What GPU do I need to run internlm/internlm2-chat-7b?
7.7B parameters, published in BF16. View on Hugging Face
Set up internlm2-chat-7binternlm2-chat-7b is published by internlm on Hugging Face, with 74,136 downloads and 83 likes to date. It's a InternLM2ForCausalLM model built for text-generation, published natively in BF16.
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision, plus a fixed overhead for KV-cache, activations, and fragmentation. Full formula and assumptions: methodology.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 14.4 GB | 17.3 GB | RTX A5000 | 1 | $0.176/hr |
| FP8 (quantized) | 7.2 GB | 8.6 GB | RTX 4070 Super | 1 | $0.121/hr |
| INT4 (quantized) | 3.6 GB | 4.3 GB | RTX 3060 | 1 | $0.110/hr |
A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.
INT4 requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Cheapest way to run internlm2-chat-7b at its published (BF16) precision: 1× RTX A5000, at $0.176/hr per GPU ($0.176/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
internlm2-chat-7b: common questions
Does internlm2-chat-7b fit on a 24 GB GPU?
Yes. At BF16 it needs 17.3 GB of VRAM, so a 24 GB card holds it with 6.7 GB to spare. A 16 GB card is not enough for it at BF16.
What is the least VRAM internlm2-chat-7b can run in?
4.3 GB, at INT4 (quantized), which fits a 6 GB card, against 17.3 GB at BF16. That row assumes an INT4 checkpoint has actually been published for this model, so check its Hugging Face page before planning around it.
Does quantizing internlm2-chat-7b lower the GPU bill?
Yes. At BF16 the cheapest live fit is one RTX A5000 at $0.176/hr. At INT4 (quantized) it drops to one RTX 3060 at $0.110/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More internlm models
- internlm3-8b-instruct (8.8B, BF16)
- internlm2_5-7b-chat (7.7B, BF16)
- internlm2-chat-20b (19.9B, BF16)
- Intern-S1 (240.7B, BF16)
- bert-base-uncased (110M, F32)
- Qwen3-0.6B (752M, BF16)
Related reading: RTX A5000 pricing and specs, The NVIDIA Inception program, explained, Google Colab alternatives for dedicated GPU access, Free GPU credits for students and researchers, and RunPod volume disk vs. network volume, compared.
