Theprint Q4_1 0126B Parameters

VRAM Requirements for
theprint-moe-8x3-0126-GGUF Q4_1

To run theprint-moe-8x3-0126-GGUF locally at Q4_1 quantization, you need at minimum 74.94 GB of GPU VRAM.

74.94 GB
Required VRAM
70.88 GB
File Size
8K tokens
Context Window
0126B
Parameters
Estimated VRAM Required
74.94
GB
Data Centre Class
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 26.9 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_1) 70.88 GB
Context Overhead (8,192 tokens × 0126B × 2 ÷ 1M) 2.064 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 74.94 GB

Try a Different Quantization

Use the interactive calculator to compare theprint-moe-8x3-0126-GGUF across all available formats.

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theprint-moe-8x3-0126-GGUF — Other Quantizations

Q2_0 39.38 GB file
43.44 GB
Q3_0 55.13 GB file
59.19 GB
Q4_0 70.88 GB file
74.94 GB
Q4_1 Current
74.94 GB
Q5_0 86.63 GB file
90.69 GB
Q6_0 102.38 GB file
106.44 GB
Q8_0 133.88 GB file
137.94 GB
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Frequently Asked Questions

Can I run theprint-moe-8x3-0126-GGUF Q4_1 on a consumer GPU?
Running theprint-moe-8x3-0126-GGUF Q4_1 locally requires 74.94 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 theprint-moe-8x3-0126-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_1 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.