Xythic K Q6_0 30B Parameters

VRAM Requirements for
Plano-Orchestrator-30B-A3B-GGUF Q6_0

To run Plano-Orchestrator-30B-A3B-GGUF locally at Q6_0 quantization, you need at minimum 26.87 GB of GPU VRAM.

26.87 GB
Required VRAM
24.38 GB
File Size
8K tokens
Context Window
30B
Parameters
Estimated VRAM Required
26.87
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 (Q6_0) 24.38 GB
Context Overhead (8,192 tokens × 30B × 2 ÷ 1M) 0.492 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 26.87 GB

Try a Different Quantization

Use the interactive calculator to compare Plano-Orchestrator-30B-A3B-GGUF across all available formats.

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Plano-Orchestrator-30B-A3B-GGUF — Other Quantizations

Q2_0 9.38 GB file
11.87 GB
Q3_0 13.13 GB file
15.62 GB
Q4_0 16.88 GB file
19.37 GB
Q5_0 20.63 GB file
23.12 GB
Q6_0 Current
26.87 GB
Q8_0 31.88 GB file
34.37 GB
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Frequently Asked Questions

Can I run Plano-Orchestrator-30B-A3B-GGUF Q6_0 on a consumer GPU?
Running Plano-Orchestrator-30B-A3B-GGUF Q6_0 locally requires 26.87 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 Plano-Orchestrator-30B-A3B-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 Q6_0 quality good enough for production?
Q6_0 is suitable for specialized use cases. Check community benchmarks for specific quality metrics.