Phi 4 Mini Instruct — Hardware Requirements & GPU Compatibility
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Microsoft Phi 4 Mini Instruct is a 3.8-billion parameter instruction-tuned model from Microsoft Research's Phi 4 family. It applies the Phi series' data-centric training philosophy to a compact model, delivering strong performance in coding, reasoning, and chat tasks relative to its small footprint. The model runs on consumer GPUs with as little as 4-6GB of VRAM when quantized, making it accessible on mainstream and even entry-level hardware. Released under the MIT license.
309.1K downloads
699 likesSpecifications
- Publisher
- Microsoft
- Family
- Phi 4
- Parameters
- 3.8B
- Architecture
- Phi3ForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 200,064
- Release Date
- 2025-12-10
- License
- MIT
Get Started
HuggingFace
How Much VRAM Does Phi 4 Mini Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 2.2 GB | 19.1 GB | 1.61 GB | 2-bit quantization with K-quant improvements |
| Q3_K_M | 3.90 | 2.4 GB | 19.3 GB | 1.85 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 2.9 GB | 19.8 GB | 2.28 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 3.3 GB | 20.2 GB | 2.71 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 3.7 GB | 20.6 GB | 3.13 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 4.4 GB | 21.3 GB | 3.80 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Phi 4 Mini Instruct?
Q4_K_M · 2.9 GBPhi 4 Mini Instruct (Q4_K_M) requires 2.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 4+ GB is recommended. Using the full 131K context window can add up to 16.9 GB, bringing total usage to 19.8 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Phi 4 Mini Instruct?
Q4_K_M · 2.9 GB33 devices with unified memory can run Phi 4 Mini Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Derivatives (1)
Frequently Asked Questions
- How much VRAM does Phi 4 Mini Instruct need?
Phi 4 Mini Instruct requires 2.9 GB of VRAM at Q4_K_M, or 4.4 GB at Q8_0. Full 131K context adds up to 16.9 GB (19.8 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 3.8B × 4.8 bits ÷ 8 = 2.3 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 17.5 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M2.9 GBQ4_K_M + full context19.8 GB- What's the best quantization for Phi 4 Mini Instruct?
For Phi 4 Mini Instruct, Q4_K_M (2.9 GB) offers the best balance of quality and VRAM usage. Q5_K_M (3.3 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 2.2 GB.
VRAM requirement by quantization
Q2_K2.2 GB~75%Q3_K_M2.4 GB~83%Q4_K_M ★2.9 GB~89%Q5_K_M3.3 GB~92%Q6_K3.7 GB~95%Q8_04.4 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Phi 4 Mini Instruct on a Mac?
Phi 4 Mini Instruct requires at least 2.2 GB at Q2_K, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Phi 4 Mini Instruct locally?
Yes — Phi 4 Mini Instruct can run locally on consumer hardware. At Q4_K_M quantization it needs 2.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Phi 4 Mini Instruct?
At Q4_K_M, Phi 4 Mini Instruct can reach ~1023 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~230 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 2.9 × 0.55 = ~1023 tok/s
Estimated speed at Q4_K_M (2.9 GB)
AMD Instinct MI300X~1023 tok/sNVIDIA GeForce RTX 4090~230 tok/sNVIDIA H100 SXM~765 tok/sAMD Instinct MI250X~632 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Phi 4 Mini Instruct?
At Q4_K_M, the download is about 2.28 GB. The full-precision Q8_0 version is 3.80 GB. The smallest option (Q2_K) is 1.61 GB.