Bartowski Q1_S 110B Parameters

VRAM Requirements for
airoboros-dpo-110b-3.3-GGUF Q1_S

To run airoboros-dpo-110b-3.3-GGUF locally at Q1_S quantization, you need at minimum 113.8 GB of GPU VRAM.

113.8 GB
Required VRAM
110 GB
File Size
8K tokens
Context Window
110B
Parameters
Estimated VRAM Required
113.8
GB
Cluster Required
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

Budget $40,000+

4× A100 40GB Cluster

160 GB

Distributed inference across 4 A100s. Minimum viable cluster.

Balanced $60,000+

2× H100 80GB NVLink

160 GB HBM3

NVLink bridge enables unified 160GB VRAM pool.

Ultimate $3,000,000+

NVIDIA GB200 NVL72

1.4 TB HBM3e

Next-gen Grace Blackwell Superchip. Built for frontier models.

📊 VRAM Calculation Breakdown

Model File Size (Q1_S) 110 GB
Context Overhead (8,192 tokens × 110B × 2 ÷ 1M) 1.802 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 113.8 GB

Try a Different Quantization

Use the interactive calculator to compare airoboros-dpo-110b-3.3-GGUF across all available formats.

Open Live Calculator →

airoboros-dpo-110b-3.3-GGUF — Other Quantizations

Q2_K 34.38 GB file
38.18 GB
Q2_M 34.38 GB file
38.18 GB
Q2_S 34.38 GB file
38.18 GB
Q2_XS 34.38 GB file
38.18 GB
Q2_XXS 34.38 GB file
38.18 GB
Q3_K_L 48.13 GB file
51.93 GB
Q3_K_M 48.13 GB file
51.93 GB
Q3_K_S 48.13 GB file
51.93 GB
Q3_M 48.13 GB file
51.93 GB
Q3_S 48.13 GB file
51.93 GB
Q3_XS 48.13 GB file
51.93 GB
Q3_XXS 48.13 GB file
51.93 GB
Q4_XS 61.88 GB file
65.68 GB
Q4_NL 61.88 GB file
65.68 GB
Q4_K_S 61.88 GB file
65.68 GB
Q4_K_M 61.88 GB file
65.68 GB
Q5_K_M 75.63 GB file
79.43 GB
Q5_K_S 75.63 GB file
79.43 GB
Q6_K 89.38 GB file
93.18 GB
Q1_S Current
113.8 GB
Q1_M 110 GB file
113.8 GB
Q8_0 116.88 GB file
120.68 GB
Advertisement Zone

Frequently Asked Questions

Can I run airoboros-dpo-110b-3.3-GGUF Q1_S on a consumer GPU?
Running airoboros-dpo-110b-3.3-GGUF Q1_S locally requires 113.8 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 airoboros-dpo-110b-3.3-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 Q1_S quality good enough for production?
Q1_S is suitable for specialized use cases. Check community benchmarks for specific quality metrics.