Phi 3 Mini 4k Instruct — Hardware Requirements & GPU Compatibility
ChatCode
Microsoft Phi 3 Mini 4K Instruct is a 3.8-billion parameter instruction-tuned model from Microsoft Research's Phi 3 generation, with a 4K token context window. The Phi 3 family demonstrated that small models trained on carefully curated, high-quality data can achieve performance competitive with models several times their size. The model runs on consumer GPUs with as little as 4-6GB of VRAM when quantized, making it one of the most accessible capable chat models for local deployment. Released under the MIT license.
595.1K downloads
1.4K likesSpecifications
- Publisher
- Microsoft
- Family
- Phi 3
- Parameters
- 3.8B
- Architecture
- Phi3ForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 32,064
- Release Date
- 2024-04-22
- License
- MIT
Get Started
HuggingFace
How Much VRAM Does Phi 3 Mini 4k Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 2.7 GB | 3.5 GB | 1.62 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 2.8 GB | 3.6 GB | 1.67 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 3.0 GB | 3.8 GB | 1.86 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 3.0 GB | 3.8 GB | 1.91 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 3.4 GB | 4.2 GB | 2.29 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 3.8 GB | 4.6 GB | 2.72 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 4.3 GB | 5.1 GB | 3.15 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 4.9 GB | 5.7 GB | 3.82 GB | 8-bit quantization, near-lossless |
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 3 Mini 4k Instruct?
Q4_K_M · 3.4 GBPhi 3 Mini 4k Instruct (Q4_K_M) requires 3.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 5+ GB is recommended. Using the full 4K context window can add up to 0.8 GB, bringing total usage to 4.2 GB. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Phi 3 Mini 4k Instruct?
Q4_K_M · 3.4 GB59 devices with unified memory can run Phi 3 Mini 4k Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, iPhone 17.
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightWhere to Download Phi 3 Mini 4k 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 3 Mini 4k Instruct need?
Phi 3 Mini 4k Instruct requires 3.4 GB of VRAM at Q4_K_M, or 8.8 GB at BF16. Full 4K context adds up to 0.8 GB (4.2 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 3.8B × 4.8 bits ÷ 8 = 2.3 GB
KV Cache + Overhead ≈ 1.1 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 1.9 GB (at full 4K context)
VRAM usage by quantization
Q4_K_M3.4 GBQ4_K_M + full context4.2 GB- What's the best quantization for Phi 3 Mini 4k Instruct?
For Phi 3 Mini 4k Instruct, Q4_K_M (3.4 GB) offers the best balance of quality and VRAM usage. Q5_0 (3.5 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 2.7 GB.
VRAM requirement by quantization
Q2_K2.7 GBQ4_03.0 GBQ4_K_S3.3 GBQ4_K_M ★3.4 GBQ5_K_M3.8 GBBF168.8 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Phi 3 Mini 4k Instruct on a Mac?
Phi 3 Mini 4k Instruct requires at least 2.7 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 3 Mini 4k Instruct locally?
Yes — Phi 3 Mini 4k Instruct can run locally on consumer hardware. At Q4_K_M quantization it needs 3.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Phi 3 Mini 4k Instruct?
At Q4_K_M, Phi 3 Mini 4k Instruct can reach ~1294 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~193 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 ÷ 3.4 × 0.65 = ~1529 tok/s
Estimated speed at Q4_K_M (3.4 GB)
~1529 tok/s~193 tok/s~1529 tok/s~1294 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Phi 3 Mini 4k Instruct?
At Q4_K_M, the download is about 2.29 GB. The full-precision BF16 version is 7.64 GB. The smallest option (Q2_K) is 1.62 GB.
- Which GPUs can run Phi 3 Mini 4k Instruct?
50 consumer GPUs can run Phi 3 Mini 4k Instruct at Q4_K_M (3.4 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 3 Mini 4k Instruct?
59 devices with unified memory can run Phi 3 Mini 4k Instruct at Q4_K_M (3.4 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.