ai-gpu
Microsoft Phi-4 VRAM Requirements
Calculate GPU VRAM needed to run Microsoft Phi-4 (14B). At FP16 it needs ~28 GB — INT4 brings it down to ~7 GB, running on a single RTX 4070.
Microsoft Phi-4: Efficient 14B Inference
Phi-4 is Microsoft's 14B parameter model trained on high-quality synthetic data. It punches well above its weight class in reasoning and coding benchmarks.
VRAM by Quantization
| Quantization | VRAM needed | Minimum GPU |
|---|---|---|
| FP16 | ~28 GB | A100 40 GB |
| INT8 | ~14 GB | RTX 4090 (tight) |
| INT4 / GGUF Q4 | ~7 GB | RTX 3070 / 4070 |
| GGUF Q4_K_M | ~8 GB | RTX 3070 / 4070 |
Why Phi-4 is Efficient
Phi-4 was trained on carefully curated synthetic data rather than raw web scrapes. This allows it to achieve near-70B performance on many benchmarks at 14B parameter count — meaning you get strong results at RTX-class GPU costs.
Running on Kubernetes
# Single RTX 4090 for INT4
resources:
limits:
nvidia.com/gpu: 1With Ollama:
``bash
ollama run phi4
``
With vLLM:
``bash
vllm serve microsoft/phi-4 --quantization awq
``
Key Terms
Full glossary →VRAM (Video RAM)
Memory on a GPU used to store model weights, activations, and KV cache during LLM inference. VRAM is the primary constraint when running large language models locally.
Quantization
A technique to reduce model memory usage by representing weights in lower precision (INT8, INT4, GGUF-Q4). Quantization trades a small accuracy loss for significant VRAM reduction.
Frequently Asked Questions
What GPU can run Phi-4 at full precision (FP16)?
At FP16, Phi-4 14B requires ~28 GB VRAM. An RTX 4090 (24 GB) is slightly too small — you need an A100 40 GB, RTX 6000 Ada (48 GB), or a cloud A100. At INT4 (~7 GB), it runs comfortably on an RTX 3070 or 4070.
How does Phi-4 compare to other 14B models in VRAM?
Phi-4 14B has the same VRAM requirements as any other 14B model — about 28 GB at FP16 and 7 GB at INT4. What distinguishes Phi-4 is its performance-per-parameter ratio: it outperforms many 70B models on reasoning tasks despite needing far less VRAM.
Is Phi-4 good for production inference on Kubernetes?
Yes. At INT4 on a single RTX 4090 or A6000 (48 GB), Phi-4 delivers fast tokens-per-second at very low cost. It's an excellent choice for edge inference, on-prem deployments, or Kubernetes nodes with limited GPU VRAM.
What is the Phi-4 context length?
Phi-4 supports a 16K token context window. This is smaller than Qwen 2.5 or Llama 3's 128K context, but sufficient for most inference tasks. The shorter context also means lower KV cache overhead.
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