1,000 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 |
|---|---|---|---|---|---|---|
| 401 | jina-reranker-m0 | 2.4 B | 33 K | ✗ cc-by-nc-4.0 | 395 K | 6.2 GB |
| 402 | gemma-3-270m | 270 M | — | ⚠ gemma | 395 K | 1.1 GB |
| 403 | granite-4.1-3b | 3.4 B | 131 K | ✓ apache-2.0 | 393 K | 8.5 GB |
| 404 | Ternary-Bonsai-8B-gguf | — | — | ✓ apache-2.0 | 392 K | 2.9 GB |
| 405 | Qwen3.8-27B-DFlash2 | 1.9 B | 262 K | ✓ apache-2.0 | 391 K | 5.0 GB |
| 406 | Kwaipilot_KAT-Coder-V2.5-Dev-GGUF | — | — | ✓ apache-2.0 | 390 K | 11.3 GB |
| 407 | Phi-4-mini-instruct | 3.8 B | 131 K | ✓ mit | 384 K | 9.5 GB |
| 408 | TwIL-LM3 | 3.1 B | 66 K | other | 384 K | 3.1 GB |
| 409 | NVIDIA-Nemotron-Nano-9B-v2 | 8.9 B | 131 K | other | 383 K | 21.4 GB |
| 410 | rtdetr_r50vd_coco_o365 | 40 M | — | ✓ apache-2.0 | 380 K | 0.7 GB |
| 411 | falcon-7b | 7.2 B | — | ✓ apache-2.0 | 379 K | 17.5 GB |
| 412 | pythia-14m | 10 M | 2 K | ✓ apache-2.0 | 378 K | 0.5 GB |
| 413 | Hermes-3-Llama-3.1-8B | 8.0 B | 131 K | ⚠ llama3 | 377 K | 19.4 GB |
| 414 | mistral-7b-v0.3-bnb-4bit | 7.5 B | 33 K | ✓ apache-2.0 | 377 K | 6.2 GB |
| 415 | Ornith-1.5-9B-OBLITERATED | 9.7 B | — | ✓ mit | 371 K | 6.3 GB |
| 416 | Qwen2-7B-Instruct | 7.6 B | 33 K | ✓ apache-2.0 | 369 K | 18.4 GB |
| 417 | segformer-b0-finetuned-ade-512-512 | <0.1 M | — | other | 368 K | 0.5 GB |
| 418 | Llama-3.2-3B | 3.2 B | — | ⚠ llama3.2 | 368 K | 8.0 GB |
| 419 | Parable-Granite-4.1-3B-Claude-Fable-5-GGUF | — | — | ✓ apache-2.0 | 366 K | 2.8 GB |
| 420 | privacy-filter-multilingual-GGUF | — | — | ✓ apache-2.0 | 366 K | 2.3 GB |
| 421 | Qwen3.8-27B-DFlash2 | 1.9 B | 262 K | ✓ apache-2.0 | 364 K | 5.0 GB |
| 422 | Parable-Granite-4.1-8B-Claude-Fable-5-GGUF | — | — | ✓ apache-2.0 | 362 K | 6.1 GB |
| 423 | Qwen2.5-Coder-1.5B | 1.5 B | 33 K | ✓ apache-2.0 | 361 K | 4.1 GB |
| 424 | MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-GGUF | — | — | ✓ apache-2.0 | 360 K | 1.8 GB |
| 425 | Qwen3.6-35B-A3B-NVFP4-MTP-GGUF | — | — | unknown | 360 K | 23.0 GB |
| 426 | POCKET-26B-GGUF | — | — | ✓ apache-2.0 | 360 K | 12.7 GB |
| 427 | Z-Image-Turbo-GGUF | — | — | ✓ apache-2.0 | 356 K | 4.5 GB |
| 428 | vlt5-base-keywords | 280 M | — | ✓ cc-by-4.0 | 355 K | 1.8 GB |
| 429 | t5-large | 740 M | — | ✓ apache-2.0 | 352 K | 3.9 GB |
| 430 | amd.Instella-MoE-16B-A3B-Think-GGUF | — | — | unknown | 351 K | 7.7 GB |
| 431 | RMBG-1.4 | 40 M | — | other | 349 K | 0.7 GB |
| 432 | wide_resnet50_2.racm_in1k | 70 M | — | ✓ apache-2.0 | 348 K | 0.8 GB |
| 433 | animagine-xl-4.0 | 2.6 B | — | ⚠ openrail++ | 344 K | 23.8 GB |
| 434 | Agents-A1-4B | 4.5 B | — | ✓ apache-2.0 | 344 K | 11.2 GB |
| 435 | Ornith-1.5-9B-MLX-8bit | 9.0 B | — | unknown | 341 K | 12.3 GB |
| 436 | Ornith-1.5-9B-MLX | 9.0 B | — | unknown | 341 K | 21.5 GB |
| 437 | Phi-3.5-mini-instruct | 3.8 B | 131 K | ✓ mit | 341 K | 9.5 GB |
| 438 | sd-turbo | 870 M | — | unknown | 339 K | 14.9 GB |
| 439 | Qwen2.5-3B | 3.1 B | 33 K | other | 338 K | 7.8 GB |
| 440 | Qwopus3.6-27B-Fusion-GGUF | — | — | other | 337 K | 15.4 GB |
| 441 | nsfw_image_detector | 90 M | — | ✓ mit | 334 K | 0.7 GB |
| 442 | distilroberta-finetuned-financial-news-sentiment-analysis | 80 M | 512 | ✓ apache-2.0 | 330 K | 0.9 GB |
| 443 | Qwen3.8-27B-DSpark | 1.9 B | 262 K | other | 329 K | 4.9 GB |
| 444 | Qwen3-Coder-30B-A3B-Instruct-AWQ | 30.5 B | 262 K | ✓ apache-2.0 | 329 K | 23.6 GB |
| 445 | t5-3b | 2.9 B | — | ✓ apache-2.0 | 326 K | 13.5 GB |
| 446 | LFM2.5-1.2B-Instruct-GGUF | — | — | other | 325 K | 1.3 GB |
| 447 | MiniCPM5-1B-Claude-Opus-Fable5-Thinking-GGUF | — | — | ✓ apache-2.0 | 325 K | 1.3 GB |
| 448 | deid_roberta_i2b2 | 350 M | 512 | ✓ mit | 324 K | 2.1 GB |
| 449 | Meta-Llama-3.1-8B-Instruct-GGUF | — | — | ⚠ llama3.1 | 320 K | 3.7 GB |
| 450 | Qwen3.6-27B-MTP-pi-tune-GGUF | — | — | ✓ apache-2.0 | 319 K | 12.5 GB |
VRAM figures are estimates for the smallest available quantization at 8K context — see /methodology. Downloads refresh nightly from the Hugging Face API.