LFM2 8B A1B Q4 0 GGUF — Hardware Requirements & GPU Compatibility
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27 downloadsSpecifications
- Publisher
- ysn-rfd
- Parameters
- 8B
- License
- Other
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HuggingFace
How Much VRAM Does LFM2 8B A1B Q4 0 GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q4_0 | 4.00 | 4.4 GB | — | 4.00 GB | 4-bit legacy quantization |
Which GPUs Can Run LFM2 8B A1B Q4 0 GGUF?
Q4_0 · 4.4 GBLFM2 8B A1B Q4 0 GGUF (Q4_0) requires 4.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 6+ GB is recommended. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run LFM2 8B A1B Q4 0 GGUF?
Q4_0 · 4.4 GB33 devices with unified memory can run LFM2 8B A1B Q4 0 GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does LFM2 8B A1B Q4 0 GGUF need?
LFM2 8B A1B Q4 0 GGUF requires 4.4 GB of VRAM at Q4_0.
VRAM = Weights + KV Cache + Overhead
Weights = 8B × 4 bits ÷ 8 = 4 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_04.4 GB- Can I run LFM2 8B A1B Q4 0 GGUF on a Mac?
LFM2 8B A1B Q4 0 GGUF requires at least 4.4 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 8B A1B Q4 0 GGUF locally?
Yes — LFM2 8B A1B Q4 0 GGUF can run locally on consumer hardware. At Q4_0 quantization it needs 4.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is LFM2 8B A1B Q4 0 GGUF?
At Q4_0, LFM2 8B A1B Q4 0 GGUF can reach ~663 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~149 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 ÷ 4.4 × 0.55 = ~663 tok/s
Estimated speed at Q4_0 (4.4 GB)
AMD Instinct MI300X~663 tok/sNVIDIA GeForce RTX 4090~149 tok/sNVIDIA H100 SXM~495 tok/sAMD Instinct MI250X~410 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 Q4 0 GGUF?
At Q4_0, the download is about 4.00 GB.
- Which GPUs can run LFM2 8B A1B Q4 0 GGUF?
35 consumer GPUs can run LFM2 8B A1B Q4 0 GGUF at Q4_0 (4.4 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run LFM2 8B A1B Q4 0 GGUF?
33 devices with unified memory can run LFM2 8B A1B Q4 0 GGUF at Q4_0 (4.4 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.