Roleplaiapp Q3_XS 165B Parameters

VRAM Requirements for
DeepSeek-R1-Distill-Llama-3.1-16.5B-Brainstorm-i1-IQ3_XS-GGUF Q3_XS

To run DeepSeek-R1-Distill-Llama-3.1-16.5B-Brainstorm-i1-IQ3_XS-GGUF locally at Q3_XS quantization, you need at minimum 76.89 GB of GPU VRAM.

76.89 GB
Required VRAM
72.19 GB
File Size
8K tokens
Context Window
165B
Parameters
Estimated VRAM Required
76.89
GB
Data Centre Class
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 28.9 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 (Q3_XS) 72.19 GB
Context Overhead (8,192 tokens × 165B × 2 ÷ 1M) 2.703 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 76.89 GB

Try a Different Quantization

Use the interactive calculator to compare DeepSeek-R1-Distill-Llama-3.1-16.5B-Brainstorm-i1-IQ3_XS-GGUF across all available formats.

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DeepSeek-R1-Distill-Llama-3.1-16.5B-Brainstorm-i1-IQ3_XS-GGUF — Other Quantizations

Q3_XS Current
76.89 GB
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Frequently Asked Questions

Can I run DeepSeek-R1-Distill-Llama-3.1-16.5B-Brainstorm-i1-IQ3_XS-GGUF Q3_XS on a consumer GPU?
Running DeepSeek-R1-Distill-Llama-3.1-16.5B-Brainstorm-i1-IQ3_XS-GGUF Q3_XS locally requires 76.89 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 DeepSeek-R1-Distill-Llama-3.1-16.5B-Brainstorm-i1-IQ3_XS-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 Q3_XS quality good enough for production?
Q3_XS is suitable for specialized use cases. Check community benchmarks for specific quality metrics.