HomeCan I run it?DeepSeek-V3.2DeepSeek-V3.2 on an RTX 4090
No. DeepSeek-V3.2 at Q4_K_M needs about 478.8 GB, and the GeForce RTX 4090 has 24.0 GB usable at a ~4k context.
Can I run DeepSeek-V3.2 on an RTX 4090?
DeepSeek-V3.2 at Q4_K_M needs about 478.8 GB, and the GeForce RTX 4090 has 24.0 GB usable. That figure is 24 GB of VRAM on the spec sheet, of which 24.0 GB is usable for weights and cache, at 1008 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, 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
- 24.0 GB of 24 GB VRAM
- Est. tokens/sec
- n/a does not fit
Memory at each quant
| Quant | Need vs 24.0 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No speed estimate: DeepSeek-V3.2 does not fit in the 24.0 GB this machine can use, so we do not publish a tokens/sec figure for CPU offload.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 to explore longer chats.
What to do instead
- Run R1 Distill 32B 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 We do not publish an offload tokens/sec figure.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. Specs for this machine: GeForce RTX 4090.
Other hardware for DeepSeek-V3.2
- DeepSeek-V3.2 on a Mac Studio 512GBWon’t fit
- DeepSeek-V3.2 on a Mac Studio 256GBWon’t fit
- DeepSeek-V3.2 on an RTX 5090Won’t fit
Other models on an RTX 4090
- Qwen3-32B on an RTX 4090Tight
- Qwen3-30B-A3B on an RTX 4090Tight
- Qwen3.8-27B on an RTX 4090Fits
- Gemma 3 27B on an RTX 4090Fits
- Gemma 4 31B on an RTX 4090Tight
- gpt-oss-20b on an RTX 4090Fits
Quick answers
- Can I run DeepSeek-V3.2 on an RTX 4090?
- No. DeepSeek-V3.2 at Q4_K_M needs about 478.8 GB, and the GeForce RTX 4090 has 24.0 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 4090?
- We do not estimate tokens/sec when the model does not fit. CPU offload is possible but slow.