Tachyphylaxis Q6_K 123B Parameters

VRAM Requirements for
ML2-123B-Magnum-Diamond-GGUF Q6_K

To run ML2-123B-Magnum-Diamond-GGUF locally at Q6_K quantization, you need at minimum 103.96 GB of GPU VRAM.

103.96 GB
Required VRAM
99.94 GB
File Size
8K tokens
Context Window
123B
Parameters
Estimated VRAM Required
103.96
GB
Cluster Required
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

Budget $40,000+

4× A100 40GB Cluster

160 GB

Distributed inference across 4 A100s. Minimum viable cluster.

Balanced $60,000+

2× H100 80GB NVLink

160 GB HBM3

NVLink bridge enables unified 160GB VRAM pool.

Ultimate $3,000,000+

NVIDIA GB200 NVL72

1.4 TB HBM3e

Next-gen Grace Blackwell Superchip. Built for frontier models.

📊 VRAM Calculation Breakdown

Model File Size (Q6_K) 99.94 GB
Context Overhead (8,192 tokens × 123B × 2 ÷ 1M) 2.015 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 103.96 GB

Try a Different Quantization

Use the interactive calculator to compare ML2-123B-Magnum-Diamond-GGUF across all available formats.

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ML2-123B-Magnum-Diamond-GGUF — Other Quantizations

Q2_K 38.44 GB file
42.46 GB
Q2_K_S 38.44 GB file
42.46 GB
Q2_M 38.44 GB file
42.46 GB
Q2_S 38.44 GB file
42.46 GB
Q2_XS 38.44 GB file
42.46 GB
Q2_XXS 38.44 GB file
42.46 GB
Q3_XXS 53.81 GB file
57.83 GB
Q3_K_L 53.81 GB file
57.83 GB
Q3_K_M 53.81 GB file
57.83 GB
Q3_K_S 53.81 GB file
57.83 GB
Q3_M 53.81 GB file
57.83 GB
Q3_S 53.81 GB file
57.83 GB
Q3_XS 53.81 GB file
57.83 GB
Q4_K_S 69.19 GB file
73.21 GB
Q4_K_M 69.19 GB file
73.21 GB
Q4_NL 69.19 GB file
73.21 GB
Q4_XS 69.19 GB file
73.21 GB
Q5_K_M 84.56 GB file
88.58 GB
Q5_K_S 84.56 GB file
88.58 GB
Q6_K Current
103.96 GB
Q1_M 123 GB file
127.02 GB
Q1_S 123 GB file
127.02 GB
Q8_0 130.69 GB file
134.71 GB
BF16 246 GB file
250.02 GB
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Frequently Asked Questions

Can I run ML2-123B-Magnum-Diamond-GGUF Q6_K on a consumer GPU?
Running ML2-123B-Magnum-Diamond-GGUF Q6_K locally requires 103.96 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 ML2-123B-Magnum-Diamond-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 Q6_K quality good enough for production?
Q6_K is suitable for specialized use cases. Check community benchmarks for specific quality metrics.