HomeCan I run it?R1 Distill 70BR1 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, 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
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No speed estimate: R1 Distill 70B does not fit in the 24.0 GB this machine can use, so we do not publish a tokens/sec figure for CPU offload.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 | 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 |
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.
- To hold R1 Distill 70B at Q4, you need about 50.2 GB. The MacBook Pro M5 Max 128GB 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 and multi-GPU notes.
- 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 We do not publish an offload tokens/sec figure.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 and the DeepSeek-R1-Distill-Llama-70B model page.
Other hardware for R1 Distill 70B
- R1 Distill 70B on an RTX 5090Won’t fit
- R1 Distill 70B on dual RTX 3090sWon’t fit
- R1 Distill 70B on a Mac mini 64GBWon’t fit
- R1 Distill 70B on a Mac Studio 256GBFits
- R1 Distill 70B on an RTX 3090Won’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 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.