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Run LLM Locally

DeepSeek R1 Distill Llama 70B on the RTX 4060 Ti 16 GB

DeepSeek R1 Distill Llama 70B at Q4_K_M needs about 45.1 GB, more than the 16 GB of the RTX 4060 Ti 16 GB: part of the model runs from system RAM, which slows generation. At Q3_K_M it does not fit in VRAM.

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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 1.2 tokens/s: too slow to be usable for chat.

Your hardware
RTX 4060 Ti 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
0.6–1.2 tok/s estimate
Largest context in fast memory
None
Confidence: low

Based on: memory bandwidth (288 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 4060 Ti 16 GB

DeepSeek R1 Distill Llama 70B on RTX 4060 Ti 16 GB + 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 · 0.4–0.8 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 · 0.6–1.2 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 · 0.9–1.7 tok/s estimateNot recommended

Alternatives

GPUs that run DeepSeek R1 Distill Llama 70B Q4_K_M entirely in VRAM

Other models that fit the RTX 4060 Ti 16 GB

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

View on amazon.com

Affiliate link

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

Frequently asked questions

Can the RTX 4060 Ti 16 GB 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 1.2 tokens/s: too slow to be usable for chat.
Which quantization should I use on the RTX 4060 Ti 16 GB?
None fits entirely in VRAM at 8K context; the smallest, Q3_K_M, still needs offload.
What is the largest context on the RTX 4060 Ti 16 GB?
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 4060 Ti 16 GB)?
We estimate 0.6–1.2 tok/s (estimate, confidence low). Based on: memory bandwidth (288 GB/s) and model size, system RAM bandwidth (~51 GB/s).