Logihertzsystems Q1_M 7B Parameters

VRAM Requirements for
Nyra-Core-7B-GGUF-IQ1_M Q1_M

To run Nyra-Core-7B-GGUF-IQ1_M locally at Q1_M quantization, you need at minimum 9.11 GB of GPU VRAM.

9.11 GB
Required VRAM
7 GB
File Size
8K tokens
Context Window
7B
Parameters
Estimated VRAM Required
9.11
GB
Mid-Range GPU Required
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

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 (Q1_M) 7 GB
Context Overhead (8,192 tokens × 7B × 2 ÷ 1M) 0.115 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 9.11 GB

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Use the interactive calculator to compare Nyra-Core-7B-GGUF-IQ1_M across all available formats.

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Nyra-Core-7B-GGUF-IQ1_M — Other Quantizations

Q1_M Current
9.11 GB
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Frequently Asked Questions

Can I run Nyra-Core-7B-GGUF-IQ1_M Q1_M on a consumer GPU?
Yes! At 9.11 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 Nyra-Core-7B-GGUF-IQ1_M?
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 Q1_M quality good enough for production?
Q1_M is suitable for specialized use cases. Check community benchmarks for specific quality metrics.