Phi 2 — Hardware Requirements & GPU Compatibility
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Microsoft Phi 2 is a 2.8-billion parameter language model from Microsoft Research that pioneered the concept of small but highly capable language models. Released in late 2023, Phi 2 demonstrated that strategic data curation and training methodology could allow a sub-3B model to outperform many 7B and 13B models on reasoning and coding benchmarks. The model runs on virtually any modern GPU and even on CPU-only setups. While succeeded by Phi 3 and Phi 4, Phi 2 remains historically significant as the model that proved small-scale language models could be genuinely useful for practical tasks. Released under the MIT license.
437.6K downloads
3.5K likesSpecifications
- Publisher
- Microsoft
- Family
- Phi 2
- Parameters
- 2.8B
- Architecture
- PhiForCausalLM
- Context Length
- 2,048 tokens
- Vocabulary Size
- 51,200
- Release Date
- 2023-12-13
- License
- MIT
Get Started
HuggingFace
How Much VRAM Does Phi 2 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 2.1 GB | — | 1.18 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 2.2 GB | — | 1.22 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 2.3 GB | — | 1.36 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 2.4 GB | — | 1.39 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 2.6 GB | — | 1.67 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 3.0 GB | — | 1.98 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 3.3 GB | — | 2.29 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 3.8 GB | — | 2.78 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Phi 2?
Q4_K_M · 2.6 GBPhi 2 (Q4_K_M) requires 2.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 4+ GB is recommended. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Phi 2?
Q4_K_M · 2.6 GB59 devices with unified memory can run Phi 2, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download Phi 2
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Benchmarks
Benchmark details →Related Models
Frequently Asked Questions
- How much VRAM does Phi 2 need?
Phi 2 requires 2.6 GB of VRAM at Q4_K_M, or 6.5 GB at FP16.
VRAM = Weights + KV Cache + Overhead
Weights = 2.8B × 4.8 bits ÷ 8 = 1.7 GB
KV Cache + Overhead ≈ 0.9 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M2.6 GB- What's the best quantization for Phi 2?
For Phi 2, Q4_K_M (2.6 GB) offers the best balance of quality and VRAM usage. Q5_0 (2.7 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 2.1 GB.
VRAM requirement by quantization
Q2_K2.1 GBQ4_02.4 GBQ4_K_M ★2.6 GBQ5_02.7 GBQ5_K_M3.0 GBFP166.5 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Phi 2 on a Mac?
Phi 2 requires at least 2.1 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 2 locally?
Yes — Phi 2 can run locally on consumer hardware. At Q4_K_M quantization it needs 2.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Phi 2?
At Q4_K_M, Phi 2 can reach ~1667 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~248 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 ÷ 2.6 × 0.65 = ~1970 tok/s
Estimated speed at Q4_K_M (2.6 GB)
~1970 tok/s~248 tok/s~1970 tok/s~1667 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Phi 2?
At Q4_K_M, the download is about 1.67 GB. The full-precision FP16 version is 5.56 GB. The smallest option (Q2_K) is 1.18 GB.
- Which GPUs can run Phi 2?
50 consumer GPUs can run Phi 2 at Q4_K_M (2.6 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 2?
59 devices with unified memory can run Phi 2 at Q4_K_M (2.6 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.