Phi Tiny MoE Instruct — Hardware Requirements & GPU Compatibility
Chat
Phi Tiny MoE Instruct is a 3.8B-parameter open language model from Microsoft in the Phi family. It supports a context window of up to 4,096 tokens. At Q4_K_M it needs about 2.82 GB of VRAM — see which GPUs and Macs can run it below.
92.3K downloads
43 likesSpecifications
- Publisher
- Microsoft
- Family
- Phi
- Parameters
- 3.8B
- Architecture
- PhiMoEForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 32,064
- Release Date
- 2025-06-23
- License
- MIT
Get Started
HuggingFace
How Much VRAM Does Phi Tiny MoE Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 2.2 GB | 2.4 GB | 1.60 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 2.4 GB | 2.7 GB | 1.83 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 2.8 GB | 3.1 GB | 2.25 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 3.2 GB | 3.5 GB | 2.68 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 3.7 GB | 3.9 GB | 3.10 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 4.3 GB | 4.6 GB | 3.76 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 8.1 GB | 8.3 GB | 7.51 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Phi Tiny MoE Instruct?
Q4_K_M · 2.8 GBPhi Tiny MoE Instruct (Q4_K_M) requires 2.8 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 4K context window can add up to 0.3 GB, bringing total usage to 3.1 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Phi Tiny MoE Instruct?
Q4_K_M · 2.8 GB59 devices with unified memory can run Phi Tiny MoE Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download Phi Tiny MoE Instruct
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does Phi Tiny MoE Instruct need?
Phi Tiny MoE Instruct requires 2.8 GB of VRAM at Q4_K_M, or 8.1 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 3.8B × 4.8 bits ÷ 8 = 2.3 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 0.8 GB (at full 4K context)
VRAM usage by quantization
Q4_K_M2.8 GBQ4_K_M + full context3.1 GB- What's the best quantization for Phi Tiny MoE Instruct?
For Phi Tiny MoE Instruct, Q4_K_M (2.8 GB) offers the best balance of quality and VRAM usage. Q5_K_M (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 GBQ4_K_M ★2.8 GBQ5_K_M3.2 GBQ6_K3.7 GBQ8_04.3 GBBF168.1 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Phi Tiny MoE Instruct on a Mac?
Phi Tiny MoE 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 Tiny MoE Instruct locally?
Yes — Phi Tiny MoE Instruct can run locally on consumer hardware. At Q4_K_M quantization it needs 2.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Phi Tiny MoE Instruct?
At Q4_K_M, Phi Tiny MoE Instruct can reach ~152 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~309 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 ÷ 2.8 × 0.65 = ~514 tok/s
Estimated speed at Q4_K_M (2.8 GB)
~514 tok/s~309 tok/s~514 tok/s~485 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Phi Tiny MoE Instruct?
At Q4_K_M, the download is about 2.25 GB. The full-precision BF16 version is 7.51 GB. The smallest option (Q2_K) is 1.60 GB.
- Which GPUs can run Phi Tiny MoE Instruct?
52 consumer GPUs can run Phi Tiny MoE Instruct at Q4_K_M (2.8 GB). 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 Tiny MoE Instruct?
59 devices with unified memory can run Phi Tiny MoE Instruct at Q4_K_M (2.8 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.