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.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 2K)Best on 24 GB (Q4_K_M · ~248 tok/s · 2K)Macs from 8 GB (Q4_K_M · ~27 tok/s)
MIT
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Phi 2
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/TheBloke/phi-2-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 21.4 GB free · 2K context
Download: phi-2.Q4_K_M.gguf1.79 GBTheBloke
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 3080 Ti (12 GB) (9 GB headroom) · Q4_K_M
- Generation speed
- ~225 tok/s
- generation speed
- Cost per 1M output tokens
- $0.15
- per 1M output tokens
How Much VRAM Does Phi 2 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 2.1 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 2K context · ~305 tok/s | |
| 2.2 GB | — | 3-bit small — 3-bit small quantization | Fits: 2K context · ~299 tok/s | |
| 2.3 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 2K context · ~281 tok/s | |
| 2.4 GB | — | 4-bit · legacy — 4-bit legacy quantization | Fits: 2K context · ~278 tok/s | |
| 2.6 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 2K context · ~248 tok/s | |
| 3.0 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 2K context · ~222 tok/s | |
| 3.3 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 2K context · ~201 tok/s | |
| 3.8 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 2K context · ~175 tok/s |
Selected Q4_K_M
4-bit medium quantization2.6GBat 2K context
- Weights
- 1.7 GB
- KV cache
- 0.7 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 21.4 GB free · ~248 tok/s
≥50 tok/s needs ≥203 GB/s · RTX 5090 ~441, RTX 3090 Ti ~248
Which Hardware 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. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Phi 2, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 5.4 GB free · up to 2K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~187Simulate output on RTX 3080RTX 2080 Ti~152Simulate output on RTX 2080 TiRTX 3070 Ti~150Simulate output on RTX 3070 TiM3*8 GB~27Simulate output on iMac 24" M3 (8 GB)Orin Nano~25Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~18Simulate output on iPhone 1720 GB
fits · 17.4 GB free · up to 2K ctx
1 GPU
RX 7900 XT~182Simulate output on RX 7900 XT32–40 GB
fits · 29.4 GB free · up to 2K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~441Simulate output on RTX 5090M5 Max36 GB~122Simulate output on Mac Studio M5 Max (36 GB)M532 GB~41Simulate output on MacBook Air 13" M5 (32 GB)Orin~50Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~33Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
How good is Phi 2?
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 7 with a result for this model; badges show who ran the test.
- MMLU
Epoch AI · Dec 2023Lab-reported
Lab-reported58.4%
#50/85 open
- BIG-Bench Hard
Epoch AI · Dec 2023Lab-reported
Lab-reported59.4%
#11/38 open
- HellaSwag
Epoch AI · Dec 2023Lab-reported
Lab-reported53.6%
#42/47 open
- ARC Challenge
Epoch AI · Dec 2023Lab-reported
Lab-reported75.9%
#16/56 open
- WinoGrande
Epoch AI · Dec 2023Lab-reported
Lab-reported54.7%
#49/51 open
- OpenBookQA
Epoch AI · Dec 2023Lab-reported
Lab-reported73.6%
#8/26 open
Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| LFM2.5 2.6BLiquid AI | — | 2.3 GB | ~321 | same size |
| Granite 3.0 2B InstructIBM | — | 2.2 GB | ~320 | same size |
| Gemma 2 2B ITGoogle | — | 2.5 GB | ~379 | same size |
| Ouro 2.6B ThinkingByteDance | — | 12.1 GB | ~102 | same size |
| Nemotron Flash 3BNVIDIA | — | 7.6 GB | ~109 | same size |
| Mamba 2.8B HFState Spaces | — | 3.7 GB | ~358 | same size |
Specifications
- 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
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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
VRAM usage by quantization
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 IQ2_M at 1.9 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Phi 2 on a Mac?
Yes — Mac Mini M2 (8 GB) and 145 other Macs can run Phi 2. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 2.6 GB of usable unified memory (RAM minus macOS overhead).
Can I run Phi 2 locally?
Yes — Phi 2 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Phi 2?
At Q4_K_M, Phi 2 can reach ~1818 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 ÷ 2.64 × 0.65 = ~1970 tok/s
Estimated speed at Q4_K_M (2.6 GB)
Real-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 (IQ2_M) is 0.94 GB.
Which GPUs can run Phi 2?
52 consumer GPUs can run Phi 2 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 2?
163 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, 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.