# Can I run R1 Distill 32B on an RTX 5080? | What Can I Run AI > No. R1 Distill 32B needs about 23.8 GB at Q4_K_M, and RTX 5080 has 16.0 GB usable. What to run instead. Source: https://whatcanirunai.vercel.app/can-i-run/deepseek-r1-distill-qwen-32b/rtx-5080/ [Home](https://whatcanirunai.vercel.app/) / [Can I run it?](https://whatcanirunai.vercel.app/can-i-run/) / [R1 Distill 32B](https://whatcanirunai.vercel.app/models/deepseek-r1-distill-qwen-32b/) / R1 Distill 32B on an RTX 5080 **No.** R1 Distill 32B at Q4\_K\_M needs about 23.8 GB, and the GeForce RTX 5080 has 16.0 GB usable at a \~4k context. # Can I run R1 Distill 32B on an RTX 5080? R1 Distill 32B at Q4\_K\_M needs about 23.8 GB, and the GeForce RTX 5080 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 960 GB/s memory bandwidth. A 32B DeepSeek-R1 distill on Qwen. Memory need matches Qwen3-32B; the weights are distilled from DeepSeek-R1\. 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 [DeepSeek-R1-Distill-Qwen-32B](https://whatcanirunai.vercel.app/models/deepseek-r1-distill-qwen-32b/) · [GeForce RTX 5080](https://whatcanirunai.vercel.app/hardware/rtx-5080/) · [Everything this machine runs](https://whatcanirunai.vercel.app/what-can-i-run/rtx-5080/) · [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Estimated tokens per second No speed estimate: R1 Distill 32B does not fit in the 16.0 GB this machine can use, so we do not publish a tokens/sec figure for CPU offload.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 R1 Distill 32B on an RTX 5080 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-5080/) instead (15.6 GB at Q4). It is a different model, not a drop-in, but it does fit this machine. * To hold R1 Distill 32B at Q4, you need about 23.8 GB. The [GeForce RTX 3090](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1-distill-qwen-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/deepseek-r1/) and the [DeepSeek-R1-Distill-Qwen-32B model page](https://whatcanirunai.vercel.app/models/deepseek-r1-distill-qwen-32b/). ## Other hardware for R1 Distill 32B * [R1 Distill 32B on an RTX 4090](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1-distill-qwen-32b/rtx-4090/)Tight * [R1 Distill 32B on an RTX 3090](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1-distill-qwen-32b/rtx-3090/)Tight * [R1 Distill 32B on an RTX 5090](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1-distill-qwen-32b/rtx-5090/)Fits * [R1 Distill 32B on a Mac mini 24GB](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1-distill-qwen-32b/mac-mini-m6-24/)Won’t fit * [R1 Distill 32B on an RX 7900 XTX](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1-distill-qwen-32b/rx-7900-xtx/)Tight ## Other models on an RTX 5080 * [Qwen3-32B on an RTX 5080](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/rtx-5080/)Won’t fit * [Qwen3-30B-A3B on an RTX 5080](https://whatcanirunai.vercel.app/can-i-run/qwen3-30b-a3b/rtx-5080/)Won’t fit * [Qwen3.8-27B on an RTX 5080](https://whatcanirunai.vercel.app/can-i-run/qwen3.8-27b/rtx-5080/)Won’t fit * [Gemma 3 27B on an RTX 5080](https://whatcanirunai.vercel.app/can-i-run/gemma-3-27b-it/rtx-5080/)Won’t fit * [Gemma 4 31B on an RTX 5080](https://whatcanirunai.vercel.app/can-i-run/gemma-4-31b-it/rtx-5080/)Won’t fit * [gpt-oss-20b on an RTX 5080](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-20b/rtx-5080/)Tight ## Quick answers Can I run R1 Distill 32B on an RTX 5080? No. R1 Distill 32B at Q4\_K\_M needs about 23.8 GB, and the GeForce RTX 5080 has 16.0 GB usable. How much VRAM does R1 Distill 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 R1 Distill 32B on an RTX 5080? 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/