# phi-4 vs Qwen3-14B | What Can I Run AI? > phi-4 (~11.2 GB at Q4) vs Qwen3-14B (~11.3 GB): memory at Q4/Q8/FP16, context cache, cheapest GPU and Mac, and est. tok/s side by side. Source: https://whatcanirunai.vercel.app/compare/phi-4-vs-qwen3-14b/ # phi-4 vs Qwen3-14B: VRAM and speed for running locally Side by side: how much memory each model needs at Q4, Q8 and FP16, how the KV cache grows with context, the cheapest GPU and Mac that run each, and estimated speed on 11 common machines. **phi-4** is lighter: about 11.2 GB at Q4 against 11.3 GB for Qwen3-14B, though both run on 48 of the devices we track. It should also be faster on the same hardware, since it reads 14.7B parameters per token against 14.8B. Which one gives better answers depends on your task, so check benchmarks for that; this page covers what it takes to run them. __phi-4 vs Qwen3-14B: specs and memory__ | [phi-4](https://whatcanirunai.vercel.app/models/phi-4/) | [Qwen3-14B](https://whatcanirunai.vercel.app/models/qwen3-14b/) | | | --------------------------------- | ----------------------------------------------------- | ----------------------------------------------------- | | Maker | microsoft | Qwen | | License | mit | apache-2.0 | | Type | Dense | Dense | | Total parameters | 14.7B | 14.8B | | Active per token | 14.7B all | 14.8B all | | Memory at Q4\_K\_M | **11.2 GB** | **11.3 GB** | | Memory at Q8\_0 | **18.9 GB** | **19.0 GB** | | Memory at FP16 | **34.7 GB** | **35.0 GB** | | KV cache growth | 205 MB per 1k tokens, FP16 | 164 MB per 1k tokens, FP16 | | Memory at Q4, 32k context | 17.1 GB 6.7 GB cache | 16.0 GB 5.4 GB cache | | Cheapest GPU at Q4 | [Radeon RX 7600 XT](https://whatcanirunai.vercel.app/hardware/rx-7600-xt/) $329 | [Radeon RX 7600 XT](https://whatcanirunai.vercel.app/hardware/rx-7600-xt/) $329 | | Cheapest Mac / mini PC at Q4 | [Mac mini M6 24GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m6-24/) $1,299 | [Mac mini M6 24GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m6-24/) $1,299 | | Cheapest GPU at Q8 | [Intel Arc Pro B70 32GB](https://whatcanirunai.vercel.app/hardware/arc-pro-b70/) $949 | [Intel Arc Pro B70 32GB](https://whatcanirunai.vercel.app/hardware/arc-pro-b70/) $949 | | Tracked devices that run it at Q4 | 48 of 82 | 48 of 82 | ## Estimated speed on the same hardware Decode tokens per second at Q4\_K\_M from memory bandwidth ([how we estimate](https://whatcanirunai.vercel.app/method/#speed)). MoE figures are upper bounds ("up to"). Estimates, not benchmarks. __Estimated tokens per second, phi-4 vs Qwen3-14B__ | Device | phi-4 | Qwen3-14B | | --------------------------------------------------------------------------------------------------------------- | --------- | --------- | | [GeForce RTX 3060 12GB](https://whatcanirunai.vercel.app/hardware/rtx-3060-12gb/)12 GB usable · 360 GB/s · $329 | tight fit | tight fit | | [GeForce RTX 4060 Ti 16GB](https://whatcanirunai.vercel.app/hardware/rtx-4060-ti-16gb/)16 GB usable · 288 GB/s · $499 | 19–28 | 19–27 | | [GeForce RTX 5080](https://whatcanirunai.vercel.app/hardware/rtx-5080/)16 GB usable · 960 GB/s · $999 | 65–92 | 64–91 | | [GeForce RTX 3090](https://whatcanirunai.vercel.app/hardware/rtx-3090/)24 GB usable · 936 GB/s · $1,499 | 63–90 | 63–89 | | [Radeon RX 7900 XTX](https://whatcanirunai.vercel.app/hardware/rx-7900-xtx/)24 GB usable · 960 GB/s · $999 | 65–92 | 64–91 | | [GeForce RTX 5090](https://whatcanirunai.vercel.app/hardware/rtx-5090/)32 GB usable · 1792 GB/s · $1,999 | 121–171 | 120–170 | | [Mac mini M6 24GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m6-24/)18 GB usable · 170 GB/s · $1,299 | 9–15 | 9–15 | | [Mac mini M6 32GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m6-32/)24 GB usable · 170 GB/s · $1,799 | 9–15 | 9–15 | | [Mac mini M5 Pro 64GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m5-pro-64/)48 GB usable · 307 GB/s · $3,199 | 16–28 | 15–27 | | [Framework Desktop (Ryzen AI Max+ 395) 128GB](https://whatcanirunai.vercel.app/hardware/framework-desktop-128/)96 GB usable · 256 GB/s · $3,449 | 13–23 | 13–23 | | [MacBook Pro M5 Max 128GB](https://whatcanirunai.vercel.app/hardware/macbook-pro-m5-max-128/)96 GB usable · 614 GB/s · $6,699 | 31–55 | 31–55 | ## Quick answers Which needs less VRAM, phi-4 or Qwen3-14B? phi-4: about 11.2 GB at Q4\_K\_M, against 11.3 GB for Qwen3-14B. At Q8\_0 it's 18.9 GB vs 19.0 GB. Which is faster locally, phi-4 or Qwen3-14B? Decode speed is limited by how many bytes are read per token. phi-4 reads 14.7B parameters per token and Qwen3-14B reads 14.8B, so phi-4 should be faster on the same machine. Our estimate on the GeForce RTX 4060 Ti 16GB at Q4 is 19–28 tok/s vs 19–27 tok/s. These are estimates, not benchmarks. What is the cheapest GPU for phi-4 and Qwen3-14B? At Q4: Radeon RX 7600 XT ($329) for phi-4; Radeon RX 7600 XT ($329) for Qwen3-14B. Prices are dated list-price snapshots. Can a Mac run phi-4 and Qwen3-14B? The cheapest Mac or mini PC we track for phi-4 is the Mac mini M6 24GB ($1,299). For Qwen3-14B it's the Mac mini M6 24GB ($1,299). Which is better, phi-4 or Qwen3-14B? We don't score answer quality. This comparison covers memory, context cache, hardware and speed. Pick the one that fits your hardware, then test both on your own prompts. ## Related comparisons * [Gemma 4 12B vs Qwen3-14B](https://whatcanirunai.vercel.app/compare/gemma-4-12b-it-vs-qwen3-14b/)9.3 vs 11.3 GB [All comparisons](https://whatcanirunai.vercel.app/compare/) · [MoE vs dense](https://whatcanirunai.vercel.app/moe-vs-dense/) · [Q4 vs Q8 vs FP16](https://whatcanirunai.vercel.app/quantization/) · [Context length and VRAM](https://whatcanirunai.vercel.app/context-length/) · [VRAM calculator](https://whatcanirunai.vercel.app/vram-calculator/) --- Agent guide: https://whatcanirunai.vercel.app/llms.txt · JSON API: https://whatcanirunai.vercel.app/developers/