LFM2 24B A2B GGUF — Hardware Requirements & GPU Compatibility
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35.1K downloads
108 likesSpecifications
- Publisher
- LiquidAI
- Parameters
- 24B
- Release Date
- 2026-02-17
- License
- Other
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HuggingFace
How Much VRAM Does LFM2 24B A2B GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q4_0 | 4.00 | 13.2 GB | — | 12.00 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 15.8 GB | — | 14.40 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 18.8 GB | — | 17.10 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 21.8 GB | — | 19.80 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 26.4 GB | — | 24.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run LFM2 24B A2B GGUF?
Q4_K_M · 15.8 GBLFM2 24B A2B GGUF (Q4_K_M) requires 15.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 21+ GB is recommended. 17 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 5080.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run LFM2 24B A2B GGUF?
Q4_K_M · 15.8 GB27 devices with unified memory can run LFM2 24B A2B GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 (16 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does LFM2 24B A2B GGUF need?
LFM2 24B A2B GGUF requires 15.8 GB of VRAM at Q4_K_M, or 26.4 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 24B × 4.8 bits ÷ 8 = 14.4 GB
KV Cache + Overhead ≈ 1.4 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M15.8 GB- Can NVIDIA GeForce RTX 4090 run LFM2 24B A2B GGUF?
Yes, at Q6_K (21.8 GB) or lower. Higher quantizations like Q8_0 (26.4 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for LFM2 24B A2B GGUF?
For LFM2 24B A2B GGUF, Q4_K_M (15.8 GB) offers the best balance of quality and VRAM usage. Q5_K_M (18.8 GB) provides better quality if you have the VRAM. The smallest option is Q4_0 at 13.2 GB.
VRAM requirement by quantization
Q4_013.2 GB~85%Q4_K_M ★15.8 GB~89%Q5_K_M18.8 GB~92%Q6_K21.8 GB~95%Q8_026.4 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run LFM2 24B A2B GGUF on a Mac?
LFM2 24B A2B GGUF requires at least 13.2 GB at Q4_0, 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 24B A2B GGUF locally?
Yes — LFM2 24B A2B GGUF can run locally on consumer hardware. At Q4_K_M quantization it needs 15.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is LFM2 24B A2B GGUF?
At Q4_K_M, LFM2 24B A2B GGUF can reach ~184 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~41 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 ÷ 15.8 × 0.55 = ~184 tok/s
Estimated speed at Q4_K_M (15.8 GB)
AMD Instinct MI300X~184 tok/sNVIDIA GeForce RTX 4090~41 tok/sNVIDIA H100 SXM~138 tok/sAMD Instinct MI250X~114 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of LFM2 24B A2B GGUF?
At Q4_K_M, the download is about 14.40 GB. The full-precision Q8_0 version is 24.00 GB. The smallest option (Q4_0) is 12.00 GB.
- Which GPUs can run LFM2 24B A2B GGUF?
17 consumer GPUs can run LFM2 24B A2B GGUF at Q4_K_M (15.8 GB). Top options include AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090, NVIDIA GeForce RTX 3090 Ti, AMD Radeon RX 6800. 5 GPUs have plenty of headroom for comfortable inference.
- Which devices can run LFM2 24B A2B GGUF?
27 devices with unified memory can run LFM2 24B A2B GGUF at Q4_K_M (15.8 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.