Featherless Ai Quants Q4_XS 41B Parameters

VRAM Requirements for
besimray-miner_id_3_72df7d62-e0d6-41b2-9153-9843320d6b82_1729802122-GGUF Q4_XS

To run besimray-miner_id_3_72df7d62-e0d6-41b2-9153-9843320d6b82_1729802122-GGUF locally at Q4_XS quantization, you need at minimum 25.73 GB of GPU VRAM.

25.73 GB
Required VRAM
23.06 GB
File Size
8K tokens
Context Window
41B
Parameters
Estimated VRAM Required
25.73
GB
Prosumer / Workstation
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

Budget $1,200 – $1,800

2× RTX 3090 (48GB total)

48 GB

Dual-GPU via tensor parallelism. Best cost per GB at this tier.

Balanced $4,000 – $5,500

NVIDIA A6000 (48GB)

48 GB

Single-card 48GB pro GPU. Clean setup, no multi-GPU overhead.

Ultimate $8,000 – $12,000

NVIDIA A100 40GB SXM

40 GB HBM2e

Data-centre HBM2e bandwidth. Dramatically faster throughput.

📊 VRAM Calculation Breakdown

Model File Size (Q4_XS) 23.06 GB
Context Overhead (8,192 tokens × 41B × 2 ÷ 1M) 0.672 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 25.73 GB

Try a Different Quantization

Use the interactive calculator to compare besimray-miner_id_3_72df7d62-e0d6-41b2-9153-9843320d6b82_1729802122-GGUF across all available formats.

Open Live Calculator →

besimray-miner_id_3_72df7d62-e0d6-41b2-9153-9843320d6b82_1729802122-GGUF — Other Quantizations

Q2_K 12.81 GB file
15.48 GB
Q3_K_L 17.94 GB file
20.61 GB
Q3_K_M 17.94 GB file
20.61 GB
Q3_K_S 17.94 GB file
20.61 GB
Q4_K_M 23.06 GB file
25.73 GB
Q4_K_S 23.06 GB file
25.73 GB
Q4_XS Current
25.73 GB
Q5_K_M 28.19 GB file
30.86 GB
Q5_K_S 28.19 GB file
30.86 GB
Q6_K 33.31 GB file
35.98 GB
Q8_0 43.56 GB file
46.23 GB
Advertisement Zone

Frequently Asked Questions

Can I run besimray-miner_id_3_72df7d62-e0d6-41b2-9153-9843320d6b82_1729802122-GGUF Q4_XS on a consumer GPU?
Running besimray-miner_id_3_72df7d62-e0d6-41b2-9153-9843320d6b82_1729802122-GGUF Q4_XS locally requires 25.73 GB VRAM, which exceeds consumer GPUs. You'll need prosumer cards like the NVIDIA A6000 (48GB) or an A100 (80GB).
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 besimray-miner_id_3_72df7d62-e0d6-41b2-9153-9843320d6b82_1729802122-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_XS 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.