# Can I run R1 Distill 70B on an RTX 4090? | What Can I Run AI > No. R1 Distill 70B needs about 50.2 GB at Q4_K_M, and RTX 4090 has 24.0 GB usable. What to run instead. Source: https://whatcanirunai.vercel.app/can-i-run/deepseek-r1-distill-llama-70b/rtx-4090/ [Home](https://whatcanirunai.vercel.app/) / [Can I run it?](https://whatcanirunai.vercel.app/can-i-run/) / [R1 Distill 70B](https://whatcanirunai.vercel.app/models/deepseek-r1-distill-llama-70b/) / R1 Distill 70B on an RTX 4090 **No.** R1 Distill 70B at Q4\_K\_M needs about 50.2 GB, and the GeForce RTX 4090 has 24.0 GB usable at a \~4k context. # Can I run R1 Distill 70B on an RTX 4090? R1 Distill 70B at Q4\_K\_M needs about 50.2 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. A 70B DeepSeek-R1 distill on Llama. Memory need matches Llama 3.3 70B; it is not the full 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 50.2 GB Memory needed 50.2 GB at Q4\_K\_M, \~4k context Usable VRAM 24.0 GB of 24 GB VRAM Est. tokens/sec n/a does not fit [DeepSeek-R1-Distill-Llama-70B](https://whatcanirunai.vercel.app/models/deepseek-r1-distill-llama-70b/) · [GeForce RTX 4090](https://whatcanirunai.vercel.app/hardware/rtx-4090/) · [Everything this machine runs](https://whatcanirunai.vercel.app/what-can-i-run/rtx-4090/) · [Q4 vs Q8 vs FP16](https://whatcanirunai.vercel.app/quantization/) ## Memory at each quant | Quant | Need vs 24.0 GB | Verdict | | -------- | --------------- | ------------- | | Q4\_K\_M | 50.2 GB | Q4Won’t fit | | Q8\_0 | 87.2 GB | Q8Won’t fit | | FP16 | 163.3 GB | FP16Won’t fit | ## Estimated tokens per second No speed estimate: R1 Distill 70B does not fit in the 24.0 GB this machine can use, so we do not publish a tokens/sec figure for CPU offload.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 Even at a short \~4k context, the weights do not fit. Extending context would only increase the gap. __Context headroom for R1 Distill 70B on an RTX 4090 at Q4\_K\_M__ | Context | KV cache (FP16) | Total at Q4\_K\_M | Verdict | | ------- | --------------- | ----------------- | --------- | | 4k | 1.3 GB | 50.2 GB | Won’t fit | | 8k | 2.7 GB | 51.5 GB | Won’t fit | | 16k | 5.4 GB | 54.2 GB | Won’t fit | | 32k | 10.7 GB | 59.6 GB | Won’t fit | | 64k | 21.5 GB | 70.3 GB | Won’t fit | | 128k | 42.9 GB | 91.8 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 [R1 Distill 32B](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1-distill-qwen-32b/rtx-4090/) instead (23.8 GB at Q4). It is a different model, not a drop-in, but it does fit this machine. * To hold R1 Distill 70B at Q4, you need about 50.2 GB. The [MacBook Pro M5 Max 128GB](https://whatcanirunai.vercel.app/hardware/macbook-pro-m5-max-128/) has 96.0 GB usable ($6699 Apple list price, 14-inch M5 Max 18C CPU/40C GPU, 128GB/2TB). * Two used RTX 3090s give 48 GB combined. Our Q4 estimate for this 70B is just over that, so we don’t mark the pair as a fit; see the [dual 3090 build](https://whatcanirunai.vercel.app/builds/dual-used-rtx-3090-70b/) and [multi-GPU notes](https://whatcanirunai.vercel.app/multi-gpu-for-local-llm/). * 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-Llama-70B model page](https://whatcanirunai.vercel.app/models/deepseek-r1-distill-llama-70b/). ## Other hardware for R1 Distill 70B * [R1 Distill 70B on an RTX 5090](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1-distill-llama-70b/rtx-5090/)Won’t fit * [R1 Distill 70B on dual RTX 3090s](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1-distill-llama-70b/dual-rtx-3090/)Won’t fit * [R1 Distill 70B on a Mac mini 64GB](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1-distill-llama-70b/mac-mini-m5-pro-64/)Won’t fit * [R1 Distill 70B on a Mac Studio 256GB](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1-distill-llama-70b/mac-studio-m5-ultra-256/)Fits * [R1 Distill 70B on an RTX 3090](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1-distill-llama-70b/rtx-3090/)Won’t fit ## Other models on an RTX 4090 * [Qwen3-32B on an RTX 4090](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/rtx-4090/)Tight * [Qwen3-30B-A3B on an RTX 4090](https://whatcanirunai.vercel.app/can-i-run/qwen3-30b-a3b/rtx-4090/)Tight * [Qwen3.8-27B on an RTX 4090](https://whatcanirunai.vercel.app/can-i-run/qwen3.8-27b/rtx-4090/)Fits * [Gemma 3 27B on an RTX 4090](https://whatcanirunai.vercel.app/can-i-run/gemma-3-27b-it/rtx-4090/)Fits * [Gemma 4 31B on an RTX 4090](https://whatcanirunai.vercel.app/can-i-run/gemma-4-31b-it/rtx-4090/)Tight * [gpt-oss-20b on an RTX 4090](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-20b/rtx-4090/)Fits ## Quick answers Can I run R1 Distill 70B on an RTX 4090? No. R1 Distill 70B at Q4\_K\_M needs about 50.2 GB, and the GeForce RTX 4090 has 24.0 GB usable. How much VRAM does R1 Distill 70B need? About 50.2 GB at Q4\_K\_M, 87.2 GB at Q8\_0 and 163.3 GB at FP16, estimated for a \~4k context. How fast is R1 Distill 70B on an RTX 4090? 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/