Run LiquidAI/LFM2.5-1.2B-Instruct-GGUF locally
LiquidAI/LFM2.5-1.2B-Instruct-GGUF is a compact instruction-tuned chat model with 1.17 billion parameters, built on the lfm2 architecture. It is released under the other license and has been downloaded 248,054 times.
To run LiquidAI/LFM2.5-1.2B-Instruct-GGUF locally at a 4,096-token context, its quantized versions need between 1.95 GB (Q4_0, lowest quality) and 3.48 GB (BF16, highest quality) of memory, weights plus KV cache and a system margin included.
For most users the best balance is BF16, needing about 3.48 GB. That means LiquidAI/LFM2.5-1.2B-Instruct-GGUF fits entirely in the VRAM of a 6 GB GPU or larger, running fully on the GPU.
All quantizations
| Quant. | Bits | Quality | Weights | KV | Total | Speed~ | Verdict |
|---|---|---|---|---|---|---|---|
| Q4_0 | 4.76 | Good | 0.65 GB | 0.5 GB | 1.95 GB | 617.3 t/s | Fits in VRAM |
| Q4_K_M | 5.0 | Good | 0.68 GB | 0.5 GB | 1.98 GB | 587.6 t/s | Fits in VRAM |
| Q5_K_M | 5.76 | Very good | 0.79 GB | 0.5 GB | 2.09 GB | 509.3 t/s | Fits in VRAM |
| Q6_K | 6.58 | Excellent | 0.9 GB | 0.5 GB | 2.2 GB | 446.1 t/s | Fits in VRAM |
| Q8_0 | 8.52 | Excellent | 1.16 GB | 0.5 GB | 2.46 GB | 344.6 t/s | Fits in VRAM |
| BF16 | 16.02 | Excellent | 2.18 GB | 0.5 GB | 3.48 GB | 183.3 t/s | Fits in VRAM |
| F16 | 16.02 | Excellent | 2.18 GB | 0.5 GB | 3.48 GB | 183.3 t/s | Fits in VRAM |
KV cache computed from the model's exact architecture. Speed is a rough estimate bounded by memory bandwidth.
Frequently asked questions
How much VRAM do you need to run LiquidAI/LFM2.5-1.2B-Instruct-GGUF?
You need about 3.48 GB of VRAM to run LiquidAI/LFM2.5-1.2B-Instruct-GGUF entirely on the GPU using the BF16 quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run LiquidAI/LFM2.5-1.2B-Instruct-GGUF on an 8 GB GPU?
Yes. With 8 GB of VRAM you can run LiquidAI/LFM2.5-1.2B-Instruct-GGUF fully on the GPU using BF16 (about 3.48 GB).
Can I run LiquidAI/LFM2.5-1.2B-Instruct-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run LiquidAI/LFM2.5-1.2B-Instruct-GGUF fully on the GPU using BF16 (about 3.48 GB).
Can I run LiquidAI/LFM2.5-1.2B-Instruct-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run LiquidAI/LFM2.5-1.2B-Instruct-GGUF fully on the GPU using BF16 (about 3.48 GB).
What is the best quantization for LiquidAI/LFM2.5-1.2B-Instruct-GGUF?
If memory allows, higher bits-per-weight means better quality. A common sweet spot is a Q4_K_M or Q5_K_M quantization, which keeps most of the quality while roughly halving the memory versus 8-bit. Pick the highest quantization that still fits in your VRAM.