LFM2 8B A1B Q8 0 GGUF — Hardware Requirements & GPU Compatibility
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7 downloadsSpecifications
- Publisher
- ysn-rfd
- Parameters
- 8B
- License
- Other
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HuggingFace
How Much VRAM Does LFM2 8B A1B Q8 0 GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q8_0 | 8.00 | 8.8 GB | — | 8.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run LFM2 8B A1B Q8 0 GGUF?
Q8_0 · 8.8 GBLFM2 8B A1B Q8 0 GGUF (Q8_0) requires 8.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 12+ GB is recommended. 28 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run LFM2 8B A1B Q8 0 GGUF?
Q8_0 · 8.8 GB27 devices with unified memory can run LFM2 8B A1B Q8 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 Q8 0 GGUF need?
LFM2 8B A1B Q8 0 GGUF requires 8.8 GB of VRAM at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 8B × 8 bits ÷ 8 = 8 GB
KV Cache + Overhead ≈ 0.8 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q8_08.8 GB- Can I run LFM2 8B A1B Q8 0 GGUF on a Mac?
LFM2 8B A1B Q8 0 GGUF requires at least 8.8 GB at Q8_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 Q8 0 GGUF locally?
Yes — LFM2 8B A1B Q8 0 GGUF can run locally on consumer hardware. At Q8_0 quantization it needs 8.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is LFM2 8B A1B Q8 0 GGUF?
At Q8_0, LFM2 8B A1B Q8 0 GGUF can reach ~331 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~75 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 ÷ 8.8 × 0.55 = ~331 tok/s
Estimated speed at Q8_0 (8.8 GB)
AMD Instinct MI300X~331 tok/sNVIDIA GeForce RTX 4090~75 tok/sNVIDIA H100 SXM~248 tok/sAMD Instinct MI250X~205 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 Q8 0 GGUF?
At Q8_0, the download is about 8.00 GB.
- Which GPUs can run LFM2 8B A1B Q8 0 GGUF?
28 consumer GPUs can run LFM2 8B A1B Q8 0 GGUF at Q8_0 (8.8 GB). Top options include AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 6900 XT, AMD Radeon RX 6700 XT. 17 GPUs have plenty of headroom for comfortable inference.
- Which devices can run LFM2 8B A1B Q8 0 GGUF?
27 devices with unified memory can run LFM2 8B A1B Q8 0 GGUF at Q8_0 (8.8 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.