Asdwvv Q4_0 15.7B Parameters

VRAM Requirements for
kanana-1.5-15.7b-a3b-instruct-Q4_K_M-GGUF Q4_0

To run kanana-1.5-15.7b-a3b-instruct-Q4_K_M-GGUF locally at Q4_0 quantization, you need at minimum 11.09 GB of GPU VRAM.

11.09 GB
Required VRAM
8.83 GB
File Size
8K tokens
Context Window
15.7B
Parameters
Estimated VRAM Required
11.09
GB
Mid-Range GPU Required
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 1.1 GB short
Budget $350 – $500

RTX 3080 (10GB)

10 GB

Used market gem. Tight on VRAM but viable for this workload.

Balanced $699 – $799

RTX 4070 Ti (12GB)

12 GB

Strong inference GPU. Handles 7-13B models comfortably.

Ultimate $1,599 – $1,999

RTX 4090 (24GB)

24 GB

Best consumer GPU. Breeze through 13B models at any quantization.

📊 VRAM Calculation Breakdown

Model File Size (Q4_0) 8.83 GB
Context Overhead (8,192 tokens × 15.7B × 2 ÷ 1M) 0.257 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 11.09 GB

Try a Different Quantization

Use the interactive calculator to compare kanana-1.5-15.7b-a3b-instruct-Q4_K_M-GGUF across all available formats.

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kanana-1.5-15.7b-a3b-instruct-Q4_K_M-GGUF — Other Quantizations

Q4_0 Current
11.09 GB
FP16 31.4 GB file
33.66 GB
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Frequently Asked Questions

Can I run kanana-1.5-15.7b-a3b-instruct-Q4_K_M-GGUF Q4_0 on a consumer GPU?
Yes! At 11.09 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 kanana-1.5-15.7b-a3b-instruct-Q4_K_M-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.