Phi 4 Mini Reasoning Abliterated — Hardware Requirements & GPU Compatibility
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Phi 4 Mini Reasoning Abliterated is a 3.8B-parameter open language model from huihui-ai in the Phi 4 family. At Q4_K_M it needs about 2.53 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 32K)Best on 24 GB (Q4_K_M · ~259 tok/s · 32K)Macs from 8 GB (Q4_K_M · ~28 tok/s)
MIT
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Phi 4 Mini Reasoning Abliterated
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve huihui-ai/Phi-4-mini-reasoning-abliterated --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (7 GB headroom) · Q4_K_M
- Generation speed
- ~93 tok/s
- generation speed
- Cost per 1M output tokens
- $0.23
- per 1M output tokens
How Much VRAM Does Phi 4 Mini Reasoning Abliterated Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 1.8 GB | 6.3 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~366 tok/s | |
| 2.1 GB | 6.5 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~318 tok/s | |
| 2.5 GB | 7 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~259 tok/s | |
| 3.0 GB | 7.5 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~218 tok/s | |
| 3.5 GB | 8 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~188 tok/s | |
| 4.2 GB | 8.7 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~155 tok/s | |
| 8.4 GB | 12.9 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 32K context · ~78 tok/s |
Selected Q4_K_M
4-bit medium quantization4.6GBat 16K context
- Weights
- 1.9 GB
- KV cache
- 2.4 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 19.4 GB free · ~259 tok/s
≥50 tok/s needs ≥194 GB/s · RTX 5090 ~460, RTX 3090 Ti ~259
Which Hardware Can Run Phi 4 Mini Reasoning Abliterated?
Q4_K_M · 2.5 GBPhi 4 Mini Reasoning Abliterated (Q4_K_M) requires 2.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 4+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Phi 4 Mini Reasoning Abliterated, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 3.4 GB free · up to 32K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~195Simulate output on RTX 3080RTX 2080 Ti~158Simulate output on RTX 2080 TiRTX 3070 Ti~156Simulate output on RTX 3070 TiM3*8 GB~28Simulate output on iMac 24" M3 (8 GB)Orin Nano~26Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~19Simulate output on iPhone 1720 GB
fits · 15.4 GB free · up to 32K ctx
1 GPU
RX 7900 XT~190Simulate output on RX 7900 XT32–40 GB
fits · 27.4 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~460Simulate output on RTX 5090M5 Max36 GB~127Simulate output on Mac Studio M5 Max (36 GB)M532 GB~43Simulate output on MacBook Air 13" M5 (32 GB)Orin~53Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~35Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Phi 4 Mini Reasoning Abliterated
Phi 4 family · 7 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Granite Vision 4.1 4BIBM | — | 4 GB | ~228 | same size |
| Qwen3 4B Instruct 2507Alibaba | — | 4.2 GB | ~226 | same size |
| MiniCPM V 4OpenBMB | — | 3.1 GB | ~236 | same size |
| Qwen2.5 VL 3B InstructAlibaba | — | 3.2 GB | ~249 | same size |
| Phi 3 Mini 4k InstructMicrosoft | — | 4.2 GB | ~193 | same size |
| CAJAL 4BAgnuxo | — | 4.8 GB | ~248 | same size |
Specifications
- Publisher
- huihui-ai
- Family
- Phi 4
- Parameters
- 3.8B
- Release Date
- 2025-05-06
- License
- MIT
Frequently Asked Questions
How much VRAM does Phi 4 Mini Reasoning Abliterated need?
Phi 4 Mini Reasoning Abliterated requires 2.5 GB of VRAM at Q4_K_M, or 8.4 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 3.8B × 4.8 bits ÷ 8 = 2.3 GB
KV Cache + Overhead ≈ 0.2 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
VRAM usage by quantization
What's the best quantization for Phi 4 Mini Reasoning Abliterated?
For Phi 4 Mini Reasoning Abliterated, Q4_K_M (2.5 GB) offers the best balance of quality and VRAM usage. Q5_K_M (3.0 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 1.8 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Phi 4 Mini Reasoning Abliterated on a Mac?
Yes — Mac Mini M2 (8 GB) and 145 other Macs can run Phi 4 Mini Reasoning Abliterated. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 2.5 GB of usable unified memory (RAM minus macOS overhead).
Can I run Phi 4 Mini Reasoning Abliterated locally?
Yes — Phi 4 Mini Reasoning Abliterated can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Phi 4 Mini Reasoning Abliterated?
At Q4_K_M, Phi 4 Mini Reasoning Abliterated can reach ~1897 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~259 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 ÷ 2.53 × 0.65 = ~2055 tok/s
Estimated speed at Q4_K_M (2.5 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Phi 4 Mini Reasoning Abliterated?
At Q4_K_M, the download is about 2.30 GB. The full-precision BF16 version is 7.67 GB. The smallest option (Q2_K) is 1.63 GB.
Which GPUs can run Phi 4 Mini Reasoning Abliterated?
52 consumer GPUs can run Phi 4 Mini Reasoning Abliterated at Q4_K_M. 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 4 Mini Reasoning Abliterated?
163 devices with unified memory can run Phi 4 Mini Reasoning Abliterated at Q4_K_M (2.5 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.