# Can I run Qwen3-32B on an RTX 4060 Ti 16GB? > No. Qwen3-32B needs about 23.8 GB at Q4_K_M, and RTX 4060 Ti 16GB has 16.0 GB usable. What to run instead. Source: https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/rtx-4060-ti-16gb/ [Home](https://whatcanirunai.vercel.app/) / [Can I run it?](https://whatcanirunai.vercel.app/can-i-run/) / [Qwen3-32B](https://whatcanirunai.vercel.app/models/qwen3-32b/) / Qwen3-32B on an RTX 4060 Ti 16GB **No.** Qwen3-32B at Q4\_K\_M needs about 23.8 GB, and the GeForce RTX 4060 Ti 16GB has 16.0 GB usable at a \~4k context. # Can I run Qwen3-32B on an RTX 4060 Ti 16GB? Qwen3-32B at Q4\_K\_M needs about 23.8 GB, and the GeForce RTX 4060 Ti 16GB has 16.0 GB usable. That figure is 16 GB of VRAM on the spec sheet, of which 16.0 GB is usable for weights and cache, at 288 GB/s memory bandwidth. Alibaba’s dense 32B Qwen3, a common “best quality that still fits a 24 GB card” pick. These are estimates from [our formula](https://whatcanirunai.vercel.app/method/), not measured benchmarks. Quant that fits None of Q4, Q8 or FP16 needs 23.8 GB Memory needed 23.8 GB at Q4\_K\_M, \~4k context Usable VRAM 16.0 GB of 16 GB VRAM Est. tokens/sec n/a does not fit [Qwen3-32B](https://whatcanirunai.vercel.app/models/qwen3-32b/) · [GeForce RTX 4060 Ti 16GB](https://whatcanirunai.vercel.app/hardware/rtx-4060-ti-16gb/) · [Everything this machine runs](https://whatcanirunai.vercel.app/what-can-i-run/rtx-4060-ti-16gb/) · [Q4 vs Q8 vs FP16](https://whatcanirunai.vercel.app/quantization/) ## Memory at each quant | Quant | Need vs 16.0 GB | Verdict | | -------- | --------------- | ------------- | | Q4\_K\_M | 23.8 GB | Q4Won’t fit | | Q8\_0 | 41.0 GB | Q8Won’t fit | | FP16 | 76.4 GB | FP16Won’t fit | ## Estimated tokens per second No speed estimate: Qwen3-32B does not fit in the 16.0 GB this machine can use, so we do not publish a tokens/sec figure for CPU offload. ## Context headroom Even at a short \~4k context, the weights do not fit. Extending context would only increase the gap. __Context headroom for Qwen3-32B on an RTX 4060 Ti 16GB at Q4\_K\_M__ | Context | KV cache (FP16) | Total at Q4\_K\_M | Verdict | | ------- | --------------- | ----------------- | --------- | | 4k | 1.1 GB | 23.8 GB | Won’t fit | | 8k | 2.1 GB | 24.9 GB | Won’t fit | | 16k | 4.3 GB | 27.1 GB | Won’t fit | | 32k | 8.6 GB | 31.4 GB | Won’t fit | | 64k | 17.2 GB | 39.9 GB | Won’t fit | | 128k | 34.4 GB | 57.1 GB | Won’t fit | From the model’s config. Try other sizes in the [VRAM calculator](https://whatcanirunai.vercel.app/vram-calculator/) or see the [context length guide](https://whatcanirunai.vercel.app/context-length/). ## What to do instead * Run [gpt-oss-20b](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-20b/rtx-4060-ti-16gb/) instead (15.6 GB at Q4). It is a different model, not a drop-in, but it does fit this machine. * To hold Qwen3-32B at Q4, you need about 23.8 GB. The [GeForce RTX 3090](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/rtx-3090/) has 24.0 GB usable ($1499 list price). * CPU offload can load the weights anyway, but it is slow. We do not publish an offload tokens/sec figure. * See the [best GPU for this model family](https://whatcanirunai.vercel.app/best-gpu-for/qwen3/) and the [Qwen3-32B model page](https://whatcanirunai.vercel.app/models/qwen3-32b/). ## Other hardware for Qwen3-32B * [Qwen3-32B on an RTX 3060 12GB](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/rtx-3060-12gb/)Won’t fit * [Qwen3-32B on an RTX 3070 Ti](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/rtx-3070-ti/)Won’t fit * [Qwen3-32B on an RTX 3080](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/rtx-3080-10gb/)Won’t fit * [Qwen3-32B on an RTX 3090](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/rtx-3090/)Tight * [Qwen3-32B on an RTX 4070 Ti Super](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/rtx-4070-ti-super/)Won’t fit * [Qwen3-32B on an RTX 4090](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/rtx-4090/)Tight ## Other models on an RTX 4060 Ti 16GB * [Qwen3-30B-A3B on an RTX 4060 Ti 16GB](https://whatcanirunai.vercel.app/can-i-run/qwen3-30b-a3b/rtx-4060-ti-16gb/)Won’t fit * [Qwen3.8-27B on an RTX 4060 Ti 16GB](https://whatcanirunai.vercel.app/can-i-run/qwen3.8-27b/rtx-4060-ti-16gb/)Won’t fit * [Gemma 3 27B on an RTX 4060 Ti 16GB](https://whatcanirunai.vercel.app/can-i-run/gemma-3-27b-it/rtx-4060-ti-16gb/)Won’t fit * [Gemma 4 31B on an RTX 4060 Ti 16GB](https://whatcanirunai.vercel.app/can-i-run/gemma-4-31b-it/rtx-4060-ti-16gb/)Won’t fit * [gpt-oss-20b on an RTX 4060 Ti 16GB](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-20b/rtx-4060-ti-16gb/)Tight * [Mistral Small 24B on an RTX 4060 Ti 16GB](https://whatcanirunai.vercel.app/can-i-run/mistral-small-3.2-24b-instruct-2506/rtx-4060-ti-16gb/)Won’t fit ## Quick answers Can I run Qwen3-32B on an RTX 4060 Ti 16GB? No. Qwen3-32B at Q4\_K\_M needs about 23.8 GB, and the GeForce RTX 4060 Ti 16GB has 16.0 GB usable. How much VRAM does Qwen3-32B need? About 23.8 GB at Q4\_K\_M, 41.0 GB at Q8\_0 and 76.4 GB at FP16, estimated for a \~4k context. How fast is Qwen3-32B on an RTX 4060 Ti 16GB? We do not estimate tokens/sec when the model does not fit. CPU offload is possible but slow. What context length can I use? The weights already do not fit, so longer context is not the limiting factor. --- Agent guide: https://whatcanirunai.vercel.app/llms.txt · JSON API: https://whatcanirunai.vercel.app/developers/