Phi 4 Mini Flash Reasoning — Hardware Requirements & GPU Compatibility
ChatMathCodeReasoning
Phi 4 Mini Flash Reasoning is a 3.9B-parameter open language model from Microsoft in the Phi 4 family. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 2.95 GB of VRAM — see which GPUs and Macs can run it below.
1.1K downloads
279 likesSpecifications
- Publisher
- Microsoft
- Family
- Phi 4
- Parameters
- 3.9B
- Architecture
- Phi4FlashForCausalLM
- Context Length
- 262,144 tokens
- Vocabulary Size
- 200,064
- Release Date
- 2025-12-10
- License
- MIT
Get Started
HuggingFace
How Much VRAM Does Phi 4 Mini Flash Reasoning Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 2.3 GB | 44.9 GB | 1.64 GB | 2-bit quantization with K-quant improvements |
| Q3_K_M | 3.90 | 2.5 GB | 45.1 GB | 1.88 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 3.0 GB | 45.6 GB | 2.31 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 3.4 GB | 46.0 GB | 2.74 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 3.8 GB | 46.4 GB | 3.18 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 4.5 GB | 47.1 GB | 3.85 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Phi 4 Mini Flash Reasoning?
Q4_K_M · 3.0 GBPhi 4 Mini Flash Reasoning (Q4_K_M) requires 3.0 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 262K context window can add up to 42.6 GB, bringing total usage to 45.6 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 Flash Reasoning?
Q4_K_M · 3.0 GB33 devices with unified memory can run Phi 4 Mini Flash Reasoning, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Phi 4 Mini Flash Reasoning need?
Phi 4 Mini Flash Reasoning requires 3.0 GB of VRAM at Q4_K_M, or 4.5 GB at Q8_0. Full 262K context adds up to 42.6 GB (45.6 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 3.9B × 4.8 bits ÷ 8 = 2.3 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 43.3 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M3.0 GBQ4_K_M + full context45.6 GB- What's the best quantization for Phi 4 Mini Flash Reasoning?
For Phi 4 Mini Flash Reasoning, Q4_K_M (3.0 GB) offers the best balance of quality and VRAM usage. Q5_K_M (3.4 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 2.3 GB.
VRAM requirement by quantization
Q2_K2.3 GBQ3_K_M2.5 GBQ4_K_M ★3.0 GBQ5_K_M3.4 GBQ6_K3.8 GBQ8_04.5 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Phi 4 Mini Flash Reasoning on a Mac?
Phi 4 Mini Flash Reasoning requires at least 2.3 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 Flash Reasoning locally?
Yes — Phi 4 Mini Flash Reasoning can run locally on consumer hardware. At Q4_K_M quantization it needs 3.0 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Phi 4 Mini Flash Reasoning?
At Q4_K_M, Phi 4 Mini Flash Reasoning can reach ~988 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~222 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 ÷ 3.0 × 0.55 = ~988 tok/s
Estimated speed at Q4_K_M (3.0 GB)
~988 tok/s~222 tok/s~739 tok/s~611 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 Flash Reasoning?
At Q4_K_M, the download is about 2.31 GB. The full-precision Q8_0 version is 3.85 GB. The smallest option (Q2_K) is 1.64 GB.
- Which GPUs can run Phi 4 Mini Flash Reasoning?
35 consumer GPUs can run Phi 4 Mini Flash Reasoning at Q4_K_M (3.0 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Phi 4 Mini Flash Reasoning?
33 devices with unified memory can run Phi 4 Mini Flash Reasoning at Q4_K_M (3.0 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.