MN 12B Mag Mell R1 Q5 K M GGUF — Hardware Requirements & GPU Compatibility
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29 downloads
1 likesSpecifications
- Publisher
- roleplaiapp
- Parameters
- 12B
- Release Date
- 2025-01-27
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HuggingFace
How Much VRAM Does MN 12B Mag Mell R1 Q5 K M GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q5_K_M | 5.70 | 9.4 GB | — | 8.55 GB | 5-bit medium quantization — good quality/size tradeoff |
Which GPUs Can Run MN 12B Mag Mell R1 Q5 K M GGUF?
Q5_K_M · 9.4 GBMN 12B Mag Mell R1 Q5 K M GGUF (Q5_K_M) requires 9.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 13+ 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 MN 12B Mag Mell R1 Q5 K M GGUF?
Q5_K_M · 9.4 GB27 devices with unified memory can run MN 12B Mag Mell R1 Q5 K M GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does MN 12B Mag Mell R1 Q5 K M GGUF need?
MN 12B Mag Mell R1 Q5 K M GGUF requires 9.4 GB of VRAM at Q5_K_M.
VRAM = Weights + KV Cache + Overhead
Weights = 12B × 5.7 bits ÷ 8 = 8.6 GB
KV Cache + Overhead ≈ 0.8 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q5_K_M9.4 GB- Can I run MN 12B Mag Mell R1 Q5 K M GGUF on a Mac?
MN 12B Mag Mell R1 Q5 K M GGUF requires at least 9.4 GB at Q5_K_M, 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 MN 12B Mag Mell R1 Q5 K M GGUF locally?
Yes — MN 12B Mag Mell R1 Q5 K M GGUF can run locally on consumer hardware. At Q5_K_M quantization it needs 9.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is MN 12B Mag Mell R1 Q5 K M GGUF?
At Q5_K_M, MN 12B Mag Mell R1 Q5 K M GGUF can reach ~310 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~70 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 ÷ 9.4 × 0.55 = ~310 tok/s
Estimated speed at Q5_K_M (9.4 GB)
AMD Instinct MI300X~310 tok/sNVIDIA GeForce RTX 4090~70 tok/sNVIDIA H100 SXM~232 tok/sAMD Instinct MI250X~192 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of MN 12B Mag Mell R1 Q5 K M GGUF?
At Q5_K_M, the download is about 8.55 GB.
- Which GPUs can run MN 12B Mag Mell R1 Q5 K M GGUF?
28 consumer GPUs can run MN 12B Mag Mell R1 Q5 K M GGUF at Q5_K_M (9.4 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 MN 12B Mag Mell R1 Q5 K M GGUF?
27 devices with unified memory can run MN 12B Mag Mell R1 Q5 K M GGUF at Q5_K_M (9.4 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.