Bartowski Q4_0 2024B Parameters

VRAM Requirements for
c4ai-command-r-08-2024-GGUF Q4_0

To run c4ai-command-r-08-2024-GGUF locally at Q4_0 quantization, you need at minimum 1173.66 GB of GPU VRAM.

1173.66 GB
Required VRAM
1138.5 GB
File Size
8K tokens
Context Window
2024B
Parameters
Estimated VRAM Required
1173.66
GB
Cluster Required
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 1,013.7 GB short
Budget $40,000+

4× A100 40GB Cluster

160 GB

Distributed inference across 4 A100s. Minimum viable cluster.

⚠️ 1,013.7 GB short
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 (Q4_0) 1138.5 GB
Context Overhead (8,192 tokens × 2024B × 2 ÷ 1M) 33.161 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 1173.66 GB

Try a Different Quantization

Use the interactive calculator to compare c4ai-command-r-08-2024-GGUF across all available formats.

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c4ai-command-r-08-2024-GGUF — Other Quantizations

Q2_K_L 632.5 GB file
667.66 GB
Q2_M 632.5 GB file
667.66 GB
Q2_S 632.5 GB file
667.66 GB
Q2_XS 632.5 GB file
667.66 GB
Q2_K 632.5 GB file
667.66 GB
Q3_K_L 885.5 GB file
920.66 GB
Q3_K_M 885.5 GB file
920.66 GB
Q3_K_S 885.5 GB file
920.66 GB
Q3_K_XL 885.5 GB file
920.66 GB
Q3_M 885.5 GB file
920.66 GB
Q3_XS 885.5 GB file
920.66 GB
Q4_0 Current
1173.66 GB
Q4_XS 1138.5 GB file
1173.66 GB
Q4_K_S 1138.5 GB file
1173.66 GB
Q4_K_M 1138.5 GB file
1173.66 GB
Q4_K_L 1138.5 GB file
1173.66 GB
Q5_K_L 1391.5 GB file
1426.66 GB
Q5_K_M 1391.5 GB file
1426.66 GB
Q5_K_S 1391.5 GB file
1426.66 GB
Q6_K 1644.5 GB file
1679.66 GB
Q6_K_L 1644.5 GB file
1679.66 GB
Q8_0 2150.5 GB file
2185.66 GB
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Frequently Asked Questions

Can I run c4ai-command-r-08-2024-GGUF Q4_0 on a consumer GPU?
Running c4ai-command-r-08-2024-GGUF Q4_0 locally requires 1173.66 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 c4ai-command-r-08-2024-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_0 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.