Dev Quasar Q2_K 235B Parameters

VRAM Requirements for
chutesai.Qwen3-235B-A22B-Instruct-2507-1M-GGUF Q2_K

To run chutesai.Qwen3-235B-A22B-Instruct-2507-1M-GGUF locally at Q2_K quantization, you need at minimum 79.29 GB of GPU VRAM.

79.29 GB
Required VRAM
73.44 GB
File Size
8K tokens
Context Window
235B
Parameters
Estimated VRAM Required
79.29
GB
Data Centre Class
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 31.3 GB short
Budget $3,200 – $4,000

2× RTX 4090 (48GB) + aggressive quant

48 GB

Use a lower quantization to fit. Viable for testing at this scale.

Balanced $15,000 – $22,000

NVIDIA A100 80GB PCIe

80 GB HBM2e

Single-card 80GB. Industry-standard for large model inference.

Ultimate $25,000 – $40,000

NVIDIA H100 80GB SXM5

80 GB HBM3

State-of-the-art inference. 3× the bandwidth of A100.

📊 VRAM Calculation Breakdown

Model File Size (Q2_K) 73.44 GB
Context Overhead (8,192 tokens × 235B × 2 ÷ 1M) 3.85 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 79.29 GB

Try a Different Quantization

Use the interactive calculator to compare chutesai.Qwen3-235B-A22B-Instruct-2507-1M-GGUF across all available formats.

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chutesai.Qwen3-235B-A22B-Instruct-2507-1M-GGUF — Other Quantizations

Q2_K Current
79.29 GB
Q3_K_M 102.81 GB file
108.66 GB
Q4_K_M 132.19 GB file
138.04 GB
Q5_K_M 161.56 GB file
167.41 GB
Q6_K 190.94 GB file
196.79 GB
Q8_0 249.69 GB file
255.54 GB
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Frequently Asked Questions

Can I run chutesai.Qwen3-235B-A22B-Instruct-2507-1M-GGUF Q2_K on a consumer GPU?
Running chutesai.Qwen3-235B-A22B-Instruct-2507-1M-GGUF Q2_K locally requires 79.29 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 chutesai.Qwen3-235B-A22B-Instruct-2507-1M-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_K quality good enough for production?
Q2_K is suitable for specialized use cases. Check community benchmarks for specific quality metrics.