Unsloth Q4_K_XL 15B Parameters

VRAM Requirements for
Apriel-1.5-15b-Thinker-GGUF Q4_K_XL

To run Apriel-1.5-15b-Thinker-GGUF locally at Q4_K_XL quantization, you need at minimum 10.69 GB of GPU VRAM.

10.69 GB
Required VRAM
8.44 GB
File Size
8K tokens
Context Window
15B
Parameters
Estimated VRAM Required
10.69
GB
Mid-Range GPU Required
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 0.7 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_K_XL) 8.44 GB
Context Overhead (8,192 tokens × 15B × 2 ÷ 1M) 0.246 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 10.69 GB

Try a Different Quantization

Use the interactive calculator to compare Apriel-1.5-15b-Thinker-GGUF across all available formats.

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Apriel-1.5-15b-Thinker-GGUF — Other Quantizations

Q2_K 4.69 GB file
6.94 GB
Q2_K_L 4.69 GB file
6.94 GB
Q2_K_XL 4.69 GB file
6.94 GB
Q2_M 4.69 GB file
6.94 GB
Q2_XXS 4.69 GB file
6.94 GB
Q3_K_M 6.56 GB file
8.81 GB
Q3_K_S 6.56 GB file
8.81 GB
Q3_K_XL 6.56 GB file
8.81 GB
Q3_XXS 6.56 GB file
8.81 GB
Q4_XS 8.44 GB file
10.69 GB
Q4_NL 8.44 GB file
10.69 GB
Q4_K_XL Current
10.69 GB
Q4_K_S 8.44 GB file
10.69 GB
Q4_K_M 8.44 GB file
10.69 GB
Q4_1 8.44 GB file
10.69 GB
Q4_0 8.44 GB file
10.69 GB
Q5_K_M 10.31 GB file
12.56 GB
Q5_K_S 10.31 GB file
12.56 GB
Q5_K_XL 10.31 GB file
12.56 GB
Q6_K 12.19 GB file
14.44 GB
Q6_K_XL 12.19 GB file
14.44 GB
Q1_M 15 GB file
17.25 GB
Q1_S 15 GB file
17.25 GB
Q8_0 15.94 GB file
18.19 GB
Q8_K_XL 15.94 GB file
18.19 GB
BF16 30 GB file
32.25 GB
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Frequently Asked Questions

Can I run Apriel-1.5-15b-Thinker-GGUF Q4_K_XL on a consumer GPU?
Yes! At 10.69 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 Apriel-1.5-15b-Thinker-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_K_XL 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.