What GPU do I need to run uzlm/alloma-3B-Instruct?
3.2B parameters, published in BF16. View on Hugging Face
Set up alloma-3B-Instructalloma-3B-Instruct is published by uzlm on Hugging Face, with 54,851 downloads and 6 likes to date. It's a LlamaForCausalLM 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 | 6.0 GB | 7.2 GB | RTX 4070 Super | 1 | $0.121/hr |
| FP8 (quantized) | 3.0 GB | 3.6 GB | RTX 4070 Super | 1 | $0.121/hr |
| INT4 (quantized) | 1.5 GB | 1.8 GB | RTX 4070 Super | 1 | $0.121/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 alloma-3B-Instruct at its published (BF16) precision: 1× RTX 4070 Super, at $0.121/hr per GPU ($0.121/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
alloma-3B-Instruct: common questions
Does alloma-3B-Instruct fit on a 8 GB GPU?
Yes. At BF16 it needs 7.2 GB of VRAM, so an 8 GB card holds it with 0.8 GB to spare. A 6 GB card is not enough for it at BF16.
What is the least VRAM alloma-3B-Instruct can run in?
1.8 GB, at INT4 (quantized), which fits a 6 GB card, against 7.2 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.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Llama 3.2 models
- Llama-3.2-3B-Instruct (3.2B, BF16)
- Llama-3.2-3B (3.2B, BF16)
- Llama-3.2-3B-Instruct (3.2B, BF16)
- Llama-3.2-3B (3.2B, BF16)
- Llama-3.2-3B-Instruct-pythonic (3.2B, BF16)
Related reading: H100 pricing and specs, The best GPUs for AI, ranked, and Best GPU for LLM inference.
