What GPU do I need to run webAI-Official/TwIL-LM3?
3.1B parameters, published in BF16. View on Hugging Face
Set up TwIL-LM3TwIL-LM3 is published by webAI-Official on Hugging Face, with 258,894 downloads and 83 likes to date. It's a SmolLM3ForCausalLM 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 | 5.7 GB | 6.9 GB | RTX 3060 | 1 | $0.110/hr |
| FP8 (quantized) | 2.9 GB | 3.4 GB | RTX 4070 Super | 1 | $0.121/hr |
| INT4 (quantized) | 1.4 GB | 1.7 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 TwIL-LM3 at its published (BF16) precision: 1× RTX 3060, at $0.110/hr per GPU ($0.110/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
TwIL-LM3: common questions
Does TwIL-LM3 fit on a 8 GB GPU?
Yes. At BF16 it needs 6.9 GB of VRAM, so an 8 GB card holds it with 1.1 GB to spare. A 6 GB card is not enough for it at BF16.
What is the least VRAM TwIL-LM3 can run in?
1.7 GB, at INT4 (quantized), which fits a 6 GB card, against 6.9 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 webAI-Official models
- TwIL-LM3-Pro (3.7B, BF16)
- bert-base-uncased (110M, F32)
- Qwen3-0.6B (752M, BF16)
- gpt2 (137M, F32)
- Qwen3-8B (8.2B, BF16)
- Qwen3.6-35B-A3B-FP8 (36.0B, F8_E4M3)
Related reading: H100 pricing and specs, The best GPUs for AI, ranked, and Best GPU for LLM inference.
