Qwen2.5-Math-1.5B-Instruct
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 Qwen/Qwen2.5-Math-1.5B-InstructNew to this? Start with Ollama · serve to many users with vLLM.
Deep dive
Notes, sources, and the full write-up
Qwen2.5-Math-1.5B-Instruct is an open-weight math model from Qwen, published on Hugging Face in September 2024. It has 1.5B parameters and is released under the apache-2.0 license.
HardwareLink to this section
At full FP16 precision the weights need about 3 GB of memory; 8-bit quantization brings that to roughly 2 GB. That is small enough for most consumer GPUs, Apple Silicon Macs, and many CPUs.
PopularityLink to this section
Qwen2.5-Math-1.5B-Instruct had 156,250 downloads on Hugging Face in the last month and has 67 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 Qwen2.5-Math-1.5B-Instruct need?
Qwen2.5-Math-1.5B-Instruct with 1.5B parameters needs approximately 1 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.
Is Qwen2.5-Math-1.5B-Instruct better than other Qwen models?
Qwen2.5-Math-1.5B-Instruct has 1.5B parameters with 4,096 context - a strong choice for math, edge.
What license is Qwen2.5-Math-1.5B-Instruct under?
Qwen2.5-Math-1.5B-Instruct is released under the apache-2.0 license, making it suitable for most commercial and personal projects.
What hardware runs Qwen2.5-Math-1.5B-Instruct well?
With 1.5B parameters, Qwen2.5-Math-1.5B-Instruct 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 Qwen2.5-Math-1.5B-Instruct?
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 Qwen2.5-Math-1.5B-Instruct?
Qwen2.5-Math-1.5B-Instruct 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