What GPU do I need to run Zyphra/Zamba2-7B-Instruct?
7.4B parameters, published in BF16. View on Hugging Face
Set up Zamba2-7B-InstructZamba2-7B-Instruct is published by Zyphra on Hugging Face, with 10,952 downloads and 93 likes to date. It's a Zamba2ForCausalLM 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 | 13.7 GB | 16.4 GB | RTX A5000 | 1 | $0.176/hr |
| FP8 (quantized) | 6.9 GB | 8.2 GB | RTX 5060 Ti | 1 | $0.110/hr |
| INT4 (quantized) | 3.4 GB | 4.1 GB | RTX 5060 Ti | 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 Zamba2-7B-Instruct 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.
Zamba2-7B-Instruct: common questions
Does Zamba2-7B-Instruct fit on a 24 GB GPU?
Yes. At BF16 it needs 16.4 GB of VRAM, so a 24 GB card holds it with 7.6 GB to spare. A 16 GB card is not enough for it at BF16.
What is the least VRAM Zamba2-7B-Instruct can run in?
4.1 GB, at INT4 (quantized), which fits a 6 GB card, against 16.4 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 Zamba2-7B-Instruct lower the GPU bill?
Yes. At BF16 the cheapest live fit is one RTX A5000 at $0.176/hr. At FP8 (quantized) it drops to one RTX 5060 Ti 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 Zamba models
- Zamba2-2.7B (2.7B, BF16)
- Zamba2-1.2B-instruct (1.2B, F32)
- Zamba-7B-v1 (7.2B, 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.
