Ubergarm Q4_K 0528B Parameters

VRAM Requirements for
DeepSeek-R1-0528-GGUF Q4_K

To run DeepSeek-R1-0528-GGUF locally at Q4_K quantization, you need at minimum 307.65 GB of GPU VRAM.

307.65 GB
Required VRAM
297 GB
File Size
8K tokens
Context Window
0528B
Parameters
Estimated VRAM Required
307.65
GB
Cluster Required
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 147.6 GB short
Budget $40,000+

4× A100 40GB Cluster

160 GB

Distributed inference across 4 A100s. Minimum viable cluster.

⚠️ 147.6 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_K) 297 GB
Context Overhead (8,192 tokens × 0528B × 2 ÷ 1M) 8.651 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 307.65 GB

Try a Different Quantization

Use the interactive calculator to compare DeepSeek-R1-0528-GGUF across all available formats.

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DeepSeek-R1-0528-GGUF — Other Quantizations

Q2_K_R4 165 GB file
175.65 GB
Q3_K 231 GB file
241.65 GB
Q3_K_R4 231 GB file
241.65 GB
Q4_K Current
307.65 GB
Q1_S 528 GB file
538.65 GB
Q1_S_R4 528 GB file
538.65 GB
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Frequently Asked Questions

Can I run DeepSeek-R1-0528-GGUF Q4_K on a consumer GPU?
Running DeepSeek-R1-0528-GGUF Q4_K locally requires 307.65 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 DeepSeek-R1-0528-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_K 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.