What GPU do I need to run LiquidAI/LFM3-Omni-2B?
2.2B parameters, published in BF16. View on Hugging Face
Set up LFM3-Omni-2BLFM3-Omni-2B is published by LiquidAI on Hugging Face, with 17,262 downloads and 426 likes to date. It's a Lfm3OmniForConditionalGeneration model built for any-to-any, published natively in BF16.
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision times a flat 1.2 overhead for activations and fragmentation. The KV-cache grows with context and is not in that factor; it is listed per model below where the architecture is published. Full formula and assumptions: methodology.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 4.0 GB | 4.8 GB | RTX 3070 | 1 | $0.132/hr |
| FP8 (quantized) | 2.0 GB | 2.4 GB | RTX 4070 | 1 | $0.154/hr |
| INT4 (quantized) | 1.0 GB | 1.2 GB | RTX 3070 | 1 | $0.132/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 LFM3-Omni-2B at its published (BF16) precision: 1× RTX 3070, at $0.132/hr per GPU ($0.132/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
LFM3-Omni-2B: KV cache by context length
The KV cache is the memory the attention layers hold for every token of context, on top of the weights. It is not part of the flat 1.2x overhead in the table above, grows with context length and with every concurrent request, and is why a long-context deployment needs more VRAM than the table shows.
KV-cache size for LFM3-Omni-2B is not published for this architecture, so no figure is shown.
LFM3-Omni-2B: common questions
How much VRAM does LFM3-Omni-2B need?
4.8 GB at BF16, 2.4 GB at FP8 (quantized), 1.2 GB at INT4 (quantized). All of those sit under 6 GB, the smallest capacity this page reasons about, so VRAM is not what limits where this model runs. The 4.0 GB of weights plus inference overhead is the whole requirement.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More LiquidAI models
- LFM3-Audio-2B (2.0B, BF16)
- d1-2B (1.9B, BF16)
- LFM3-1.7B (1.7B, BF16)
- LFM3-1.7B-Base (1.7B, BF16)
Alternatives at this size
Other models for any-to-any within about a third of LFM3-Omni-2B's 2.2B parameters, from other model lines.
- Janus-Pro-1B (2.1B, BF16)
More on LFM3-Omni-2B
Fits on an 8 GB GPU at BF16: every model that fits in 8 GB.
Related reading: RTX 3070 pricing and specs, How much VRAM you need for LLMs, Serving LLMs with vLLM, vLLM vs TensorRT-LLM vs SGLang, and Best GPU for LLM inference.
