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.
926.3K 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
- 2025-12-10
- License
- MIT
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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 |
|---|---|---|---|---|---|
| Q8_0 | 8.00 | 4.9 GB | 5.7 GB | 3.80 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Phi 3 Mini 4k Instruct?
Q8_0 · 4.9 GBPhi 3 Mini 4k Instruct (Q8_0) requires 4.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 7+ GB is recommended. Using the full 4K context window can add up to 0.8 GB, bringing total usage to 5.7 GB. 35 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?
Q8_0 · 4.9 GB33 devices with unified memory can run Phi 3 Mini 4k Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Derivatives (1)
Frequently Asked Questions
- How much VRAM does Phi 3 Mini 4k Instruct need?
Phi 3 Mini 4k Instruct requires 4.9 GB of VRAM at Q8_0. Full 4K context adds up to 0.8 GB (5.7 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 3.8B × 8 bits ÷ 8 = 3.8 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
Q8_04.9 GBQ8_0 + full context5.7 GB- Can I run Phi 3 Mini 4k Instruct on a Mac?
Phi 3 Mini 4k Instruct requires at least 4.9 GB at Q8_0, 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 Q8_0 quantization it needs 4.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Phi 3 Mini 4k Instruct?
At Q8_0, Phi 3 Mini 4k Instruct can reach ~594 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~133 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 4.9 × 0.55 = ~594 tok/s
Estimated speed at Q8_0 (4.9 GB)
AMD Instinct MI300X~594 tok/sNVIDIA GeForce RTX 4090~133 tok/sNVIDIA H100 SXM~444 tok/sAMD Instinct MI250X~367 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 Q8_0, the download is about 3.80 GB.