Bartowski Q2_XXS 72B Parameters

VRAM Requirements for
Chuluun-Qwen2.5-72B-v0.08-GGUF Q2_XXS

To run Chuluun-Qwen2.5-72B-v0.08-GGUF locally at Q2_XXS quantization, you need at minimum 25.68 GB of GPU VRAM.

25.68 GB
Required VRAM
22.5 GB
File Size
8K tokens
Context Window
72B
Parameters
Estimated VRAM Required
25.68
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.

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 (Q2_XXS) 22.5 GB
Context Overhead (8,192 tokens × 72B × 2 ÷ 1M) 1.18 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 25.68 GB

Try a Different Quantization

Use the interactive calculator to compare Chuluun-Qwen2.5-72B-v0.08-GGUF across all available formats.

Open Live Calculator →

Chuluun-Qwen2.5-72B-v0.08-GGUF — Other Quantizations

Q2_K 22.5 GB file
25.68 GB
Q2_K_L 22.5 GB file
25.68 GB
Q2_M 22.5 GB file
25.68 GB
Q2_S 22.5 GB file
25.68 GB
Q2_XS 22.5 GB file
25.68 GB
Q2_XXS Current
25.68 GB
Q3_M 31.5 GB file
34.68 GB
Q3_XXS 31.5 GB file
34.68 GB
Q3_K_XL 31.5 GB file
34.68 GB
Q3_K_S 31.5 GB file
34.68 GB
Q3_K_M 31.5 GB file
34.68 GB
Q3_K_L 31.5 GB file
34.68 GB
Q4_0 40.5 GB file
43.68 GB
Q4_1 40.5 GB file
43.68 GB
Q4_K_M 40.5 GB file
43.68 GB
Q4_K_S 40.5 GB file
43.68 GB
Q4_NL 40.5 GB file
43.68 GB
Q4_XS 40.5 GB file
43.68 GB
Q5_K_M 49.5 GB file
52.68 GB
Q5_K_S 49.5 GB file
52.68 GB
Q6_K 58.5 GB file
61.68 GB
Q1_M 72 GB file
75.18 GB
Q8_0 76.5 GB file
79.68 GB
Advertisement Zone

Frequently Asked Questions

Can I run Chuluun-Qwen2.5-72B-v0.08-GGUF Q2_XXS on a consumer GPU?
Running Chuluun-Qwen2.5-72B-v0.08-GGUF Q2_XXS locally requires 25.68 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 Chuluun-Qwen2.5-72B-v0.08-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 Q2_XXS quality good enough for production?
Q2_XXS is suitable for specialized use cases. Check community benchmarks for specific quality metrics.