What GPU do I need to run LiquidAI/LFM2.5-350M?
354M parameters, published in BF16. View on Hugging Face
LFM2.5-350M is published by LiquidAI on Hugging Face, with 97,265 downloads and 405 likes to date. It's a Lfm2ForCausalLM 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.
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 caveat: requires a quantized checkpoint actually published for this model — check its Hugging Face page before relying on this row.
Cheapest way to run LFM2.5-350M at its published (BF16) precision: 1× RTX 3070 on simplepod, at $0.050/hr per GPU ($0.050/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More LiquidAI models
- LFM2.5-1.2B-Instruct (1.2B, BF16)
- LFM2.5-8B-A1B (8.5B, BF16)
- LFM2.5-2.6B (2.7B, BF16)
- LFM2-1.2B (1.2B, BF16)
- Qwen3-0.6B (752M, BF16)
- Qwen3-8B (8.2B, BF16)
