table-transformer-detection
Will it run on your hardware?
Pick your GPU memory - see which quantizations fit, and the cheapest card for the rest
Need an exact figure for your context length? Use the VRAM calculator.
Run it locally
Copy-paste - running in under a minute
vllm serve microsoft/table-transformer-detectionNew to this? Start with Ollama · serve to many users with vLLM.
Deep dive
Notes, sources, and the full write-up
table-transformer-detection is an open-weight object detection model from microsoft, published on Hugging Face in October 2022. It has 30M parameters and is released under the mit license.
HardwareLink to this section
At full FP16 precision the weights need about 1 GB of memory; 8-bit quantization brings that to roughly 1 GB. That is small enough for most consumer GPUs, Apple Silicon Macs, and many CPUs.
PopularityLink to this section
table-transformer-detection had 262,625 downloads on Hugging Face in the last month and has 445 likes.
Run it locallyLink to this section
The weights are on Hugging Face and load with Hugging Face Transformers. Follow the model card for the exact setup and license terms.
Frequently asked
Quick answers to common questions
How much VRAM does table-transformer-detection need?
table-transformer-detection with 0.03B parameters needs approximately 1 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.
Is table-transformer-detection better than other microsoft models?
table-transformer-detection has 0.03B parameters with 1,024 context - a strong choice for object-detection.
What license is table-transformer-detection under?
table-transformer-detection is released under the mit license, making it suitable for most commercial and personal projects.
What hardware runs table-transformer-detection well?
With 0.03B parameters, table-transformer-detection requires adequate VRAM. High-end GPUs like the RTX 4090 (24GB), RTX 5090 (32GB), or Mac Studio with unified memory are good options. Check our hardware directory for specific recommendations.
What is the best quantization for table-transformer-detection?
Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Q5_K_M (~1 GB) is an option if you have spare VRAM. Use our VRAM calculator to compare.
What models compete with table-transformer-detection?
table-transformer-detection competes with other models in its class. Browse our model directory for comparisons, benchmarks, and community reviews to find the best fit.
Nearby options
Similar models and compatible hardware by spec