Featherless Ai Quants Q2_K 50B Parameters

VRAM Requirements for
c1park-20240105_mistral-step50-GGUF Q2_K

To run c1park-20240105_mistral-step50-GGUF locally at Q2_K quantization, you need at minimum 18.45 GB of GPU VRAM.

18.45 GB
Required VRAM
15.63 GB
File Size
8K tokens
Context Window
50B
Parameters
Estimated VRAM Required
18.45
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 (Q2_K) 15.63 GB
Context Overhead (8,192 tokens × 50B × 2 ÷ 1M) 0.819 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 18.45 GB

Try a Different Quantization

Use the interactive calculator to compare c1park-20240105_mistral-step50-GGUF across all available formats.

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c1park-20240105_mistral-step50-GGUF — Other Quantizations

Q2_K Current
18.45 GB
Q3_K_L 21.88 GB file
24.7 GB
Q3_K_M 21.88 GB file
24.7 GB
Q3_K_S 21.88 GB file
24.7 GB
Q4_K_M 28.13 GB file
30.95 GB
Q4_K_S 28.13 GB file
30.95 GB
Q4_XS 28.13 GB file
30.95 GB
Q5_K_M 34.38 GB file
37.2 GB
Q5_K_S 34.38 GB file
37.2 GB
Q6_K 40.63 GB file
43.45 GB
Q8_0 53.13 GB file
55.95 GB
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Frequently Asked Questions

Can I run c1park-20240105_mistral-step50-GGUF Q2_K on a consumer GPU?
Yes! At 18.45 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 c1park-20240105_mistral-step50-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 Q2_K quality good enough for production?
Q2_K is suitable for specialized use cases. Check community benchmarks for specific quality metrics.