Rent a GPU

Can the NVIDIA RTX 4090 run DeepSeek R1 Distill Llama 70B?

RTX 4090 × DeepSeek R1 Distill Llama 70B8K context
NoDoes not fit in memory
Memory at Q4_K_M46.1 GB of 24 GB
Weights 42.8 GBKV cache 2.7 GBRuntime 0.6 GB
On the GPU48%of the Q4_K_M weights
In system RAM22.1 GBthe rest spills over
Offload speed~1.5tokens per second
  1. FP16
  2. Q8_0
  3. Q6_K
  4. Q5_K_M
  5. Q4_K_M
  6. Q3_K_M
  7. Q2_K

No. Even at Q4_K_M, DeepSeek R1 Distill Llama 70B needs about 46.1 GB, but the NVIDIA RTX 4090 has 24 GB usable. You could offload some layers to system RAM, but it will be very slow.

Try other context lengths

Run it on a rented GPU

On a cloud GPU with enough memory, the same command you'd use locally works:

ollama run deepseek-r1:70b

At Q4_K_M it needs about 46.1 GB, so pick a GPU with at least 48 GB.

New to this? Step-by-step setup for the RTX 4090

Run it in the cloud

Too big for the NVIDIA RTX 4090? Run DeepSeek R1 Distill Llama 70B on a rented GPU instead.

Rent a cloud GPU by the hour. No upfront cost, stop any time. Not sure whether to rent or buy? Compare the cost.

Rent a GPU on RunPodRent a GPU on Vast.ai

Memory needed by quantization

At 8,192 tokens of context. The black line marks the 24 GB the NVIDIA RTX 4090 can use.

QuantQualityMemoryFits?Speed
FP16Full quality144.5 GBToo big—
Q8_0Near-lossless78.3 GBToo big—
Q6_KExcellent61.2 GBToo big—
Q5_K_MVery good53.5 GBToo big—
Q4_K_MGood, most popular46.1 GBToo big—
Q3_K_MNoticeably degraded37.8 GBToo big—
Q2_KHeavily degraded29.8 GBToo big—

Measured speed from real RTX 4090 owners

Tokens per second that people measured on their own NVIDIA RTX 4090, next to our estimate.

No one has measured DeepSeek R1 Distill Llama 70B on the NVIDIA RTX 4090 yet. The speeds above are estimates. Run the benchmark and yours becomes the first real number on this page.

Have an RTX 4090? Add your real speed

One command times DeepSeek R1 Distill Llama 70B in your own Ollama, shows you exactly what it would send, and asks before sending anything. No sign-up, no personal data.

macOS and Linux

curl -fsSL https://gpufit.dev/bench.sh | sh -s -- deepseek-r1:70b

Windows (PowerShell)

$env:GPUFIT_MODEL="deepseek-r1:70b"; irm https://gpufit.dev/bench.ps1 | iex

How the benchmark works

GPUs that run DeepSeek R1 Distill Llama 70B comfortably

Check Mac Studio M4 Max (64 GB) prices

Models that run well on the NVIDIA RTX 4090

Frequently asked questions

Can NVIDIA RTX 4090 run DeepSeek R1 Distill Llama 70B?

No. Even at Q4_K_M, DeepSeek R1 Distill Llama 70B needs about 46.1 GB, but the NVIDIA RTX 4090 has 24 GB usable. You could offload some layers to system RAM, but it will be very slow.

How much VRAM does DeepSeek R1 Distill Llama 70B need?

About 46.1 GB at Q4_K_M with 8,192 tokens of context, and about 144.5 GB at full FP16 precision.

How fast is DeepSeek R1 Distill Llama 70B on the NVIDIA RTX 4090?

It does not fit in the NVIDIA RTX 4090's memory, so most layers would run from system RAM and generation would be very slow.