Second State Q4_0 72B Parameters

VRAM Requirements for
Liberated-Qwen1.5-72B-GGUF Q4_0

To run Liberated-Qwen1.5-72B-GGUF locally at Q4_0 quantization, you need at minimum 43.68 GB of GPU VRAM.

43.68 GB
Required VRAM
40.5 GB
File Size
8K tokens
Context Window
72B
Parameters
Estimated VRAM Required
43.68
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.

⚠️ 3.7 GB short
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 (Q4_0) 40.5 GB
Context Overhead (8,192 tokens × 72B × 2 ÷ 1M) 1.18 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 43.68 GB

Try a Different Quantization

Use the interactive calculator to compare Liberated-Qwen1.5-72B-GGUF across all available formats.

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Liberated-Qwen1.5-72B-GGUF — Other Quantizations

Q2_K 22.5 GB file
25.68 GB
Q3_K_L 31.5 GB file
34.68 GB
Q3_K_M 31.5 GB file
34.68 GB
Q3_K_S 31.5 GB file
34.68 GB
Q4_0 Current
43.68 GB
Q4_K_M 40.5 GB file
43.68 GB
Q4_K_S 40.5 GB file
43.68 GB
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Frequently Asked Questions

Can I run Liberated-Qwen1.5-72B-GGUF Q4_0 on a consumer GPU?
Running Liberated-Qwen1.5-72B-GGUF Q4_0 locally requires 43.68 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 Liberated-Qwen1.5-72B-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 Q4_0 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.