internlm2-math-plus-7b
internlmOthertext

internlm2-math-plus-7b

Updated Nov 13, 2026
Parameters
7.7B
Context
8,192
License
Other
Updated
Nov 13, 2026

Will it run on your hardware?

Pick your GPU memory - see which quantizations fit, and the cheapest card for the rest

Runs on your 24 GB - best at FP16
4 of 4 quantizations fit internlm2-math-plus-7b with real runtime overhead.
Q4_K_M
4 GB
Q5_K_M
5 GB
Q8_0
8 GB
FP16
15 GB
fits tight too big

Need an exact figure for your context length? Use the VRAM calculator.

Run it locally

Copy-paste - running in under a minute

vLLMOpenAI-compatible API
vllm serve internlm/internlm2-math-plus-7b

New to this? Start with Ollama · serve to many users with vLLM.

Deep dive

Notes, sources, and the full write-up

internlm2-math-plus-7b is an open-weight math model from internlm, published on Hugging Face in May 2024. It has 7.7B parameters and is released under a custom license (see the model card).

HardwareLink to this section

At full FP16 precision the weights need about 15 GB of memory; 8-bit quantization brings that to roughly 8 GB. Check the VRAM tiers above to see which GPUs fit it.

PopularityLink to this section

internlm2-math-plus-7b had 4,015 downloads on Hugging Face in the last month and has 11 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 internlm2-math-plus-7b need?

internlm2-math-plus-7b with 7.7B parameters needs approximately 4 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.

Is internlm2-math-plus-7b better than other internlm models?

internlm2-math-plus-7b has 7.7B parameters with 8,192 context - a strong choice for math.

What license is internlm2-math-plus-7b under?

internlm2-math-plus-7b is released under the Other license, making it suitable for most commercial and personal projects.

What hardware runs internlm2-math-plus-7b well?

With 7.7B parameters, internlm2-math-plus-7b 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 internlm2-math-plus-7b?

Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Q5_K_M (~5 GB) is an option if you have spare VRAM. Use our VRAM calculator to compare.

What models compete with internlm2-math-plus-7b?

internlm2-math-plus-7b 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