Dev Quasar 6 Q5_K_M 102B Parameters

VRAM Requirements for
moreh.Llama-3-Motif-102B-GGUF Q5_K_M

To run moreh.Llama-3-Motif-102B-GGUF locally at Q5_K_M quantization, you need at minimum 73.8 GB of GPU VRAM.

73.8 GB
Required VRAM
70.13 GB
File Size
8K tokens
Context Window
102B
Parameters
Estimated VRAM Required
73.8
GB
Data Centre Class
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 25.8 GB short
Budget $3,200 – $4,000

2× RTX 4090 (48GB) + aggressive quant

48 GB

Use a lower quantization to fit. Viable for testing at this scale.

Balanced $15,000 – $22,000

NVIDIA A100 80GB PCIe

80 GB HBM2e

Single-card 80GB. Industry-standard for large model inference.

Ultimate $25,000 – $40,000

NVIDIA H100 80GB SXM5

80 GB HBM3

State-of-the-art inference. 3× the bandwidth of A100.

📊 VRAM Calculation Breakdown

Model File Size (Q5_K_M) 70.13 GB
Context Overhead (8,192 tokens × 102B × 2 ÷ 1M) 1.671 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 73.8 GB

Try a Different Quantization

Use the interactive calculator to compare moreh.Llama-3-Motif-102B-GGUF across all available formats.

Open Live Calculator →

moreh.Llama-3-Motif-102B-GGUF — Other Quantizations

Q2_K 31.88 GB file
35.55 GB
Q3_K_M 44.63 GB file
48.3 GB
Q4_K_M 57.38 GB file
61.05 GB
Q5_K_M Current
73.8 GB
Q6_K 82.88 GB file
86.55 GB
Q8_0 108.38 GB file
112.05 GB
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Frequently Asked Questions

Can I run moreh.Llama-3-Motif-102B-GGUF Q5_K_M on a consumer GPU?
Running moreh.Llama-3-Motif-102B-GGUF Q5_K_M locally requires 73.8 GB VRAM, which exceeds consumer GPUs. You'll need prosumer cards like the NVIDIA A6000 (48GB) or an A100 (80GB).
What happens if I don't have enough VRAM?
If your GPU VRAM is insufficient, llama.cpp and similar tools will offload model layers to system RAM (CPU inference). This is much slower — expect 10-50× the generation latency compared to full GPU inference.
Can I use multiple GPUs to run moreh.Llama-3-Motif-102B-GGUF?
Yes! Tools like llama.cpp, vLLM, and Ollama support tensor parallelism across multiple GPUs. For example, 2× RTX 3090 (24GB each) gives you 48GB total VRAM, which can run many large models.
Is Q5_K_M quality good enough for production?
Q5_K_M is suitable for specialized use cases. Check community benchmarks for specific quality metrics.