How much VRAM do I need for phi-4-deepseek-r1-distilled-gguf Q3_0?

You need 306.55 GB of VRAM to run phi-4-deepseek-r1-distilled-gguf at Q3_0 quantization. The model file is 293.56 GB with a context window of 8,192 tokens.

Ykarout Q3_0 671B Parameters

VRAM Requirements for
phi-4-deepseek-r1-distilled-gguf Q3_0

To run phi-4-deepseek-r1-distilled-gguf locally at Q3_0 quantization, you need at minimum 306.55 GB of GPU VRAM.

306.55 GB

Required VRAM

293.56 GB

File Size

8K tokens

Context Window

671B

Parameters

Estimated VRAM Required

306.55

Cluster Required

0 8GB
RTX 3060 16GB
RTX 3080 24GB
RTX 4090 48GB
A6000 80GB
A100 80GB+

Recommended GPU Configurations

⚠️ 146.6 GB short

Budget $40,000+

4× A100 40GB Cluster

160 GB

Distributed inference across 4 A100s. Minimum viable cluster.

⚠️ 146.6 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 (Q3_0) 293.56 GB

Context Overhead (8,192 tokens × 671B × 2 ÷ 1M) 10.994 GB

System Buffer (OS + CUDA runtime) 2.00 GB

Total Required VRAM 306.55 GB

Try a Different Quantization

Use the interactive calculator to compare phi-4-deepseek-r1-distilled-gguf across all available formats.

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phi-4-deepseek-r1-distilled-gguf — Other Quantizations

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Frequently Asked Questions

Can I run phi-4-deepseek-r1-distilled-gguf Q3_0 on a consumer GPU?

Running phi-4-deepseek-r1-distilled-gguf Q3_0 locally requires 306.55 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 phi-4-deepseek-r1-distilled-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 Q3_0 quality good enough for production?

Q3_0 is suitable for specialized use cases. Check community benchmarks for specific quality metrics.

VRAM Requirements for phi-4-deepseek-r1-distilled-gguf Q3_0