Csabakecskemeti Q5_K_S 104B Parameters

VRAM Requirements for
MrRobotoAI.104-GGUF Q5_K_S

To run MrRobotoAI.104-GGUF locally at Q5_K_S quantization, you need at minimum 75.2 GB of GPU VRAM.

75.2 GB
Required VRAM
71.5 GB
File Size
8K tokens
Context Window
104B
Parameters
Estimated VRAM Required
75.2
GB
Data Centre Class
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 27.2 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 (Q5_K_S) 71.5 GB
Context Overhead (8,192 tokens × 104B × 2 ÷ 1M) 1.704 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 75.2 GB

Try a Different Quantization

Use the interactive calculator to compare MrRobotoAI.104-GGUF across all available formats.

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MrRobotoAI.104-GGUF — Other Quantizations

Q2_K 32.5 GB file
36.2 GB
Q3_K_L 45.5 GB file
49.2 GB
Q3_K_M 45.5 GB file
49.2 GB
Q3_K_S 45.5 GB file
49.2 GB
Q4_K_M 58.5 GB file
62.2 GB
Q4_K_S 58.5 GB file
62.2 GB
Q5_K_M 71.5 GB file
75.2 GB
Q5_K_S Current
75.2 GB
Q6_K 84.5 GB file
88.2 GB
Q8_0 110.5 GB file
114.2 GB
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Frequently Asked Questions

Can I run MrRobotoAI.104-GGUF Q5_K_S on a consumer GPU?
Running MrRobotoAI.104-GGUF Q5_K_S locally requires 75.2 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 MrRobotoAI.104-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_S quality good enough for production?
Q5_K_S is suitable for specialized use cases. Check community benchmarks for specific quality metrics.