# Gemma 4 12B vs Qwen3-14B | What Can I Run AI? > Gemma 4 12B (~9.3 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/gemma-4-12b-it-vs-qwen3-14b/ # Gemma 4 12B 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. **Gemma 4 12B** is lighter: about 9.3 GB at Q4 against 11.3 GB for Qwen3-14B, so it runs on 63 tracked devices vs 48\. It should also be faster on the same hardware, since it reads 12.0B 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. __Gemma 4 12B vs Qwen3-14B: specs and memory__ | [Gemma 4 12B](https://whatcanirunai.vercel.app/models/gemma-4-12b-it/) | [Qwen3-14B](https://whatcanirunai.vercel.app/models/qwen3-14b/) | | | -------------------------------------- | -------------------------------------------------- | ----------------------------------------------------- | | Maker | google | Qwen | | License | apache-2.0 | apache-2.0 | | Type | Dense | Dense | | Total parameters | 12.0B | 14.8B | | Active per token | 12.0B all | 14.8B all | | Memory at Q4\_K\_M | **9.3 GB** | **11.3 GB** | | Memory at Q8\_0 | **15.6 GB** | **19.0 GB** | | Memory at FP16 | **28.5 GB** | **35.0 GB** | | KV cache growth | not modeled | 164 MB per 1k tokens, FP16 | | Memory at Q4, 32k context | cache not modeled | 16.0 GB 5.4 GB cache | | Cheapest GPU at Q4 | [Intel Arc B580](https://whatcanirunai.vercel.app/hardware/arc-b580/) $249 | [Radeon RX 7600 XT](https://whatcanirunai.vercel.app/hardware/rx-7600-xt/) $329 | | Cheapest Mac / mini PC at Q4 | [Mac mini M6 16GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m6-16/) $899 | [Mac mini M6 24GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m6-24/) $1,299 | | Cheapest GPU at Q8 | [Radeon RX 7900 XT](https://whatcanirunai.vercel.app/hardware/rx-7900-xt/) $899 | [Intel Arc Pro B70 32GB](https://whatcanirunai.vercel.app/hardware/arc-pro-b70/) $949 | | Tracked devices that run it at Q4 | 63 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, Gemma 4 12B vs Qwen3-14B__ | Device | Gemma 4 12B | Qwen3-14B | | --------------------------------------------------------------------------------------------------------------- | ----------- | --------- | | [GeForce RTX 3060 12GB](https://whatcanirunai.vercel.app/hardware/rtx-3060-12gb/)12 GB usable · 360 GB/s · $329 | 30–42 | tight fit | | [GeForce RTX 4060 Ti 16GB](https://whatcanirunai.vercel.app/hardware/rtx-4060-ti-16gb/)16 GB usable · 288 GB/s · $499 | 24–34 | 19–27 | | [GeForce RTX 5080](https://whatcanirunai.vercel.app/hardware/rtx-5080/)16 GB usable · 960 GB/s · $999 | 79–113 | 64–91 | | [GeForce RTX 3090](https://whatcanirunai.vercel.app/hardware/rtx-3090/)24 GB usable · 936 GB/s · $1,499 | 77–110 | 63–89 | | [Radeon RX 7900 XTX](https://whatcanirunai.vercel.app/hardware/rx-7900-xtx/)24 GB usable · 960 GB/s · $999 | 79–113 | 64–91 | | [GeForce RTX 5090](https://whatcanirunai.vercel.app/hardware/rtx-5090/)32 GB usable · 1792 GB/s · $1,999 | 148–210 | 120–170 | | [Mac mini M6 24GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m6-24/)18 GB usable · 170 GB/s · $1,299 | 11–19 | 9–15 | | [Mac mini M6 32GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m6-32/)24 GB usable · 170 GB/s · $1,799 | 11–19 | 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 | 19–34 | 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 | 16–28 | 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 | 38–68 | 31–55 | ## Hardware that runs only one of them **Run Gemma 4 12B but not Qwen3-14B:** [Intel Arc B580](https://whatcanirunai.vercel.app/hardware/arc-b580/), [GeForce RTX 3060 12GB](https://whatcanirunai.vercel.app/hardware/rtx-3060-12gb/), [Radeon RX 7700 XT](https://whatcanirunai.vercel.app/hardware/rx-7700-xt/), [GeForce RTX 5070](https://whatcanirunai.vercel.app/hardware/rtx-5070/), [GeForce RTX 4070](https://whatcanirunai.vercel.app/hardware/rtx-4070/), [GeForce RTX 4070 Super](https://whatcanirunai.vercel.app/hardware/rtx-4070-super/), [Mac mini M6 16GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m6-16/), [GeForce RTX 3080 Ti](https://whatcanirunai.vercel.app/hardware/rtx-3080-ti/). Current priced devices, cheapest first, at Q4 with \~4k context. ## Quick answers Which needs less VRAM, Gemma 4 12B or Qwen3-14B? Gemma 4 12B: about 9.3 GB at Q4\_K\_M, against 11.3 GB for Qwen3-14B. At Q8\_0 it's 15.6 GB vs 19.0 GB. Which is faster locally, Gemma 4 12B or Qwen3-14B? Decode speed is limited by how many bytes are read per token. Gemma 4 12B reads 12.0B parameters per token and Qwen3-14B reads 14.8B, so Gemma 4 12B should be faster on the same machine. Our estimate on the GeForce RTX 4060 Ti 16GB at Q4 is 24–34 tok/s vs 19–27 tok/s. These are estimates, not benchmarks. What is the cheapest GPU for Gemma 4 12B and Qwen3-14B? At Q4: Intel Arc B580 ($249) for Gemma 4 12B; Radeon RX 7600 XT ($329) for Qwen3-14B. Prices are dated list-price snapshots. Can a Mac run Gemma 4 12B and Qwen3-14B? The cheapest Mac or mini PC we track for Gemma 4 12B is the Mac mini M6 16GB ($899). For Qwen3-14B it's the Mac mini M6 24GB ($1,299). Which is better, Gemma 4 12B 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 * [phi-4 vs Qwen3-14B](https://whatcanirunai.vercel.app/compare/phi-4-vs-qwen3-14b/)11.2 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/