Microsoft/Dense

Microsoftphi-3-mini-4k 3.8B

chatThinking
3.8B
Parameters
4K
Context length
7
Benchmarks
6
Quantizations
840K
HF downloads
Architecture
Dense
Released
2024-04-23
Layers
32
KV Heads
32
Head Dim
96
Family
phi

Quantization Options

QuantBitsVRAMQuality
Q4_K_M4.892.8 GBgood
Q5_K_S5.573.1 GBgood
Q5_K_M5.73.2 GBgood
Q6_K6.563.6 GBexcellent
Q8_08.54.5 GBlossless
FP16168.1 GBlossless

Select your GPU above to see speed estimates and compatibility for each quantization.

READY TO RUN THIS?RENT BY THE HOUR

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Community Ratings

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Benchmarks (7)

IFEval73.8
BBH38.7
BigCodeBench36.8
MMLU-PRO32.6
MATH17.0
GPQA7.9
MUSR6.4

Run this model

Easiest way to get started·Beginners
DOCS ↗
curl -fsSL https://ollama.com/install.sh | sh
$ollama run phi3:3.8b-instruct-q4_K_M

Downloads and runs automatically. Add --verbose for speed stats.

▸ SETUP GUIDE
>_

Auto-setup with fitmyllm CLI

Detects your GPU, recommends the best model, downloads it, and starts chatting — zero config. Benchmarks your speed and contributes anonymous data to improve predictions.

pip install fitmyllmthen run fitmyllmLearn more
Auto-detect GPULive tok/s in chatSpeed benchmarks9 inference engines

GPUs that can run this model

At Q4_K_M quantization. Sorted by minimum VRAM.

NVIDIA Tesla C2050
3 GB VRAM • 144 GB/s
NVIDIA
NVIDIA Tesla M2050
3 GB VRAM • 148 GB/s
NVIDIA
NVIDIA Tesla S2050
3 GB VRAM • 148 GB/s
NVIDIA

Find the best GPU for phi-3-mini-4k 3.8B

Build Hardware for phi-3-mini-4k 3.8B

Read the full model card for detailed information about this model.

▸ SPEC SHEET

phi-3-mini-4k 3.8B3.8B Dense.

▸ SPECIFICATIONS
PARAMETERS
3.8B
ARCHITECTURE
Dense Transformer
CONTEXT LENGTH
4K tokens
CAPABILITIES
chat
RELEASE DATE
2024-04-23
PROVIDER
Microsoft
FAMILY
phi
▸ VRAM REQUIREMENTS
QUANTBPWVRAMQUALITY
Q4_K_M4.892.8 GB94%
Q5_K_S5.573.1 GB96%
Q5_K_M5.73.2 GB96%
Q6_K6.563.6 GB97%
Q8_08.54.5 GB100%
FP16168.1 GB100%
§ 01BENCHMARK SCORES
MMLU-PRO32.6
MATH17.0
IFEval73.8
BBH38.7
GPQA7.9
MUSR6.4
BigCodeBench36.8
§ 02RUN COMMAND

Run phi-3-mini-4k 3.8B locally with Ollama — needs 2.8 GB VRAM at Q4_K_M:

$ollama run phi3:3.8b