J Thomas Co E Q4_K_M 14B Parameters

VRAM Requirements for
coe-gemma4-law-mmlu_pro-14b-a4b-q4 Q4_K_M

To run coe-gemma4-law-mmlu_pro-14b-a4b-q4 locally at Q4_K_M quantization, you need at minimum 10.11 GB of GPU VRAM.

10.11 GB
Required VRAM
7.88 GB
File Size
8K tokens
Context Window
14B
Parameters
Estimated VRAM Required
10.11
GB
Mid-Range GPU Required
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 0.1 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_K_M) 7.88 GB
Context Overhead (8,192 tokens × 14B × 2 ÷ 1M) 0.229 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 10.11 GB

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coe-gemma4-law-mmlu_pro-14b-a4b-q4 — Other Quantizations

Q4_K_M Current
10.11 GB
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Frequently Asked Questions

Can I run coe-gemma4-law-mmlu_pro-14b-a4b-q4 Q4_K_M on a consumer GPU?
Yes! At 10.11 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 coe-gemma4-law-mmlu_pro-14b-a4b-q4?
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_M 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.