What GPU do I need to run migtissera/Tess-2.0-Llama-3-8B?
8.0B parameters, published in F16. View on Hugging Face
Set up Tess-2.0-Llama-3-8BTess-2.0-Llama-3-8B is published by migtissera on Hugging Face, with 16,796 downloads and 17 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 | 15.0 GB | 17.9 GB | RTX A5000 | 1 | $0.176/hr |
| FP8 (quantized) | 7.5 GB | 9.0 GB | RTX 5060 Ti | 1 | $0.110/hr |
| INT4 (quantized) | 3.7 GB | 4.5 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 Tess-2.0-Llama-3-8B at its published (F16) 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.
Tess-2.0-Llama-3-8B: common questions
Does Tess-2.0-Llama-3-8B fit on a 24 GB GPU?
Yes. At FP16 it needs 17.9 GB of VRAM, so a 24 GB card holds it with 6.1 GB to spare. A 16 GB card is not enough for it at FP16.
What is the least VRAM Tess-2.0-Llama-3-8B can run in?
4.5 GB, at INT4 (quantized), which fits a 6 GB card, against 17.9 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.
Does quantizing Tess-2.0-Llama-3-8B lower the GPU bill?
Yes. At FP16 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 Llama 3 models
- Meta-Llama-3-8B-Instruct (8.0B, BF16)
- Meta-Llama-3-8B (8.0B, BF16)
- Llama-3-8B-UltraMedical (8.0B, BF16)
- Meta-Llama-3-8B-Instruct (8.0B, BF16)
- Meta-Llama-3-8B (8.0B, 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.
