Anthracite Org Q4_0 123B Parameters

VRAM Requirements for
magnum-v2-123b-gguf Q4_0

To run magnum-v2-123b-gguf locally at Q4_0 quantization, you need at minimum 73.21 GB of GPU VRAM.

73.21 GB
Required VRAM
69.19 GB
File Size
8K tokens
Context Window
123B
Parameters
Estimated VRAM Required
73.21
GB
Data Centre Class
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 25.2 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 (Q4_0) 69.19 GB
Context Overhead (8,192 tokens × 123B × 2 ÷ 1M) 2.015 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 73.21 GB

Try a Different Quantization

Use the interactive calculator to compare magnum-v2-123b-gguf across all available formats.

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magnum-v2-123b-gguf — Other Quantizations

Q2_0 38.44 GB file
42.46 GB
Q3_0 53.81 GB file
57.83 GB
Q4_0 Current
73.21 GB
Q5_0 84.56 GB file
88.58 GB
Q6_0 99.94 GB file
103.96 GB
Q1_0 123 GB file
127.02 GB
FP16 246 GB file
250.02 GB
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Frequently Asked Questions

Can I run magnum-v2-123b-gguf Q4_0 on a consumer GPU?
Running magnum-v2-123b-gguf Q4_0 locally requires 73.21 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 magnum-v2-123b-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 Q4_0 quality good enough for production?
Q4_K_M is an excellent balance of quality and performance. Perplexity tests show minimal degradation (< 2%) vs FP16 for most models. Suitable for most production applications.