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.
1.1M downloads
764 likesSpecifications
- Publisher
- Microsoft
- Family
- Phi 4
- Parameters
- 3.8B
- Architecture
- Phi3ForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 200,064
- Release Date
- 2025-02-19
- License
- MIT
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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 | 1.63 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 2.3 GB | 19.2 GB | 1.68 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 2.4 GB | 19.4 GB | 1.87 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 2.9 GB | 19.8 GB | 2.30 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 3.3 GB | 20.2 GB | 2.73 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 3.7 GB | 20.6 GB | 3.16 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 4.4 GB | 21.3 GB | 3.84 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. 50 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 GB59 devices with unified memory can run Phi 4 Mini Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download Phi 4 Mini Instruct
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
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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 8.2 GB at BF16. 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_S (3.2 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 GBQ3_K_L2.5 GBQ4_K_M ★2.9 GBQ5_K_S3.2 GBQ6_K3.7 GBBF168.2 GB★ 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 ~1533 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~228 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 ÷ 2.9 × 0.65 = ~1812 tok/s
Estimated speed at Q4_K_M (2.9 GB)
~1812 tok/s~228 tok/s~1812 tok/s~1533 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 2.30 GB. The full-precision BF16 version is 7.67 GB. The smallest option (Q2_K) is 1.63 GB.
- Which GPUs can run Phi 4 Mini Instruct?
50 consumer GPUs can run Phi 4 Mini Instruct at Q4_K_M (2.9 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 50 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Phi 4 Mini Instruct?
59 devices with unified memory can run Phi 4 Mini Instruct at Q4_K_M (2.9 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, 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.