Vibhansh Q4_XS 8B Parameters

VRAM Requirements for
Veda-8B-v1-Cognitive Q4_XS

To run Veda-8B-v1-Cognitive locally at Q4_XS quantization, you need at minimum 6.63 GB of GPU VRAM.

6.63 GB
Required VRAM
4.5 GB
File Size
8K tokens
Context Window
8B
Parameters
Estimated VRAM Required
6.63
GB
Consumer Friendly
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

Budget $350 – $500

RTX 3080 (10GB)

10 GB

Used market gem. Tight on VRAM but viable for this workload.

Balanced $699 – $799

RTX 4070 Ti (12GB)

12 GB

Strong inference GPU. Handles 7-13B models comfortably.

Ultimate $1,599 – $1,999

RTX 4090 (24GB)

24 GB

Best consumer GPU. Breeze through 13B models at any quantization.

📊 VRAM Calculation Breakdown

Model File Size (Q4_XS) 4.5 GB
Context Overhead (8,192 tokens × 8B × 2 ÷ 1M) 0.131 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 6.63 GB

Try a Different Quantization

Use the interactive calculator to compare Veda-8B-v1-Cognitive across all available formats.

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Veda-8B-v1-Cognitive — Other Quantizations

Q3_K_M 3.5 GB file
5.63 GB
Q4_K_M 4.5 GB file
6.63 GB
Q4_XS Current
6.63 GB
Q8_0 8.5 GB file
10.63 GB
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Frequently Asked Questions

Can I run Veda-8B-v1-Cognitive Q4_XS on a consumer GPU?
Yes! At 6.63 GB VRAM required, a single high-end consumer GPU like the RTX 4090 (24GB) can handle this workload. You can also use multiple GPUs for tensor parallelism.
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 Veda-8B-v1-Cognitive?
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_XS 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.