Phi 3 Mini 4k Instruct — Hardware Requirements & GPU Compatibility
ChatCode
Microsoft Phi 3 Mini 4K Instruct is a 3.8-billion parameter instruction-tuned model from Microsoft Research's Phi 3 generation, with a 4K token context window. The Phi 3 family demonstrated that small models trained on carefully curated, high-quality data can achieve performance competitive with models several times their size. The model runs on consumer GPUs with as little as 4-6GB of VRAM when quantized, making it one of the most accessible capable chat models for local deployment. Released under the MIT license.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 4K)Best on 24 GB (Q4_K_M · ~193 tok/s · 4K)Macs from 8 GB (Q4_K_M · ~21 tok/s)
MIT
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Phi 3 Mini 4k Instruct
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
ollama run hf.co/bartowski/Phi-3.1-mini-4k-instruct-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 19.8 GB free · 4K context
Download: Phi-3.1-mini-4k-instruct-Q4_K_M.gguf2.39 GBbartowski
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 24 GB GPU
- The easiest way to try this model
- Running in a few minutes, easy setup
- Chat right here or connect any app
$5+ bonus for new users
Runs on RTX 3060 12GB (7 GB headroom) · Q4_K_M
- Generation speed
- ~69 tok/s
- generation speed
- Cost per 1M output tokens
- $0.24
- per 1M output tokens
How Much VRAM Does Phi 3 Mini 4k Instruct Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 2.7 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 4K context · ~240 tok/s | |
| 2.8 GB | — | 3-bit small — 3-bit small quantization | Fits: 4K context · ~236 tok/s | |
| 3.0 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 4K context · ~221 tok/s | |
| 3.0 GB | — | 4-bit · legacy — 4-bit legacy quantization | Fits: 4K context · ~217 tok/s | |
| 3.4 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 4K context · ~193 tok/s | |
| 3.8 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 4K context · ~171 tok/s | |
| 4.3 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 4K context · ~154 tok/s | |
| 4.9 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 4K context · ~133 tok/s |
Selected Q4_K_M
4-bit medium quantization4.2GBat 4K context
- Weights
- 2.3 GB
- KV cache
- 1.6 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 19.8 GB free · ~193 tok/s
≥50 tok/s needs ≥261 GB/s · RTX 5090 ~343, RTX 3090 Ti ~193
Which Hardware Can Run Phi 3 Mini 4k Instruct?
Q4_K_M · 3.4 GBPhi 3 Mini 4k Instruct (Q4_K_M) requires 3.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 5+ GB is recommended. Using the full 4K context window can add up to 0.8 GB, bringing total usage to 4.2 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Phi 3 Mini 4k Instruct, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), MacBook Air 13" M3 (8 GB).
≤ 11 GB
≤ 11 GB tier →fits · 3.8 GB free · up to 4K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~145Simulate output on RTX 3080RTX 2080 Ti~118Simulate output on RTX 2080 TiRTX 3070 Ti~116Simulate output on RTX 3070 TiM3*8 GB~21Simulate output on iMac 24" M3 (8 GB)Orin Nano~20Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~14Simulate output on iPhone 1720 GB
fits · 15.8 GB free · up to 4K ctx
1 GPU
RX 7900 XT~141Simulate output on RX 7900 XT32–40 GB
fits · 27.8 GB free · up to 4K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~343Simulate output on RTX 5090M5 Max36 GB~95Simulate output on Mac Studio M5 Max (36 GB)M532 GB~32Simulate output on MacBook Air 13" M5 (32 GB)Orin~39Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~26Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
How good is Phi 3 Mini 4k Instruct?
llmrun Score blends public benchmark boards into one number; 100 = the average open model.
Not enough data yet for an llmrun Score.
A Score needs results on enough independent boards. How the Composite Score is built
Benchmarks
The 6 boards that weigh most in the llmrun Score, of 9 with a result for this model; badges show who ran the test.
- LMArena Text
LMArena · Oct 2026Arena
Arena1143.2
#171/188 open
- MMLU-Pro
TIGER-LabIndependent
Independent45.7%
#83/146 open
- MMLU
Epoch AI · Apr 2024Lab-reported
Lab-reported68.8%
#31/85 open
- BIG-Bench Hard
Epoch AI · Apr 2024Lab-reported
Lab-reported71.7%
#8/38 open
- HellaSwag
Epoch AI · Apr 2024Lab-reported
Lab-reported76.7%
#22/47 open
- ARC Challenge
Epoch AI · Apr 2024Lab-reported
Lab-reported84.9%
#9/56 open
Alternatives to Phi 3 Mini 4k Instruct
Phi 3 family · 15 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Spark X2.6 4BXHToken | — | 4.3 GB | ~221 | same size |
| NVIDIA Nemotron Labs Dory1.0 4B BF16NVIDIA | — | 5.7 GB | ~214 | same size |
| Granite Vision 4.1 4BIBM | — | 4 GB | ~228 | same size |
| Qwen3 4B Instruct 2507Alibaba | — | 4.2 GB | ~226 | same size |
| MiniCPM V 4OpenBMB | — | 3.1 GB | ~236 | same size |
| Qwen2.5 VL 3B InstructAlibaba | — | 3.2 GB | ~249 | same size |
Fine-tunes & variants(3)
Specifications
- Publisher
- Microsoft
- Family
- Phi 3
- Parameters
- 3.8B
- Architecture
- Phi3ForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 32,064
- Release Date
- 2024-04-22
- License
- MIT
Frequently Asked Questions
How much VRAM does Phi 3 Mini 4k Instruct need?
Phi 3 Mini 4k Instruct requires 3.4 GB of VRAM at Q4_K_M, or 8.8 GB at BF16. Full 4K context adds up to 0.8 GB (4.2 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 3.8B × 4.8 bits ÷ 8 = 2.3 GB
KV Cache + Overhead ≈ 1.1 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 1.9 GB (at full 4K context)
VRAM usage by quantization
What's the best quantization for Phi 3 Mini 4k Instruct?
For Phi 3 Mini 4k Instruct, Q4_K_M (3.4 GB) offers the best balance of quality and VRAM usage. Q4_K_L (3.5 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 2.2 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Phi 3 Mini 4k Instruct on a Mac?
Yes — Mac Mini M2 (8 GB) and 145 other Macs can run Phi 3 Mini 4k Instruct. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 3.4 GB of usable unified memory (RAM minus macOS overhead).
Can I run Phi 3 Mini 4k Instruct locally?
Yes — Phi 3 Mini 4k Instruct can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Phi 3 Mini 4k Instruct?
At Q4_K_M, Phi 3 Mini 4k Instruct can reach ~1412 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~193 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 3.4 × 0.65 = ~1529 tok/s
Estimated speed at Q4_K_M (3.4 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Phi 3 Mini 4k Instruct?
At Q4_K_M, the download is about 2.29 GB. The full-precision BF16 version is 7.64 GB. The smallest option (IQ2_XXS) is 1.05 GB.
Which GPUs can run Phi 3 Mini 4k Instruct?
52 consumer GPUs can run Phi 3 Mini 4k Instruct at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
Which devices can run Phi 3 Mini 4k Instruct?
163 devices with unified memory can run Phi 3 Mini 4k Instruct at Q4_K_M (3.4 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.