Phi 3 Medium 4k Instruct — Hardware Requirements & GPU Compatibility
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Phi 3 Medium 4k Instruct is a 14.0B-parameter open language model from Microsoft in the Phi 3 family. It supports a context window of up to 4,096 tokens. At Q4_K_M it needs about 9.10 GB of VRAM — see which GPUs and Macs can run it below.
11.3K downloads
225 likesSpecifications
- Publisher
- Microsoft
- Family
- Phi 3
- Parameters
- 14.0B
- Architecture
- Phi3ForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 32,064
- Release Date
- 2024-05-07
- License
- MIT
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HuggingFace
How Much VRAM Does Phi 3 Medium 4k Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 6.7 GB | 7.1 GB | 5.93 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 7.5 GB | 7.9 GB | 6.81 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 9.1 GB | 9.5 GB | 8.38 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 10.7 GB | 11.1 GB | 9.95 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 12.2 GB | 12.7 GB | 11.52 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 14.7 GB | 15.1 GB | 13.96 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 28.6 GB | 29.1 GB | 27.92 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 3 Medium 4k Instruct?
Q4_K_M · 9.1 GBPhi 3 Medium 4k Instruct (Q4_K_M) requires 9.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 12+ GB is recommended. Using the full 4K context window can add up to 0.4 GB, bringing total usage to 9.5 GB. 39 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Phi 3 Medium 4k Instruct?
Q4_K_M · 9.1 GB49 devices with unified memory can run Phi 3 Medium 4k Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, iPad Pro M5 13" (16 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Phi 3 Medium 4k Instruct need?
Phi 3 Medium 4k Instruct requires 9.1 GB of VRAM at Q4_K_M, or 28.6 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 14.0B × 4.8 bits ÷ 8 = 8.4 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 1.1 GB (at full 4K context)
VRAM usage by quantization
Q4_K_M9.1 GBQ4_K_M + full context9.5 GB- Can NVIDIA GeForce RTX 4090 run Phi 3 Medium 4k Instruct?
Yes, at Q8_0 (14.7 GB) or lower. Higher quantizations like BF16 (28.6 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Phi 3 Medium 4k Instruct?
For Phi 3 Medium 4k Instruct, Q4_K_M (9.1 GB) offers the best balance of quality and VRAM usage. Q5_K_M (10.7 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 6.7 GB.
VRAM requirement by quantization
Q2_K6.7 GBQ4_K_M ★9.1 GBQ5_K_M10.7 GBQ6_K12.2 GBQ8_014.7 GBBF1628.6 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Phi 3 Medium 4k Instruct on a Mac?
Phi 3 Medium 4k Instruct requires at least 6.7 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 Medium 4k Instruct locally?
Yes — Phi 3 Medium 4k Instruct can run locally on consumer hardware. At Q4_K_M quantization it needs 9.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Phi 3 Medium 4k Instruct?
At Q4_K_M, Phi 3 Medium 4k Instruct can reach ~484 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~72 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 ÷ 9.1 × 0.65 = ~571 tok/s
Estimated speed at Q4_K_M (9.1 GB)
~571 tok/s~72 tok/s~571 tok/s~484 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 Medium 4k Instruct?
At Q4_K_M, the download is about 8.38 GB. The full-precision BF16 version is 27.92 GB. The smallest option (Q2_K) is 5.93 GB.
- Which GPUs can run Phi 3 Medium 4k Instruct?
39 consumer GPUs can run Phi 3 Medium 4k Instruct at Q4_K_M (9.1 GB). Top options include AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 6900 XT, AMD Radeon RX 6700 XT. 26 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Phi 3 Medium 4k Instruct?
52 devices with unified memory can run Phi 3 Medium 4k Instruct at Q4_K_M (9.1 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.