LFM2 VL 1.6B — Hardware Requirements & GPU Compatibility
Vision
LFM2-VL-1.6B is Liquid AI's 1.6-billion-parameter vision-language model, pairing a hybrid convolution-and-attention LFM2 language backbone (1.2B parameters) with a 400-million-parameter SigLIP2 NaFlex vision encoder. It targets image understanding and lightweight agentic tasks with variable-resolution inputs, tuned for low latency over heavy reasoning; Liquid AI recommends fine-tuning it for narrow use cases. It is small enough to run on a single modest consumer GPU. The model supports a 128,000 token context window. It is released under Liquid AI's LFM Open License v1.0, free including for commercial use under $10 million in annual revenue, above which a separate license applies. Published in August 2025 alongside a smaller 450M sibling, it claims roughly 2x faster GPU inference than comparable models while handling native 512x512 resolution without upscaling.
83.9K downloads
233 likesSpecifications
- Publisher
- Liquid AI
- Family
- LFM2
- Parameters
- 1.6B
- Architecture
- Lfm2VlForConditionalGeneration
- Context Length
- 128,000 tokens
- Vocabulary Size
- 65,536
- Release Date
- 2025-08-12
- License
- Other
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HuggingFace
How Much VRAM Does LFM2 VL 1.6B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 1.0 GB | 5.2 GB | 0.67 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 1.1 GB | 5.2 GB | 0.69 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 1.1 GB | 5.3 GB | 0.77 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 1.2 GB | 5.3 GB | 0.79 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 1.3 GB | 5.5 GB | 0.95 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 1.5 GB | 5.6 GB | 1.13 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 1.7 GB | 5.8 GB | 1.31 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 1.9 GB | 6.1 GB | 1.58 GB | 8-bit quantization, near-lossless |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run LFM2 VL 1.6B?
Q4_K_M · 1.3 GBLFM2 VL 1.6B (Q4_K_M) requires 1.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ GB is recommended. Using the full 128K context window can add up to 4.1 GB, bringing total usage to 5.5 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run LFM2 VL 1.6B?
Q4_K_M · 1.3 GB59 devices with unified memory can run LFM2 VL 1.6B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download LFM2 VL 1.6B
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does LFM2 VL 1.6B need?
LFM2 VL 1.6B requires 1.3 GB of VRAM at Q4_K_M, or 3.5 GB at BF16. Full 128K context adds up to 4.1 GB (5.5 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 1.6B × 4.8 bits ÷ 8 = 1 GB
KV Cache + Overhead ≈ 0.3 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 4.5 GB (at full 128K context)
VRAM usage by quantization
Q4_K_M1.3 GBQ4_K_M + full context5.5 GB- What's the best quantization for LFM2 VL 1.6B?
For LFM2 VL 1.6B, Q4_K_M (1.3 GB) offers the best balance of quality and VRAM usage. Q5_0 (1.4 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 0.8 GB.
VRAM requirement by quantization
IQ2_XXS0.8 GBIQ3_XS1.0 GBQ4_01.2 GBQ4_K_M ★1.3 GBQ5_01.4 GBBF163.5 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run LFM2 VL 1.6B on a Mac?
LFM2 VL 1.6B requires at least 0.8 GB at IQ2_XXS, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run LFM2 VL 1.6B locally?
Yes — LFM2 VL 1.6B can run locally on consumer hardware. At Q4_K_M quantization it needs 1.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is LFM2 VL 1.6B?
At Q4_K_M, LFM2 VL 1.6B can reach ~3636 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~496 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 1.3 × 0.65 = ~3939 tok/s
Estimated speed at Q4_K_M (1.3 GB)
~3939 tok/s~496 tok/s~3939 tok/s~3636 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of LFM2 VL 1.6B?
At Q4_K_M, the download is about 0.95 GB. The full-precision BF16 version is 3.17 GB. The smallest option (IQ2_XXS) is 0.44 GB.
- Which GPUs can run LFM2 VL 1.6B?
52 consumer GPUs can run LFM2 VL 1.6B at Q4_K_M (1.3 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
- Which devices can run LFM2 VL 1.6B?
59 devices with unified memory can run LFM2 VL 1.6B at Q4_K_M (1.3 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.