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Run LLM Locally

DeepSeek R1 Distill Llama 70B on the RTX 4080

DeepSeek R1 Distill Llama 70B at Q4_K_M needs about 45.1 GB, more than the 16 GB of the RTX 4080: part of the model runs from system RAM, which slows generation. At Q3_K_M it does not fit in VRAM.

DeepSeek R1 Distill Llama 70B

Q4_K_M · 8K context

Not recommended

The model needs about 45.1 GB but only 15.5 GB of VRAM is usable: 29.5 GB must be loaded into system RAM. It runs, 34% on the GPU, but more slowly.

Estimated speed is at most 1.3 tokens/s: too slow to be usable for chat.

Your hardware
RTX 4080 16 GB · 64 GB RAM
Estimated memory
~45.1 GB estimate
VRAM (usable memory)
15.5 GB
GPU only
No
CPU/GPU offload
Required (29.5 GB)
Generation speed
0.7–1.3 tok/s estimate
Largest context in fast memory
None
Confidence: low

Based on: memory bandwidth (717 GB/s) and model size · system RAM bandwidth (~51 GB/s)

Memory breakdown

~45.1 GB (43.9 GB–46.3 GB) needed · estimate

29.5 GB must be offloaded to system RAM

  • Model weights39.6 GB
  • KV cache2.5 GB
  • Runtime overhead0.8 GB
  • Safety margin2.1 GB
Assumptions: context 8K tokens · KV cache f16 · llama.cpp (CUDA) · flash attention on · weights = real file size

EstimateData updated 24 days agoSource: Run LLM Locally engine, Hugging Face config.json + model card

What makes this estimate less reliable

  • no benchmark for this hardware and model yet
  • split CPU/GPU runs vary a lot with the CPU and PCIe link
  • system RAM speed unknown (dual-channel DDR4-3200 assumed)

Other quantizations on the RTX 4080

DeepSeek R1 Distill Llama 70B on RTX 4080 + 64 GB RAM · 8K context
DeepSeek R1 Distill Llama 70B Q8_08K~76.8 GB · No dataIncompatible
DeepSeek R1 Distill Llama 70B Q6_K8K~60.1 GB · 0.4–0.9 tok/s estimateNot recommended
DeepSeek R1 Distill Llama 70B Q5_K_M8K~52.3 GB · 0.5–1 tok/s estimateNot recommended
DeepSeek R1 Distill Llama 70B Q4_K_M8K~45.1 GB · 0.7–1.3 tok/s estimateNot recommended
DeepSeek R1 Distill Llama 70B IQ4_XS8K~40.6 GB · 0.8–1.5 tok/s estimateNot recommended
DeepSeek R1 Distill Llama 70B Q3_K_M8K~37 GB · 0.9–1.8 tok/s estimateNot recommended

Alternatives

Hardware that runs DeepSeek R1 Distill Llama 70B Q4_K_M entirely in memory

Prices and links follow the market you choose.

RTX 6000 Ada Generation

NVIDIA

CompatibleRuns DeepSeek R1 Distill Llama 70B Q4_K_M entirely in memory

Price not available

Availability unknown

View on amazon.com

Affiliate link

Radeon PRO W7900

AMD

CompatibleRuns DeepSeek R1 Distill Llama 70B Q4_K_M entirely in memory

Price not available

Availability unknown

View on amazon.com

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AMD Ryzen AI Max+ 395

Unified memory PC

CompatibleRuns DeepSeek R1 Distill Llama 70B Q4_K_M entirely in memory

Price not available

Availability unknown

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No current offer imported for this market: the link opens a marketplace search.

Frequently asked questions

Can the RTX 4080 run DeepSeek R1 Distill Llama 70B?
The model needs about 45.1 GB but only 15.5 GB of VRAM is usable: 29.5 GB must be loaded into system RAM. It runs, 34% on the GPU, but more slowly. Estimated speed is at most 1.3 tokens/s: too slow to be usable for chat.
Which quantization should I use on the RTX 4080?
None fits entirely in VRAM at 8K context; the smallest, Q3_K_M, still needs offload.
What is the largest context on the RTX 4080?
At Q4_K_M, the model does not fit entirely in VRAM even at a short context.
How fast is DeepSeek R1 Distill Llama 70B (RTX 4080)?
We estimate 0.7–1.3 tok/s (estimate, confidence low). Based on: memory bandwidth (717 GB/s) and model size, system RAM bandwidth (~51 GB/s).