David A U Q5_K_M 48B Parameters

VRAM Requirements for
Qwen3-48B-A4B-Savant-Commander-GATED-12x-Closed-Open-Source-Distill-GGUF Q5_K_M

To run Qwen3-48B-A4B-Savant-Commander-GATED-12x-Closed-Open-Source-Distill-GGUF locally at Q5_K_M quantization, you need at minimum 35.79 GB of GPU VRAM.

35.79 GB
Required VRAM
33 GB
File Size
8K tokens
Context Window
48B
Parameters
Estimated VRAM Required
35.79
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 (Q5_K_M) 33 GB
Context Overhead (8,192 tokens × 48B × 2 ÷ 1M) 0.786 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 35.79 GB

Try a Different Quantization

Use the interactive calculator to compare Qwen3-48B-A4B-Savant-Commander-GATED-12x-Closed-Open-Source-Distill-GGUF across all available formats.

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Qwen3-48B-A4B-Savant-Commander-GATED-12x-Closed-Open-Source-Distill-GGUF — Other Quantizations

Q3_K_M 21 GB file
23.79 GB
Q4_K_M 27 GB file
29.79 GB
Q4_K_S 27 GB file
29.79 GB
Q4_XS 27 GB file
29.79 GB
Q5_K_M Current
35.79 GB
Q5_K_S 33 GB file
35.79 GB
Q6_K 39 GB file
41.79 GB
Q8_0 51 GB file
53.79 GB
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Frequently Asked Questions

Can I run Qwen3-48B-A4B-Savant-Commander-GATED-12x-Closed-Open-Source-Distill-GGUF Q5_K_M on a consumer GPU?
Running Qwen3-48B-A4B-Savant-Commander-GATED-12x-Closed-Open-Source-Distill-GGUF Q5_K_M locally requires 35.79 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 Qwen3-48B-A4B-Savant-Commander-GATED-12x-Closed-Open-Source-Distill-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_M quality good enough for production?
Q5_K_M is suitable for specialized use cases. Check community benchmarks for specific quality metrics.