Ykarout Q4_0 671B Parameters

VRAM Requirements for
phi4-deepseek-r1-distilled-gguf-v5 Q4_0

To run phi4-deepseek-r1-distilled-gguf-v5 locally at Q4_0 quantization, you need at minimum 390.43 GB of GPU VRAM.

390.43 GB
Required VRAM
377.44 GB
File Size
8K tokens
Context Window
671B
Parameters
Estimated VRAM Required
390.43
GB
Cluster Required
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 230.4 GB short
Budget $40,000+

4× A100 40GB Cluster

160 GB

Distributed inference across 4 A100s. Minimum viable cluster.

⚠️ 230.4 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) 377.44 GB
Context Overhead (8,192 tokens × 671B × 2 ÷ 1M) 10.994 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 390.43 GB

Try a Different Quantization

Use the interactive calculator to compare phi4-deepseek-r1-distilled-gguf-v5 across all available formats.

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phi4-deepseek-r1-distilled-gguf-v5 — Other Quantizations

Q4_0 Current
390.43 GB
Q8_0 712.94 GB file
725.93 GB
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Frequently Asked Questions

Can I run phi4-deepseek-r1-distilled-gguf-v5 Q4_0 on a consumer GPU?
Running phi4-deepseek-r1-distilled-gguf-v5 Q4_0 locally requires 390.43 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 phi4-deepseek-r1-distilled-gguf-v5?
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.