754Geg Q1_S 22B Parameters

VRAM Requirements for
kpok Q1_S

To run kpok locally at Q1_S quantization, you need at minimum 24.36 GB of GPU VRAM.

24.36 GB
Required VRAM
22 GB
File Size
8K tokens
Context Window
22B
Parameters
Estimated VRAM Required
24.36
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.

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 (Q1_S) 22 GB
Context Overhead (8,192 tokens × 22B × 2 ÷ 1M) 0.36 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 24.36 GB

Try a Different Quantization

Use the interactive calculator to compare kpok across all available formats.

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kpok — Other Quantizations

Q1_S Current
24.36 GB
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Frequently Asked Questions

Can I run kpok Q1_S on a consumer GPU?
Running kpok Q1_S locally requires 24.36 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 kpok?
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 Q1_S quality good enough for production?
Q1_S is suitable for specialized use cases. Check community benchmarks for specific quality metrics.