Anthracite Org Q2_0 123B Parameters

VRAM Requirements for
magnum-v2-123b-gguf Q2_0

To run magnum-v2-123b-gguf locally at Q2_0 quantization, you need at minimum 42.46 GB of GPU VRAM.

42.46 GB
Required VRAM
38.44 GB
File Size
8K tokens
Context Window
123B
Parameters
Estimated VRAM Required
42.46
GB
Prosumer / Workstation
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

Budget $1,200 – $1,800

2× RTX 3090 (48GB total)

48 GB

Dual-GPU via tensor parallelism. Best cost per GB at this tier.

Balanced $4,000 – $5,500

NVIDIA A6000 (48GB)

48 GB

Single-card 48GB pro GPU. Clean setup, no multi-GPU overhead.

⚠️ 2.5 GB short
Ultimate $8,000 – $12,000

NVIDIA A100 40GB SXM

40 GB HBM2e

Data-centre HBM2e bandwidth. Dramatically faster throughput.

📊 VRAM Calculation Breakdown

Model File Size (Q2_0) 38.44 GB
Context Overhead (8,192 tokens × 123B × 2 ÷ 1M) 2.015 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 42.46 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 Current
42.46 GB
Q3_0 53.81 GB file
57.83 GB
Q4_0 69.19 GB file
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 Q2_0 on a consumer GPU?
Running magnum-v2-123b-gguf Q2_0 locally requires 42.46 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 Q2_0 quality good enough for production?
Q2_0 is suitable for specialized use cases. Check community benchmarks for specific quality metrics.