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Phi-4 14B Instruct

Phi-4 14B Instruct needs roughly 11.4 GB VRAM at Q4_K_M quantization (33.2 GB at FP16). 106 GPUs we track can run it fully in VRAM at 8k context.

106 GPUs run this natively · 12 with CPU offload

Microsoft14B params16k contextMITCommercial use ok

Phi-4 14B Instruct is a 14B parameter dense model developed by Microsoft. December 2024 14B model with MIT licensing, compact yet competitive.

To run Phi-4 14B Instruct locally: Q4_K_M needs ~8.5GB for weights and ~11.4GB in total at 8K context (10 KV heads make its cache heavier than the other 14B models'), right at a 12GB card's usable ceiling, so a 16GB GPU is the comfortable fit; Q5_K_M reaches ~13GB.

MMLU-Pro 70.4%, Math 80.4%, punches above weight class.

VRAM at each quantization

Figures below assume 8k context; KV cache grows linearly as context length increases.

QuantWeightsKV cacheTotal
FP3256.0 GB1.68 GB64.6 GB
BF1628.0 GB1.68 GB33.2 GB
FP1628.0 GB1.68 GB33.2 GB
Q8_014.9 GB1.68 GB18.6 GB
Q6_K11.5 GB1.68 GB14.8 GB
Q5_K_Mrec10.0 GB1.68 GB13.0 GB
Q4_K_M8.5 GB1.68 GB11.4 GB
Q3_K_M6.7 GB1.68 GB9.4 GB
Q2_K5.3 GB1.68 GB7.8 GB
NVFP4cuda7.0 GB1.68 GB9.7 GB

KV cache is calculated at 8k context (FP16). Note that NVFP4 only runs on CUDA GPUs. Turn on TurboQuant in the calculator above for lower KV cache estimates.

Benchmarks

MMLU-Pro
70.4
MATH
80.4

GPUs that run Phi-4 14B Instruct natively (106)

Show 101 more
Plus 12 GPUs that run it with CPU offload (slower)

No GPU? Rent an RTX 4000 Ada from $0.20/hr ↗

Prices as of Oct 17, 2026. Rates change; check RunPod for current pricing. Affiliate link: CanItRun may earn a commission. Doesn't affect the fit calculation above.

Hugging Face ↗Ollama ↗Released 2024-12-13

Compare Phi-4 14B Instruct with other models

How to run Phi-4 14B Instruct locally

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Q5_K_M needs 13.0 GB: fits a single high-end consumer GPU (24 GB).

Ollama

ollama run phi4:14b

llama.cpp

./llama-cli -m phi-4.Q5_K_M.gguf -c 8192 -ngl 99

LM Studio: Search for 'Phi 4' in LM Studio. The Q5_K_M variant runs well on 12-16 GB GPUs and delivers impressive benchmark scores for its size.

Why this quantization? Phi-4 is a 14B dense model that punches far above its weight class, scoring 70.4 on MMLU-Pro and 80.4 on MATH -- figures that rival 70B models. Q5_K_M at roughly 10 GB preserves these exceptional capabilities while fitting comfortably on a 16 GB GPU. Dropping to Q4 saves only about 1.5 GB but risks degrading the precise knowledge that makes Phi-4 special.

Who is Phi-4 14B Instruct for?

Users with mid-range GPUs (12-16 GB VRAM) who want the best possible reasoning and math performance at this hardware tier. Ideal for students, researchers, and developers who need high-quality analytical capabilities without the VRAM demands of a 70B model. The MIT license makes it deployable anywhere.

Best for

  • Mathematical problem solving and STEM tutoring (MATH: 80.4, rivaling 70B models)
  • Knowledge-intensive Q&A and exam-style reasoning (MMLU-Pro: 70.4)
  • Code generation and technical analysis
  • Academic research assistance and paper comprehension
  • Running a capable AI assistant on a single mid-range GPU

Not ideal for

  • Long-context tasks -- the 16K context window is much shorter than most competitors
  • Creative writing and open-ended generation where larger models have more expressive range
  • Multilingual workloads -- Phi-4 is primarily optimized for English

Continue reading

vram-guides13 min
Best LLMs for 16 GB VRAM GPUs (2026)
tutorials10 min
How to Run LLMs Locally on Mac (M1–M4 Guide)
tutorials10 min
GGUF Quantization Explained: Q4, Q5, Q6, Q8 Compared
tutorials9 min
Getting Started with Ollama: Run Any LLM in One Command
vram-guides10 min
Best LLMs for 12 GB VRAM (2026): RTX 3060, 4070 & 5070
hardware10 min
How to Choose Your First GPU for Local LLMs
tutorials12 min
llama.cpp Complete Setup Guide: Build, Configure, and Optimize
tutorials9 min
How to Benchmark Your Local LLM Setup
comparisons10 min
Ollama vs LM Studio vs llama.cpp: Which Tool Should You Use?
hardware9 min
Best GPUs $500-$1000 for LLMs (2026)
hardware10 min
NVIDIA RTX 40 Series for LLMs: Complete VRAM & Performance Guide
hardware10 min
AMD Radeon for LLMs: ROCm & Vulkan Complete Guide
tutorials10 min
LLM Benchmarks Explained: MMLU, HumanEval, SWE-bench, and More

Frequently asked questions

What are the VRAM requirements for Phi-4 14B Instruct?
Phi-4 14B Instruct requires approximately 11.4 GB of VRAM at Q4_K_M quantization, 18.6 GB at Q8, and 33.2 GB at FP16. These numbers assume 8k context window; VRAM scales linearly with context length due to the KV cache.
How many parameters does Phi-4 14B Instruct have?
Phi-4 14B Instruct has 14 billion parameters.
How capable is Phi-4 14B Instruct?
Phi-4 14B Instruct achieves an MMLU-Pro score of 70.4, placing it among the most capable open-weight models available, competitive with frontier systems on general knowledge and reasoning.
Can Phi-4 14B Instruct run on a 16 GB GPU?
Yes. Phi-4 14B Instruct needs 11.4 GB at Q4_K_M, which fits in a 16 GB GPU like the RTX 4080 or RTX 5070 Ti.
What is the smallest quantization for Phi-4 14B Instruct that fits in 24 GB of VRAM?
At NVFP4, Phi-4 14B Instruct needs 9.7 GB, the highest-quality quantization that fits in 24 GB of VRAM.
What GPU do I need to run Phi-4 14B Instruct locally?
A 16 GB GPU is enough. At Q4_K_M, Phi-4 14B Instruct needs 11.4 GB VRAM. Good options: RTX 4080 (16 GB), RTX 5070 Ti (16 GB).
Can I run Phi-4 14B Instruct in the cloud?
Yes. The cheapest RunPod setup we found that holds Phi-4 14B Instruct entirely in GPU memory at 8k context is 1× RTX 4000 Ada at Q8_0, at roughly 16.3 tokens per second and from $0.20 per hour for the whole pod. Prices as of Oct 17, 2026; RunPod rates change, so check their pricing page for the current figure.