LFM2 8B A1B — Hardware Requirements & GPU Compatibility
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LFM2 8B A1B is Liquid AI's larger mixture-of-experts model, combining the company's novel hybrid architecture with approximately 8 billion total parameters. It uses a MoE design to keep active compute per token low while maintaining strong general performance across chat and reasoning tasks. For local users, it offers an intriguing alternative to conventional 8B transformers, with Liquid AI's architecture promising improved efficiency and throughput on consumer-grade hardware.
62.9K downloads
335 likesSpecifications
- Publisher
- LiquidAI
- Parameters
- 8.3B
- Architecture
- Lfm2MoeForCausalLM
- Context Length
- 128,000 tokens
- Vocabulary Size
- 65,536
- Release Date
- 2026-03-04
- License
- Other
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HuggingFace
How Much VRAM Does LFM2 8B A1B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| IQ2_XXS | 2.20 | 2.7 GB | 8.9 GB | 2.29 GB | Importance-weighted 2-bit, extreme compression — significant quality loss |
| IQ2_S | 2.50 | 3.0 GB | 9.2 GB | 2.61 GB | Importance-weighted 2-bit, small |
| IQ2_M | 2.70 | 3.2 GB | 9.4 GB | 2.81 GB | Importance-weighted 2-bit, medium |
| IQ3_XXS | 3.10 | 3.6 GB | 9.8 GB | 3.23 GB | Importance-weighted 3-bit |
| Q2_K | 3.40 | 4.0 GB | 10.1 GB | 3.54 GB | 2-bit quantization with K-quant improvements |
| IQ3_S | 3.40 | 4.0 GB | 10.1 GB | 3.54 GB | Importance-weighted 3-bit, small |
| Q3_K_S | 3.50 | 4.0 GB | 10.2 GB | 3.65 GB | 3-bit small quantization |
| IQ3_M | 3.60 | 4.2 GB | 10.3 GB | 3.75 GB | Importance-weighted 3-bit, medium |
| Q3_K_M | 3.90 | 4.5 GB | 10.7 GB | 4.07 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 4.6 GB | 10.8 GB | 4.17 GB | 4-bit legacy quantization |
| Q3_K_L | 4.10 | 4.7 GB | 10.9 GB | 4.27 GB | 3-bit large quantization |
| IQ4_XS | 4.30 | 4.9 GB | 11.1 GB | 4.48 GB | Importance-weighted 4-bit, compact |
| IQ4_NL | 4.50 | 5.1 GB | 11.3 GB | 4.69 GB | Importance-weighted 4-bit, non-linear |
| Q4_K_S | 4.50 | 5.1 GB | 11.3 GB | 4.69 GB | 4-bit small quantization |
| Q4_1 | 4.50 | 5.1 GB | 11.3 GB | 4.69 GB | 4-bit legacy quantization with offset |
| Q4_K_M | 4.80 | 5.4 GB | 11.6 GB | 5.00 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_0 | 5.00 | 5.6 GB | 11.8 GB | 5.21 GB | 5-bit legacy quantization |
| Q5_K_S | 5.50 | 6.1 GB | 12.3 GB | 5.73 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 6.3 GB | 12.5 GB | 5.94 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 7.3 GB | 13.5 GB | 6.88 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 8.7 GB | 14.9 GB | 8.34 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run LFM2 8B A1B?
Q4_K_M · 5.4 GBLFM2 8B A1B (Q4_K_M) requires 5.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 8+ GB is recommended. Using the full 128K context window can add up to 6.2 GB, bringing total usage to 11.6 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run LFM2 8B A1B?
Q4_K_M · 5.4 GB33 devices with unified memory can run LFM2 8B A1B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Derivatives (5)
Frequently Asked Questions
- How much VRAM does LFM2 8B A1B need?
LFM2 8B A1B requires 5.4 GB of VRAM at Q4_K_M, or 8.7 GB at Q8_0. Full 128K context adds up to 6.2 GB (11.6 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8.3B × 4.8 bits ÷ 8 = 5 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 6.6 GB (at full 128K context)
VRAM usage by quantization
Q4_K_M5.4 GBQ4_K_M + full context11.6 GB- What's the best quantization for LFM2 8B A1B?
For LFM2 8B A1B, Q4_K_M (5.4 GB) offers the best balance of quality and VRAM usage. Q5_0 (5.6 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 2.7 GB.
VRAM requirement by quantization
IQ2_XXS2.7 GB~53%IQ3_S4.0 GB~75%Q3_K_L4.7 GB~86%Q4_K_M ★5.4 GB~89%Q5_05.6 GB~90%Q8_08.7 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run LFM2 8B A1B on a Mac?
LFM2 8B A1B requires at least 2.7 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 8B A1B locally?
Yes — LFM2 8B A1B can run locally on consumer hardware. At Q4_K_M quantization it needs 5.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is LFM2 8B A1B?
At Q4_K_M, LFM2 8B A1B can reach ~540 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~121 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 5.4 × 0.55 = ~540 tok/s
Estimated speed at Q4_K_M (5.4 GB)
AMD Instinct MI300X~540 tok/sNVIDIA GeForce RTX 4090~121 tok/sNVIDIA H100 SXM~404 tok/sAMD Instinct MI250X~334 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of LFM2 8B A1B?
At Q4_K_M, the download is about 5.00 GB. The full-precision Q8_0 version is 8.34 GB. The smallest option (IQ2_XXS) is 2.29 GB.