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Open-weight model · Video classification
by Microsoft microsoft/xclip-large-patch14
xclip-large-patch14 is an open-weight model for video classification from Microsoft, released under MIT License. It has 576M parameters and a 77-token context. At 16-bit it needs about 1.4 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index. It draws 7.5k downloads a month.
X-CLIP model (large-sized, patch resolution of 14) trained fully-supervised on Kinetics-400. It was introduced in the paper Expanding Language-Image Pretrained Models for General Video Recognition by Ni et al. and first released in this repository.
What it takes to serve xclip-large-patch14 (576M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.
| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
|---|---|---|---|---|---|
| 16-bit | 1.2 GB | 1.4 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.6 GB | 0.7 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.3 GB | 0.3 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Oct 9, 2026.
xclip-large-patch14 on every accelerator the SAVRN Index prices, at every precision
By Microsoft, published under mit, revision a9dd1429a16c.
X-CLIP model (large-sized, patch resolution of 14) trained fully-supervised on Kinetics-400. It was introduced in the paper Expanding Language-Image Pretrained Models for General Video Recognition by Ni et al. and first released in this repository.
This model was trained using 8 frames per video, at a resolution of 224x224.
Disclaimer: The team releasing X-CLIP did not write a model card for this model so this model card has been written by the Hugging Face team.
X-CLIP is a minimal extension of CLIP for general video-language understanding. The model is trained in a contrastive way on (video, text) pairs.
This allows the model to be used for tasks like zero-shot, few-shot or fully supervised video classification and video-text retrieval.
You can use the raw model for determining how well text goes with a given video. See the model hub to look for fine-tuned versions on a task that interests you.
11 files, 4.6 GB in total. The weights are 2 files totalling 4.6 GB in bin, safetensors.
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 2.3 GB | 5dfd16f191d7 |
| pytorch_model.bin | Weights | 2.3 GB | 452ba339d9f0 |
| config.json | Configuration | 8.9 KB | — |
| preprocessor_config.json | Configuration | 310 B | — |
| special_tokens_map.json | Configuration | 472 B | — |
| README.md | Documentation | 2.7 KB | — |
| .gitattributes | Repository | 1.4 KB | — |
| merges.txt | Tokenizer | 524.6 KB | — |
| tokenizer.json | Tokenizer | 2.2 MB | — |
| tokenizer_config.json | Tokenizer | 927 B | — |
| vocab.json | Tokenizer | 1.1 MB | — |
Released by Microsoft through its official repository on Hugging Face. Read the license.
Each result is shown as reported, with the conditions its reporter stated. None is a SAVRN measurement. A comparison lines two results up only when their configuration, unit and setup are all stated and identical.
| Benchmark | Conditions | Result | Reported by | Revision | Date |
|---|---|---|---|---|---|
| Kinetics 400 | Task video-classificationMetric top-1 accuracyComparison conditions not established | 87.1 | microsoft Publisher reported |
Evaluated revision not stated | — |
| Kinetics 400 | Task video-classificationMetric top-5 accuracyComparison conditions not established | 97.6 | microsoft Publisher reported |
Evaluated revision not stated | — |
| Precision | Weights in memory |
|---|---|
| As published | 4.6 GB |
| 16-bit | 1.2 GB |
| 8-bit | 0.6 GB |
| 4-bit | 0.3 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
About 1.4 GB at 16-bit and 0.3 GB at 4-bit: the weights (576M parameters) plus a working margin. A long context needs more.
At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Yes. xclip-large-patch14 is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.
77 tokens, from the maximum position embeddings in its published configuration.
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