Phi 3 Mini 128k Instruct — Hardware Requirements & GPU Compatibility
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Phi 3 Mini 128k Instruct is a 3.8B-parameter open language model from Microsoft in the Phi 3 family. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 3.40 GB of VRAM — see which GPUs and Macs can run it below.
248.6K downloads
1.7K likesSpecifications
- Publisher
- Microsoft
- Family
- Phi 3
- Parameters
- 3.8B
- Architecture
- Phi3ForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 32,064
- Release Date
- 2025-12-10
- License
- MIT
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HuggingFace
How Much VRAM Does Phi 3 Mini 128k Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 2.7 GB | 53.5 GB | 1.62 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 2.8 GB | 53.5 GB | 1.67 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 3.0 GB | 53.7 GB | 1.86 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 3.0 GB | 53.8 GB | 1.91 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 3.4 GB | 54.1 GB | 2.29 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 3.8 GB | 54.6 GB | 2.72 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 4.3 GB | 55.0 GB | 3.15 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 4.9 GB | 55.7 GB | 3.82 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Phi 3 Mini 128k Instruct?
Q4_K_M · 3.4 GBPhi 3 Mini 128k 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 131K context window can add up to 50.7 GB, bringing total usage to 54.1 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 128k Instruct?
Q4_K_M · 3.4 GB33 devices with unified memory can run Phi 3 Mini 128k Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Phi 3 Mini 128k Instruct need?
Phi 3 Mini 128k Instruct requires 3.4 GB of VRAM at Q4_K_M, or 4.9 GB at Q8_0. Full 131K context adds up to 50.7 GB (54.1 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 ≈ 51.8 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M3.4 GBQ4_K_M + full context54.1 GB- What's the best quantization for Phi 3 Mini 128k Instruct?
For Phi 3 Mini 128k Instruct, Q4_K_M (3.4 GB) offers the best balance of quality and VRAM usage. Q5_K_S (3.7 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_S3.7 GBQ8_04.9 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Phi 3 Mini 128k Instruct on a Mac?
Phi 3 Mini 128k 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 128k Instruct locally?
Yes — Phi 3 Mini 128k 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 128k Instruct?
At Q4_K_M, Phi 3 Mini 128k Instruct can reach ~857 tok/s on AMD Instinct MI300X. 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: AMD Instinct MI300X → 5300 ÷ 3.4 × 0.55 = ~857 tok/s
Estimated speed at Q4_K_M (3.4 GB)
~857 tok/s~193 tok/s~641 tok/s~530 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 128k Instruct?
At Q4_K_M, the download is about 2.29 GB. The full-precision Q8_0 version is 3.82 GB. The smallest option (Q2_K) is 1.62 GB.
- Which GPUs can run Phi 3 Mini 128k Instruct?
35 consumer GPUs can run Phi 3 Mini 128k 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. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Phi 3 Mini 128k Instruct?
33 devices with unified memory can run Phi 3 Mini 128k Instruct at Q4_K_M (3.4 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.