# Can I run DeepSeek-V3.2 on an RTX 5090? | What Can I Run AI?

> No. DeepSeek-V3.2 needs about 478.8 GB at Q4_K_M, and RTX 5090 has 32.0 GB usable. What to run instead.

Source: https://whatcanirunai.vercel.app/can-i-run/deepseek-v3.2/rtx-5090/

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**No.** DeepSeek-V3.2 at Q4\_K\_M needs about 478.8 GB, and the GeForce RTX 5090 has 32.0 GB usable at a \~4k context.

# Can I run DeepSeek-V3.2 on an RTX 5090?

DeepSeek-V3.2 at Q4\_K\_M needs about 478.8 GB, and the GeForce RTX 5090 has 32.0 GB usable. That figure is 32 GB of VRAM on the spec sheet, of which 32.0 GB is usable for weights and cache, at 1792 GB/s memory bandwidth. DeepSeek’s large V3.2 model. Like full R1, it is far beyond a single consumer GPU or current Mac Studio at Q4\. These are estimates from [our formula](https://whatcanirunai.vercel.app/method/), not measured benchmarks.

Quant that fits

None of Q4, Q8 or FP16 needs 478.8 GB

Memory needed

478.8 GB at Q4\_K\_M, \~4k context

Usable VRAM

32.0 GB of 32 GB VRAM

Est. tokens/sec

n/a does not fit

[DeepSeek-V3.2](https://whatcanirunai.vercel.app/models/deepseek-v3.2/) · [GeForce RTX 5090](https://whatcanirunai.vercel.app/hardware/rtx-5090/) · [Everything this machine runs](https://whatcanirunai.vercel.app/what-can-i-run/rtx-5090/) · [Q4 vs Q8 vs FP16](https://whatcanirunai.vercel.app/quantization/)

## Memory at each quant

| Quant    | Need vs 32.0 GB | Verdict       |
| -------- | --------------- | ------------- |
| Q4\_K\_M | 478.8 GB        | Q4Won’t fit   |
| Q8\_0    | 838.4 GB        | Q8Won’t fit   |
| FP16     | 1577.3 GB       | FP16Won’t fit |

## Estimated tokens per second

No speed estimate: DeepSeek-V3.2 does not fit in the 32.0 GB this machine can use, so we do not publish a tokens/sec figure for CPU offload.

## Context headroom

We do not have a published KV config for this model, so the memory need is the \~4k-context estimate only. Use the [VRAM calculator](https://whatcanirunai.vercel.app/vram-calculator/) to explore longer chats.

## What to do instead

* Run [R1 Distill 32B](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1-distill-qwen-32b/rtx-5090/) instead (23.8 GB at Q4). It is a different model, not a drop-in, but it does fit this machine.
* No single device we track has enough usable memory for DeepSeek-V3.2 at Q4 (478.8 GB needed). A smaller distill or a hosted API is the practical path.
* CPU offload can load the weights anyway, but it is slow. We do not publish an offload tokens/sec figure.
* Full hardware list: [DeepSeek-V3.2](https://whatcanirunai.vercel.app/models/deepseek-v3.2/). Specs for this machine: [GeForce RTX 5090](https://whatcanirunai.vercel.app/hardware/rtx-5090/).

## Other hardware for DeepSeek-V3.2

* [DeepSeek-V3.2 on a Mac Studio 512GB](https://whatcanirunai.vercel.app/can-i-run/deepseek-v3.2/mac-studio-m5-ultra-512/)Won’t fit
* [DeepSeek-V3.2 on a Mac Studio 256GB](https://whatcanirunai.vercel.app/can-i-run/deepseek-v3.2/mac-studio-m5-ultra-256/)Won’t fit
* [DeepSeek-V3.2 on an RTX 4090](https://whatcanirunai.vercel.app/can-i-run/deepseek-v3.2/rtx-4090/)Won’t fit

## Other models on an RTX 5090

* [Qwen3-32B on an RTX 5090](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/rtx-5090/)Fits
* [Qwen3-30B-A3B on an RTX 5090](https://whatcanirunai.vercel.app/can-i-run/qwen3-30b-a3b/rtx-5090/)Fits
* [Qwen3.8-27B on an RTX 5090](https://whatcanirunai.vercel.app/can-i-run/qwen3.8-27b/rtx-5090/)Fits
* [Gemma 3 27B on an RTX 5090](https://whatcanirunai.vercel.app/can-i-run/gemma-3-27b-it/rtx-5090/)Fits
* [Gemma 4 31B on an RTX 5090](https://whatcanirunai.vercel.app/can-i-run/gemma-4-31b-it/rtx-5090/)Fits
* [gpt-oss-20b on an RTX 5090](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-20b/rtx-5090/)Fits

## Quick answers

Can I run DeepSeek-V3.2 on an RTX 5090?

No. DeepSeek-V3.2 at Q4\_K\_M needs about 478.8 GB, and the GeForce RTX 5090 has 32.0 GB usable.

How much VRAM does DeepSeek-V3.2 need?

About 478.8 GB at Q4\_K\_M, 838.4 GB at Q8\_0 and 1577.3 GB at FP16, estimated for a \~4k context.

How fast is DeepSeek-V3.2 on an RTX 5090?

We do not estimate tokens/sec when the model does not fit. CPU offload is possible but slow.

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