Ringo Systems Q5_K_M 27B Parameters

VRAM Requirements for
RingoLLM Q5_K_M

To run RingoLLM locally at Q5_K_M quantization, you need at minimum 21 GB of GPU VRAM.

21 GB
Required VRAM
18.56 GB
File Size
8K tokens
Context Window
27B
Parameters
Estimated VRAM Required
21
GB
High-End Consumer GPU
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 (Q5_K_M) 18.56 GB
Context Overhead (8,192 tokens × 27B × 2 ÷ 1M) 0.442 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 21 GB

Try a Different Quantization

Use the interactive calculator to compare RingoLLM across all available formats.

Open Live Calculator →

RingoLLM — Other Quantizations

Q4_K_M 15.19 GB file
17.63 GB
Q5_0 18.56 GB file
21 GB
Q5_K_M Current
21 GB
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Frequently Asked Questions

Can I run RingoLLM Q5_K_M on a consumer GPU?
Yes! At 21 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 RingoLLM?
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 Q5_K_M quality good enough for production?
Q5_K_M is suitable for specialized use cases. Check community benchmarks for specific quality metrics.