# Gemma 4 E4B vs Qwen3-4B | What Can I Run AI? > Gemma 4 E4B (~6.6 GB at Q4) vs Qwen3-4B (~3.8 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-e4b-it-vs-qwen3-4b/ # Gemma 4 E4B vs Qwen3-4B: 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. **Qwen3-4B** is lighter: about 3.8 GB at Q4 against 6.6 GB for Gemma 4 E4B, so it runs on 82 tracked devices vs 81\. It should also be faster on the same hardware, since it reads 4.0B parameters per token against 8.0B. 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 E4B vs Qwen3-4B: specs and memory__ | [Gemma 4 E4B](https://whatcanirunai.vercel.app/models/gemma-4-e4b-it/) | [Qwen3-4B](https://whatcanirunai.vercel.app/models/qwen3-4b/) | | | -------------------------------------- | -------------------------------------------------- | -------------------------------------------------- | | Maker | google | Qwen | | License | apache-2.0 | apache-2.0 | | Type | Dense | Dense | | Total parameters | 8.0B | 4.0B | | Active per token | 8.0B all | 4.0B all | | Memory at Q4\_K\_M | **6.6 GB** | **3.8 GB** | | Memory at Q8\_0 | **10.8 GB** | **5.9 GB** | | Memory at FP16 | **19.4 GB** | **10.3 GB** | | KV cache growth | not modeled | 147 MB per 1k tokens, FP16 | | Memory at Q4, 32k context | cache not modeled | 8.0 GB 4.8 GB cache | | Cheapest GPU at Q4 | [Intel Arc B570](https://whatcanirunai.vercel.app/hardware/arc-b570/) $219 | [Intel Arc B570](https://whatcanirunai.vercel.app/hardware/arc-b570/) $219 | | Cheapest Mac / mini PC at Q4 | [Mac mini M6 16GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m6-16/) $899 | [Mac mini M6 16GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m6-16/) $899 | | Cheapest GPU at Q8 | [Intel Arc B580](https://whatcanirunai.vercel.app/hardware/arc-b580/) $249 | [Intel Arc B570](https://whatcanirunai.vercel.app/hardware/arc-b570/) $219 | | Tracked devices that run it at Q4 | 81 of 82 | 82 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 E4B vs Qwen3-4B__ | Device | Gemma 4 E4B | Qwen3-4B | | --------------------------------------------------------------------------------------------------------------- | ----------- | -------- | | [GeForce RTX 3060 12GB](https://whatcanirunai.vercel.app/hardware/rtx-3060-12gb/)12 GB usable · 360 GB/s · $329 | 45–63 | 89–125 | | [GeForce RTX 4060 Ti 16GB](https://whatcanirunai.vercel.app/hardware/rtx-4060-ti-16gb/)16 GB usable · 288 GB/s · $499 | 36–50 | 71–100 | | [GeForce RTX 5080](https://whatcanirunai.vercel.app/hardware/rtx-5080/)16 GB usable · 960 GB/s · $999 | 119–168 | 236–335 | | [GeForce RTX 3090](https://whatcanirunai.vercel.app/hardware/rtx-3090/)24 GB usable · 936 GB/s · $1,499 | 116–164 | 230–326 | | [Radeon RX 7900 XTX](https://whatcanirunai.vercel.app/hardware/rx-7900-xtx/)24 GB usable · 960 GB/s · $999 | 119–168 | 236–335 | | [GeForce RTX 5090](https://whatcanirunai.vercel.app/hardware/rtx-5090/)32 GB usable · 1792 GB/s · $1,999 | 222–314 | 441–625 | | [Mac mini M6 24GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m6-24/)18 GB usable · 170 GB/s · $1,299 | 16–28 | 31–56 | | [Mac mini M6 32GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m6-32/)24 GB usable · 170 GB/s · $1,799 | 16–28 | 31–56 | | [Mac mini M5 Pro 64GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m5-pro-64/)48 GB usable · 307 GB/s · $3,199 | 28–51 | 57–101 | | [Framework Desktop (Ryzen AI Max+ 395) 128GB](https://whatcanirunai.vercel.app/hardware/framework-desktop-128/)96 GB usable · 256 GB/s · $3,449 | 24–42 | 47–84 | | [MacBook Pro M5 Max 128GB](https://whatcanirunai.vercel.app/hardware/macbook-pro-m5-max-128/)96 GB usable · 614 GB/s · $6,699 | 57–101 | 113–201 | ## Quick answers Which needs less VRAM, Gemma 4 E4B or Qwen3-4B? Qwen3-4B: about 3.8 GB at Q4\_K\_M, against 6.6 GB for Gemma 4 E4B. At Q8\_0 it's 5.9 GB vs 10.8 GB. Which is faster locally, Gemma 4 E4B or Qwen3-4B? Decode speed is limited by how many bytes are read per token. Qwen3-4B reads 4.0B parameters per token and Gemma 4 E4B reads 8.0B, so Qwen3-4B should be faster on the same machine. Our estimate on the GeForce RTX 3060 12GB at Q4 is 89–125 tok/s vs 45–63 tok/s. These are estimates, not benchmarks. What is the cheapest GPU for Gemma 4 E4B and Qwen3-4B? At Q4: Intel Arc B570 ($219) for Gemma 4 E4B; Intel Arc B570 ($219) for Qwen3-4B. Prices are dated list-price snapshots. Can a Mac run Gemma 4 E4B and Qwen3-4B? The cheapest Mac or mini PC we track for Gemma 4 E4B is the Mac mini M6 16GB ($899). For Qwen3-4B it's the Mac mini M6 16GB ($899). Which is better, Gemma 4 E4B or Qwen3-4B? 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. [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/