# Qwen3.8-27B vs Qwen3-32B | What Can I Run AI? > Qwen3.8-27B (~20.4 GB at Q4) vs Qwen3-32B (~23.8 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/qwen3.8-27b-vs-qwen3-32b/ # Qwen3.8-27B vs Qwen3-32B: 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. **Qwen3.8-27B** is lighter: about 20.4 GB at Q4 against 23.8 GB for Qwen3-32B, so it runs on 25 tracked devices vs 16\. It should also be faster on the same hardware, since it reads 27.8B parameters per token against 32.8B. Which one gives better answers depends on your task, so check benchmarks for that; this page covers what it takes to run them. __Qwen3.8-27B vs Qwen3-32B: specs and memory__ | [Qwen3.8-27B](https://whatcanirunai.vercel.app/models/qwen3.8-27b/) | [Qwen3-32B](https://whatcanirunai.vercel.app/models/qwen3-32b/) | | | ----------------------------------- | ---------------------------------------------------------- | ------------------------------------------------------------ | | Maker | Qwen | Qwen | | License | apache-2.0 | apache-2.0 | | Type | Dense | Dense | | Total parameters | 27.8B | 32.8B | | Active per token | 27.8B all | 32.8B all | | Memory at Q4\_K\_M | **20.4 GB** | **23.8 GB** | | Memory at Q8\_0 | **34.9 GB** | **41.0 GB** | | Memory at FP16 | **64.9 GB** | **76.4 GB** | | KV cache growth | 66 MB per 1k tokens, FP16 | 262 MB per 1k tokens, FP16 | | Memory at Q4, 32k context | 22.2 GB 2.1 GB cache | 31.4 GB 8.6 GB cache | | Cheapest GPU at Q4 | [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 | | Cheapest Mac / mini PC at Q4 | [Mac mini M6 32GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m6-32/) $1,799 | [Mac mini M5 Pro 64GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m5-pro-64/) $3,199 | | Cheapest GPU at Q8 | [NVIDIA RTX 6000 Ada 48GB](https://whatcanirunai.vercel.app/hardware/rtx-6000-ada/) $6,799 | [NVIDIA RTX 6000 Ada 48GB](https://whatcanirunai.vercel.app/hardware/rtx-6000-ada/) $6,799 | | Tracked devices that run it at Q4 | 25 of 82 | 16 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, Qwen3.8-27B vs Qwen3-32B__ | Device | Qwen3.8-27B | Qwen3-32B | | --------------------------------------------------------------------------------------------------------------- | ----------- | ----------- | | [GeForce RTX 3060 12GB](https://whatcanirunai.vercel.app/hardware/rtx-3060-12gb/)12 GB usable · 360 GB/s · $329 | doesn’t fit | doesn’t fit | | [GeForce RTX 4060 Ti 16GB](https://whatcanirunai.vercel.app/hardware/rtx-4060-ti-16gb/)16 GB usable · 288 GB/s · $499 | doesn’t fit | doesn’t fit | | [GeForce RTX 5080](https://whatcanirunai.vercel.app/hardware/rtx-5080/)16 GB usable · 960 GB/s · $999 | doesn’t fit | doesn’t fit | | [GeForce RTX 3090](https://whatcanirunai.vercel.app/hardware/rtx-3090/)24 GB usable · 936 GB/s · $1,499 | 33–47 | tight fit | | [Radeon RX 7900 XTX](https://whatcanirunai.vercel.app/hardware/rx-7900-xtx/)24 GB usable · 960 GB/s · $999 | 34–48 | tight fit | | [GeForce RTX 5090](https://whatcanirunai.vercel.app/hardware/rtx-5090/)32 GB usable · 1792 GB/s · $1,999 | 64–90 | 54–77 | | [Mac mini M6 24GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m6-24/)18 GB usable · 170 GB/s · $1,299 | doesn’t fit | doesn’t fit | | [Mac mini M6 32GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m6-32/)24 GB usable · 170 GB/s · $1,799 | 5–8 | tight fit | | [Mac mini M5 Pro 64GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m5-pro-64/)48 GB usable · 307 GB/s · $3,199 | 8–15 | 7–12 | | [Framework Desktop (Ryzen AI Max+ 395) 128GB](https://whatcanirunai.vercel.app/hardware/framework-desktop-128/)96 GB usable · 256 GB/s · $3,449 | 7–12 | 6–10 | | [MacBook Pro M5 Max 128GB](https://whatcanirunai.vercel.app/hardware/macbook-pro-m5-max-128/)96 GB usable · 614 GB/s · $6,699 | 16–29 | 14–25 | ## Hardware that runs only one of them **Run Qwen3.8-27B but not Qwen3-32B:** [Radeon RX 7900 XTX](https://whatcanirunai.vercel.app/hardware/rx-7900-xtx/), [GeForce RTX 3090](https://whatcanirunai.vercel.app/hardware/rtx-3090/), [GeForce RTX 4090](https://whatcanirunai.vercel.app/hardware/rtx-4090/), [Mac mini M6 32GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m6-32/), [GeForce RTX 3090 Ti](https://whatcanirunai.vercel.app/hardware/rtx-3090-ti/), [MacBook Pro M6 32GB](https://whatcanirunai.vercel.app/hardware/macbook-pro-m6-32/). Current priced devices, cheapest first, at Q4 with \~4k context. ## Quick answers Which needs less VRAM, Qwen3.8-27B or Qwen3-32B? Qwen3.8-27B: about 20.4 GB at Q4\_K\_M, against 23.8 GB for Qwen3-32B. At Q8\_0 it's 34.9 GB vs 41.0 GB. Which is faster locally, Qwen3.8-27B or Qwen3-32B? Decode speed is limited by how many bytes are read per token. Qwen3.8-27B reads 27.8B parameters per token and Qwen3-32B reads 32.8B, so Qwen3.8-27B should be faster on the same machine. Our estimate on the GeForce RTX 5090 at Q4 is 64–90 tok/s vs 54–77 tok/s. These are estimates, not benchmarks. What is the cheapest GPU for Qwen3.8-27B and Qwen3-32B? At Q4: Intel Arc Pro B70 32GB ($949) for Qwen3.8-27B; Intel Arc Pro B70 32GB ($949) for Qwen3-32B. Prices are dated list-price snapshots. Can a Mac run Qwen3.8-27B and Qwen3-32B? The cheapest Mac or mini PC we track for Qwen3.8-27B is the Mac mini M6 32GB ($1,799). For Qwen3-32B it's the Mac mini M5 Pro 64GB ($3,199). Which is better, Qwen3.8-27B or Qwen3-32B? 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 * [Qwen3.8-27B vs Gemma 4 31B](https://whatcanirunai.vercel.app/compare/qwen3.8-27b-vs-gemma-4-31b-it/)20.4 vs 22.8 GB * [DeepSeek-R1-Distill-Qwen-32B vs Qwen3-32B](https://whatcanirunai.vercel.app/compare/deepseek-r1-distill-qwen-32b-vs-qwen3-32b/)23.8 vs 23.8 GB * [Nanbeige4.3-33B vs Qwen3.8-27B](https://whatcanirunai.vercel.app/compare/nanbeige4.3-33b-vs-qwen3.8-27b/)24.1 vs 20.4 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/