Dev Quasar Q2_K 40B Parameters

VRAM Requirements for
Kwaipilot.KAT-V1-40B-GGUF Q2_K

To run Kwaipilot.KAT-V1-40B-GGUF locally at Q2_K quantization, you need at minimum 15.16 GB of GPU VRAM.

15.16 GB
Required VRAM
12.5 GB
File Size
8K tokens
Context Window
40B
Parameters
Estimated VRAM Required
15.16
GB
Mid-Range GPU Required
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

Budget $600 – $900

Used RTX 3090 (24GB)

24 GB

Best used-market value for 24GB VRAM. Solid for 30B-class models.

Balanced $1,599 – $1,999

RTX 4090 (24GB)

24 GB

Fastest 24GB consumer GPU. Excellent for daily local inference.

Ultimate $2,500 – $3,500

NVIDIA A5000 (32GB)

32 GB

Pro workstation card with ECC memory. Maximum headroom at 24GB.

📊 VRAM Calculation Breakdown

Model File Size (Q2_K) 12.5 GB
Context Overhead (8,192 tokens × 40B × 2 ÷ 1M) 0.655 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 15.16 GB

Try a Different Quantization

Use the interactive calculator to compare Kwaipilot.KAT-V1-40B-GGUF across all available formats.

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Kwaipilot.KAT-V1-40B-GGUF — Other Quantizations

Q2_K Current
15.16 GB
Q3_K_L 17.5 GB file
20.16 GB
Q3_K_M 17.5 GB file
20.16 GB
Q3_K_S 17.5 GB file
20.16 GB
Q4_K_M 22.5 GB file
25.16 GB
Q4_K_S 22.5 GB file
25.16 GB
Q5_K_M 27.5 GB file
30.16 GB
Q5_K_S 27.5 GB file
30.16 GB
Q6_K 32.5 GB file
35.16 GB
Q8_0 42.5 GB file
45.16 GB
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Frequently Asked Questions

Can I run Kwaipilot.KAT-V1-40B-GGUF Q2_K on a consumer GPU?
Yes! At 15.16 GB VRAM required, a single high-end consumer GPU like the RTX 4090 (24GB) can handle this workload. You can also use multiple GPUs for tensor parallelism.
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 Kwaipilot.KAT-V1-40B-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.