What GPU do I need to run radheneev/NetrAI-L3?
3.2B parameters, published in F16. View on Hugging Face
Set up NetrAI-L3NetrAI-L3 is published by radheneev on Hugging Face, with 14,089 downloads and 3 likes to date. It's a LlamaForCausalLM model built for text-generation, published natively in F16.
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) |
|---|---|---|---|---|---|
| FP16 | 6.0 GB | 7.2 GB | V100 | 1 | $0.088/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 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 NetrAI-L3 at its published (F16) precision: 1× V100, at $0.088/hr per GPU ($0.088/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
NetrAI-L3: common questions
Does NetrAI-L3 fit on a 8 GB GPU?
Yes. At FP16 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 FP16.
How many copies of NetrAI-L3 fit on one V100?
2, by VRAM alone. That card carries 16.0 GB and one copy needs 7.2 GB at FP16, on a live rate of $0.088/hr for the whole card. Throughput is not modelled here, so 2 copies is not 2 times the requests served.
What is the least VRAM NetrAI-L3 can run in?
1.8 GB, at INT4 (quantized), which fits a 6 GB card, against 7.2 GB at FP16. 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: V100 pricing and specs, and The best GPUs for AI, ranked.
