Unsloth Q2_K 405B Parameters

VRAM Requirements for
cogito-v2-preview-llama-405B-GGUF Q2_K

To run cogito-v2-preview-llama-405B-GGUF locally at Q2_K quantization, you need at minimum 135.2 GB of GPU VRAM.

135.2 GB
Required VRAM
126.56 GB
File Size
8K tokens
Context Window
405B
Parameters
Estimated VRAM Required
135.2
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 (Q2_K) 126.56 GB
Context Overhead (8,192 tokens × 405B × 2 ÷ 1M) 6.636 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 135.2 GB

Try a Different Quantization

Use the interactive calculator to compare cogito-v2-preview-llama-405B-GGUF across all available formats.

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cogito-v2-preview-llama-405B-GGUF — Other Quantizations

Q2_K Current
135.2 GB
Q2_K_L 126.56 GB file
135.2 GB
Q2_K_XL 126.56 GB file
135.2 GB
Q2_M 126.56 GB file
135.2 GB
Q2_XXS 126.56 GB file
135.2 GB
Q3_K_M 177.19 GB file
185.83 GB
Q3_K_S 177.19 GB file
185.83 GB
Q3_K_XL 177.19 GB file
185.83 GB
Q3_XXS 177.19 GB file
185.83 GB
Q4_XS 227.81 GB file
236.45 GB
Q4_NL 227.81 GB file
236.45 GB
Q4_K_XL 227.81 GB file
236.45 GB
Q4_K_S 227.81 GB file
236.45 GB
Q4_K_M 227.81 GB file
236.45 GB
Q4_1 227.81 GB file
236.45 GB
Q4_0 227.81 GB file
236.45 GB
Q5_K_M 278.44 GB file
287.08 GB
Q5_K_S 278.44 GB file
287.08 GB
Q5_K_XL 278.44 GB file
287.08 GB
Q6_K 329.06 GB file
337.7 GB
Q6_K_XL 329.06 GB file
337.7 GB
Q1_M 405 GB file
413.64 GB
Q1_S 405 GB file
413.64 GB
Q8_0 430.31 GB file
438.95 GB
Q8_K_XL 430.31 GB file
438.95 GB
BF16 810 GB file
818.64 GB
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Frequently Asked Questions

Can I run cogito-v2-preview-llama-405B-GGUF Q2_K on a consumer GPU?
Running cogito-v2-preview-llama-405B-GGUF Q2_K locally requires 135.2 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 cogito-v2-preview-llama-405B-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.