Phi 1 5 — Hardware Requirements & GPU Compatibility
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Phi 1 5 is a 1.4B-parameter open language model from Microsoft in the Phi family. It supports a context window of up to 2,048 tokens. At Q4_K_M it needs about 0.94 GB of VRAM — see which GPUs and Macs can run it below.
60.9K downloads
1.4K likesSpecifications
- Publisher
- Microsoft
- Family
- Phi
- Parameters
- 1.4B
- Architecture
- PhiForCausalLM
- Context Length
- 2,048 tokens
- Vocabulary Size
- 51,200
- Release Date
- 2023-09-10
- License
- MIT
Get Started
HuggingFace
How Much VRAM Does Phi 1 5 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 0.7 GB | — | 0.60 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 0.7 GB | — | 0.62 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 0.8 GB | — | 0.69 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 0.9 GB | — | 0.85 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 1.1 GB | — | 1.01 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 1.3 GB | — | 1.17 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 1.6 GB | — | 1.42 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Phi 1 5?
Q4_K_M · 0.9 GBPhi 1 5 (Q4_K_M) requires 0.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ 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 1 5?
Q4_K_M · 0.9 GB59 devices with unified memory can run Phi 1 5, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download Phi 1 5
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 1 5 need?
Phi 1 5 requires 0.9 GB of VRAM at Q4_K_M, or 3.1 GB at FP16.
VRAM = Weights + KV Cache + Overhead
Weights = 1.4B × 4.8 bits ÷ 8 = 0.9 GB
VRAM usage by quantization
Q4_K_M0.9 GB- What's the best quantization for Phi 1 5?
For Phi 1 5, Q4_K_M (0.9 GB) offers the best balance of quality and VRAM usage. Q5_K_S (1.1 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 0.7 GB.
VRAM requirement by quantization
Q2_K0.7 GBQ3_K_L0.8 GBQ4_K_M ★0.9 GBQ5_K_S1.1 GBQ5_K_M1.1 GBFP163.1 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Phi 1 5 on a Mac?
Phi 1 5 requires at least 0.7 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 1 5 locally?
Yes — Phi 1 5 can run locally on consumer hardware. At Q4_K_M quantization it needs 0.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Phi 1 5?
At Q4_K_M, Phi 1 5 can reach ~4681 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~697 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 ÷ 0.9 × 0.65 = ~5532 tok/s
Estimated speed at Q4_K_M (0.9 GB)
~5532 tok/s~697 tok/s~5532 tok/s~4681 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Phi 1 5?
At Q4_K_M, the download is about 0.85 GB. The full-precision FP16 version is 2.84 GB. The smallest option (Q2_K) is 0.60 GB.
- Which GPUs can run Phi 1 5?
50 consumer GPUs can run Phi 1 5 at Q4_K_M (0.9 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 1 5?
59 devices with unified memory can run Phi 1 5 at Q4_K_M (0.9 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.