 | 616 K | 12.7 GB |
| 262 | gte-large | 340 M | 512 | ✓ mit | 616 K | 1.3 GB |
| 263 | SmolLM3-3B-Base | 3.1 B | 66 K | ✓ apache-2.0 | 615 K | 7.7 GB |
| 264 | SmolLM3-3B | 3.1 B | 66 K | ✓ apache-2.0 | 614 K | 7.7 GB |
| 265 | EXAONE-3.5-7.8B-Instruct-AWQ | 7.8 B | 33 K | other | 614 K | 7.5 GB |
| 266 | MiniCPM5-1B | 1.1 B | 131 K | ✓ apache-2.0 | 611 K | 3.0 GB |
| 267 | repvgg_a0.rvgg_in1k | 10 M | — | ✓ mit | 606 K | 0.5 GB |
| 268 | VieNeu-TTS-v3-Turbo | 130 M | 1 K | ✓ apache-2.0 | 605 K | 1.1 GB |
| 269 | Qwen2.5-Coder-3B-Instruct | 3.1 B | 33 K | other | 604 K | 7.8 GB |
| 270 | Qwen2-1.5B-Instruct | 1.5 B | 33 K | ✓ apache-2.0 | 601 K | 4.1 GB |
| 271 | bge-small-en-v1.5 | 30 M | 512 | ✓ mit | 599 K | 0.7 GB |
| 272 | Qwen3-ASR-0.6B | 940 M | — | ✓ apache-2.0 | 598 K | 2.7 GB |
| 273 | detr-resnet-50 | 40 M | 1 K | ✓ apache-2.0 | 591 K | 0.7 GB |
| 274 | Qwen3-Reranker-4B-W4A16-G128 | 4.1 B | 41 K | ✓ apache-2.0 | 586 K | 4.0 GB |
| 275 | LFM2.5-230M-GGUF | — | — | other | 586 K | 0.7 GB |
| 276 | Parable-Qwen3-8B-Claude-Fable-5-GGUF | — | — | ✓ apache-2.0 | 583 K | 6.0 GB |
| 277 | parakeet-tdt-0.6b-v3 | 630 M | — | ✓ cc-by-4.0 | 577 K | 1.4 GB |
| 278 | convnext_tiny.in12k_ft_in1k | 30 M | — | ✓ apache-2.0 | 577 K | 0.6 GB |
| 279 | table-transformer-detection | 30 M | 1 K | ✓ mit | 576 K | 0.6 GB |
| 280 | parakeet-tdt-0.6b-v3-gguf | — | — | ✓ cc-by-4.0 | 575 K | 1.0 GB |
| 281 | japanese-gpt-neox-small | 200 M | 2 K | ✓ mit | 575 K | 1.3 GB |
| 282 | SmolLM-1.7B-Instruct-quantized.w4a16 | 1.8 B | 2 K | ✓ apache-2.0 | 571 K | 2.7 GB |
| 283 | LFM2.5-8B-A1B-GGUF | — | — | other | 571 K | 5.8 GB |
| 284 | Qwen2.5-Coder-1.5B-Instruct | 1.5 B | 33 K | ✓ apache-2.0 | 567 K | 4.1 GB |
| 285 | wav2vec2-large-xls-r-300m-welsh | 300 M | — | ✓ apache-2.0 | 567 K | 1.2 GB |
| 286 | bge-reranker-v2.5-gemma2-lightweight-gptq | 9.2 B | 8 K | unknown | 563 K | 8.7 GB |
| 287 | e5-small-v2 | 30 M | 512 | ✓ mit | 558 K | 0.7 GB |
| 288 | xlm-roberta-base-language-detection | 280 M | 512 | ✓ mit | 557 K | 1.8 GB |
| 289 | rtdetr_v2_r18vd | 20 M | — | ✓ apache-2.0 | 557 K | 0.6 GB |
| 290 | phi-2 | 2.8 B | 2 K | ✓ mit | 556 K | 7.0 GB |
| 291 | wav2vec2-xls-r-300m-cv7-turkish | 300 M | — | ✓ cc-by-4.0 | 556 K | 1.2 GB |
| 292 | inclusively-classification | 110 M | 512 | ✗ cc-by-nc-sa-4.0 | 552 K | 1.0 GB |
| 293 | paraphrase-albert-small-v2 | 10 M | 512 | ✓ apache-2.0 | 551 K | 0.6 GB |
| 294 | macbert4csc-base-chinese | 100 M | 512 | ✓ apache-2.0 | 543 K | 1.0 GB |
| 295 | gpt-oss-20b-GGUF | — | 131 K | ✓ apache-2.0 | 543 K | 13.1 GB |
| 296 | resnet-50 | 30 M | — | ✓ apache-2.0 | 542 K | 0.6 GB |
| 297 | ko-sroberta-multitask | 110 M | 512 | unknown | 540 K | 1.0 GB |
| 298 | gpt-neo-125m | 150 M | 2 K | ✓ mit | 538 K | 1.1 GB |
| 299 | whisper-bemba-stt | 240 M | — | unknown | 534 K | 1.6 GB |
| 300 | Qwen3-Embedding-4B-W4A16-G128 | 4.1 B | 41 K | ✓ apache-2.0 | 532 K | 4.0 GB |
VRAM figures are estimates for the smallest available quantization at 8K context — see /methodology. Downloads refresh nightly from the Hugging Face API.