Lumimaid Magnum 12B — Hardware Requirements & GPU Compatibility
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Lumimaid Magnum 12B is a 12.2B-parameter open language model from Undi95. It supports a context window of up to 1,024,000 tokens. At Q4_K_M it needs about 8.07 GB of VRAM — see which GPUs and Macs can run it below.
15 downloads
22 likesSpecifications
- Publisher
- Undi95
- Parameters
- 12.2B
- Architecture
- MistralForCausalLM
- Context Length
- 1,024,000 tokens
- Vocabulary Size
- 131,072
- Release Date
- 2024-07-30
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HuggingFace
How Much VRAM Does Lumimaid Magnum 12B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 5.9 GB | 215.2 GB | 5.21 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 6.1 GB | 215.4 GB | 5.36 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 6.7 GB | 216.0 GB | 5.97 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 6.8 GB | 216.1 GB | 6.12 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 8.1 GB | 217.4 GB | 7.35 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 9.4 GB | 218.7 GB | 8.73 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 10.8 GB | 220.1 GB | 10.10 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 13.0 GB | 222.3 GB | 12.25 GB | 8-bit quantization, near-lossless |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Lumimaid Magnum 12B?
Q4_K_M · 8.1 GBLumimaid Magnum 12B (Q4_K_M) requires 8.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 11+ GB is recommended. Using the full 1024K context window can add up to 209.3 GB, bringing total usage to 217.4 GB. 39 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 Lumimaid Magnum 12B?
Q4_K_M · 8.1 GB49 devices with unified memory can run Lumimaid Magnum 12B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, iPad Pro M5 13" (16 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightWhere to Download Lumimaid Magnum 12B
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does Lumimaid Magnum 12B need?
Lumimaid Magnum 12B requires 8.1 GB of VRAM at Q4_K_M, or 25.2 GB at BF16. Full 1024K context adds up to 209.3 GB (217.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 12.2B × 4.8 bits ÷ 8 = 7.3 GB
KV Cache + Overhead ≈ 0.8 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 210.1 GB (at full 1024K context)
VRAM usage by quantization
Q4_K_M8.1 GBQ4_K_M + full context217.4 GB- Can NVIDIA GeForce RTX 4090 run Lumimaid Magnum 12B?
Yes, at Q8_0 (13.0 GB) or lower. Higher quantizations like BF16 (25.2 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Lumimaid Magnum 12B?
For Lumimaid Magnum 12B, Q4_K_M (8.1 GB) offers the best balance of quality and VRAM usage. Q4_K_L (8.2 GB) provides better quality if you have the VRAM. The smallest option is IQ2_M at 4.8 GB.
VRAM requirement by quantization
IQ2_M4.8 GBIQ3_M6.2 GBQ4_K_S7.6 GBQ4_K_M ★8.1 GBQ5_K_M9.4 GBBF1625.2 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Lumimaid Magnum 12B on a Mac?
Lumimaid Magnum 12B requires at least 4.8 GB at IQ2_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 Lumimaid Magnum 12B locally?
Yes — Lumimaid Magnum 12B can run locally on consumer hardware. At Q4_K_M quantization it needs 8.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Lumimaid Magnum 12B?
At Q4_K_M, Lumimaid Magnum 12B can reach ~545 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~81 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 8.1 × 0.65 = ~644 tok/s
Estimated speed at Q4_K_M (8.1 GB)
~644 tok/s~81 tok/s~644 tok/s~545 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Lumimaid Magnum 12B?
At Q4_K_M, the download is about 7.35 GB. The full-precision BF16 version is 24.50 GB. The smallest option (IQ2_M) is 4.13 GB.
- Which GPUs can run Lumimaid Magnum 12B?
39 consumer GPUs can run Lumimaid Magnum 12B at Q4_K_M (8.1 GB). Top options include AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 6900 XT, AMD Radeon RX 6700 XT. 26 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Lumimaid Magnum 12B?
52 devices with unified memory can run Lumimaid Magnum 12B at Q4_K_M (8.1 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.