Bartowski Q2_XS 78B Parameters

VRAM Requirements for
calme-3.1-instruct-78b-GGUF Q2_XS

To run calme-3.1-instruct-78b-GGUF locally at Q2_XS quantization, you need at minimum 27.66 GB of GPU VRAM.

27.66 GB
Required VRAM
24.38 GB
File Size
8K tokens
Context Window
78B
Parameters
Estimated VRAM Required
27.66
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 (Q2_XS) 24.38 GB
Context Overhead (8,192 tokens × 78B × 2 ÷ 1M) 1.278 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 27.66 GB

Try a Different Quantization

Use the interactive calculator to compare calme-3.1-instruct-78b-GGUF across all available formats.

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calme-3.1-instruct-78b-GGUF — Other Quantizations

Q2_K 24.38 GB file
27.66 GB
Q2_K_L 24.38 GB file
27.66 GB
Q2_M 24.38 GB file
27.66 GB
Q2_XS Current
27.66 GB
Q2_XXS 24.38 GB file
27.66 GB
Q3_M 34.13 GB file
37.41 GB
Q3_XXS 34.13 GB file
37.41 GB
Q3_K_XL 34.13 GB file
37.41 GB
Q3_K_S 34.13 GB file
37.41 GB
Q3_K_M 34.13 GB file
37.41 GB
Q3_K_L 34.13 GB file
37.41 GB
Q4_0 43.88 GB file
47.16 GB
Q4_K_M 43.88 GB file
47.16 GB
Q4_K_S 43.88 GB file
47.16 GB
Q4_XS 43.88 GB file
47.16 GB
Q5_K_M 53.63 GB file
56.91 GB
Q5_K_S 53.63 GB file
56.91 GB
Q6_K 63.38 GB file
66.66 GB
Q1_M 78 GB file
81.28 GB
Q8_0 82.88 GB file
86.16 GB
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Frequently Asked Questions

Can I run calme-3.1-instruct-78b-GGUF Q2_XS on a consumer GPU?
Running calme-3.1-instruct-78b-GGUF Q2_XS locally requires 27.66 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 calme-3.1-instruct-78b-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_XS quality good enough for production?
Q2_XS is suitable for specialized use cases. Check community benchmarks for specific quality metrics.