# Can I run Qwen3-Coder 30B on an RTX 5070 Ti? > No. Qwen3-Coder 30B needs about 22.3 GB at Q4_K_M, and RTX 5070 Ti has 16.0 GB usable. What to run instead. Source: https://whatcanirunai.vercel.app/can-i-run/qwen3-coder-30b-a3b-instruct/rtx-5070-ti/ [Home](https://whatcanirunai.vercel.app/) / [Can I run it?](https://whatcanirunai.vercel.app/can-i-run/) / [Qwen3-Coder 30B](https://whatcanirunai.vercel.app/models/qwen3-coder-30b-a3b-instruct/) / Qwen3-Coder 30B on an RTX 5070 Ti **No.** Qwen3-Coder 30B at Q4\_K\_M needs about 22.3 GB, and the GeForce RTX 5070 Ti has 16.0 GB usable at a \~4k context. # Can I run Qwen3-Coder 30B on an RTX 5070 Ti? Qwen3-Coder 30B at Q4\_K\_M needs about 22.3 GB, and the GeForce RTX 5070 Ti 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 896 GB/s memory bandwidth. The coding-tuned sibling of Qwen3-30B-A3B. Memory need matches the base 30B-A3B; the weights are trained for code. These are estimates from [our formula](https://whatcanirunai.vercel.app/method/), not measured benchmarks. Quant that fits None of Q4, Q8 or FP16 needs 22.3 GB Memory needed 22.3 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-Coder-30B-A3B-Instruct](https://whatcanirunai.vercel.app/models/qwen3-coder-30b-a3b-instruct/) · [GeForce RTX 5070 Ti](https://whatcanirunai.vercel.app/hardware/rtx-5070-ti/) · [Everything this machine runs](https://whatcanirunai.vercel.app/what-can-i-run/rtx-5070-ti/) · [Q4 vs Q8 vs FP16](https://whatcanirunai.vercel.app/quantization/) ## Memory at each quant | Quant | Need vs 16.0 GB | Verdict | | -------- | --------------- | ------------- | | Q4\_K\_M | 22.3 GB | Q4Won’t fit | | Q8\_0 | 38.3 GB | Q8Won’t fit | | FP16 | 71.2 GB | FP16Won’t fit | ## Estimated tokens per second No speed estimate: Qwen3-Coder 30B does not fit in the 16.0 GB this machine can use, so we do not publish a tokens/sec figure for CPU offload.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-Coder 30B on an RTX 5070 Ti at Q4\_K\_M__ | Context | KV cache (FP16) | Total at Q4\_K\_M | Verdict | | ------- | --------------- | ----------------- | --------- | | 4k | 0.4 GB | 22.3 GB | Won’t fit | | 8k | 0.8 GB | 22.7 GB | Won’t fit | | 16k | 1.6 GB | 23.5 GB | Won’t fit | | 32k | 3.2 GB | 25.1 GB | Won’t fit | | 64k | 6.4 GB | 28.3 GB | Won’t fit | | 128k | 12.9 GB | 34.8 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-5070-ti/) instead (15.6 GB at Q4). It is a different model, not a drop-in, but it does fit this machine. * To hold Qwen3-Coder 30B at Q4, you need about 22.3 GB. The [GeForce RTX 3090](https://whatcanirunai.vercel.app/can-i-run/qwen3-coder-30b-a3b-instruct/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/coding-models/) and the [Qwen3-Coder-30B-A3B-Instruct model page](https://whatcanirunai.vercel.app/models/qwen3-coder-30b-a3b-instruct/). ## Other hardware for Qwen3-Coder 30B * [Qwen3-Coder 30B on an RTX 4090](https://whatcanirunai.vercel.app/can-i-run/qwen3-coder-30b-a3b-instruct/rtx-4090/)Tight * [Qwen3-Coder 30B on an RTX 3090](https://whatcanirunai.vercel.app/can-i-run/qwen3-coder-30b-a3b-instruct/rtx-3090/)Tight * [Qwen3-Coder 30B on an RTX 3060 12GB](https://whatcanirunai.vercel.app/can-i-run/qwen3-coder-30b-a3b-instruct/rtx-3060-12gb/)Won’t fit * [Qwen3-Coder 30B on a Mac mini 32GB](https://whatcanirunai.vercel.app/can-i-run/qwen3-coder-30b-a3b-instruct/mac-mini-m6-32/)Tight * [Qwen3-Coder 30B on an Arc B580](https://whatcanirunai.vercel.app/can-i-run/qwen3-coder-30b-a3b-instruct/arc-b580/)Won’t fit ## Other models on an RTX 5070 Ti * [Qwen3-32B on an RTX 5070 Ti](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/rtx-5070-ti/)Won’t fit * [Qwen3-30B-A3B on an RTX 5070 Ti](https://whatcanirunai.vercel.app/can-i-run/qwen3-30b-a3b/rtx-5070-ti/)Won’t fit * [Qwen3.8-27B on an RTX 5070 Ti](https://whatcanirunai.vercel.app/can-i-run/qwen3.8-27b/rtx-5070-ti/)Won’t fit * [Gemma 3 27B on an RTX 5070 Ti](https://whatcanirunai.vercel.app/can-i-run/gemma-3-27b-it/rtx-5070-ti/)Won’t fit * [Gemma 4 31B on an RTX 5070 Ti](https://whatcanirunai.vercel.app/can-i-run/gemma-4-31b-it/rtx-5070-ti/)Won’t fit * [gpt-oss-20b on an RTX 5070 Ti](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-20b/rtx-5070-ti/)Tight ## Quick answers Can I run Qwen3-Coder 30B on an RTX 5070 Ti? No. Qwen3-Coder 30B at Q4\_K\_M needs about 22.3 GB, and the GeForce RTX 5070 Ti has 16.0 GB usable. How much VRAM does Qwen3-Coder 30B need? About 22.3 GB at Q4\_K\_M, 38.3 GB at Q8\_0 and 71.2 GB at FP16, estimated for a \~4k context. How fast is Qwen3-Coder 30B on an RTX 5070 Ti? 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/