FreeLLMAll models › LiquidAI
LFM3-1.7B
LiquidAI · open license · released 23 Oct 2026
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Arena score—Not voted on Arena yet
Size1.7 Bparameters
4-bit download1.0 GBweights only, Q4_K_M
Max context256Ktokens, from the model config
Where to use it for free
- Run it in one command with whichllm (free, open source):
uvx whichllm@latest plan "LFM3-1.7B"shows the GPU you need,uvx whichllm@latest run "LFM3-1.7B gguf"downloads it and starts a chat - Download and run it at home: official weights · GGUF files for Ollama and LM Studio
No Arena votes yet
Arena has not voted on this model. It is listed because it is a popular small model on its lab's official Hugging Face page (33K downloads a month).
Speed on popular hardware
| Hardware | GPU memory | Fits? | Speed |
|---|---|---|---|
| RTX 3060 12 GB | 12 GB | Fits in GPU | 164 tok/s |
| RTX 4060 Ti 16 GB | 16 GB | Fits in GPU | 135 tok/s |
| RTX 3090 | 24 GB | Fits in GPU | 337 tok/s |
| RTX 4090 | 24 GB | Fits in GPU | 354 tok/s |
| RTX 5090 | 32 GB | Fits in GPU | 493 tok/s |
| RX 7900 XTX | 24 GB | Fits in GPU | 343 tok/s |
| 2 × RTX 3090 | 48 GB | Fits in GPU | 327 tok/s |
| 4 × RTX 3090 | 96 GB | Fits in GPU | 309 tok/s |
| Mac M4 Max 128 GB | 96 GB | Fits in GPU | 146 tok/s |
| Ryzen AI Max+ 395 128 GB | 96 GB | Fits in GPU | 122 tok/s |
| 8 × H100 SXM | 640 GB | Fits in GPU | 533 tok/s |
Estimate at 4-bit with 32K context, 64 GB of DDR5 for offload. Speed ≈ memory bandwidth × 78% ÷ data read per token, plus a fixed cost per token, calibrated on 44 real llama.cpp tests; typical error about ±10%. Compare all GPUs.
GPU memory needed
| Level | 8K context | 32K context | 128K context | Quality vs full |
|---|---|---|---|---|
| Q3_K_M | 1.6 GB | 1.9 GB | 3.1 GB | +10.5% noticeable loss |
| Q4_K_M | 1.7 GB | 2.0 GB | 3.2 GB | +2.8% small loss |
| Q5_K_M | 1.9 GB | 2.2 GB | 3.4 GB | +0.9% very small loss |
| Q6_K | 2.1 GB | 2.4 GB | 3.6 GB | +0.3% near-lossless |
| Q8_0 | 2.5 GB | 2.8 GB | 4.0 GB | ≈ 0% same as full |
| F16 | 4.1 GB | 4.4 GB | 5.6 GB | ≈ 0% same as full |
Weights + context cache (from the model's architecture) + runtime. Quality: increase in perplexity against the full model, from llama.cpp tests, scaled by model size. Simulate your own setup.
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