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Run LLM Locally

DeepSeek R1 Distill Qwen 14B on the RTX 4060 Ti 8 GB

DeepSeek R1 Distill Qwen 14B at Q4_K_M needs about 11.2 GB, more than the 8 GB of the RTX 4060 Ti 8 GB: part of the model runs from system RAM, which slows generation. At Q3_K_M it does not fit in VRAM.

DeepSeek R1 Distill Qwen 14B

Q4_K_M · 8K context

CPU/GPU offload

The model needs about 11.2 GB but only 7.7 GB of VRAM is usable: 3.6 GB must be loaded into system RAM. It runs, 68% on the GPU, but more slowly.

Your hardware
RTX 4060 Ti 8 GB · 64 GB RAM
Estimated memory
~11.2 GB estimate
VRAM (usable memory)
7.7 GB
GPU only
No
CPU/GPU offload
Required (3.6 GB)
Generation speed
5–9.5 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

~11.2 GB (10.7 GB–11.8 GB) needed · estimate

3.6 GB must be offloaded to system RAM

  • Model weights8.4 GB
  • KV cache1.5 GB
  • Runtime overhead0.8 GB
  • Safety margin0.5 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 8 GB

DeepSeek R1 Distill Qwen 14B on RTX 4060 Ti 8 GB + 64 GB RAM · 8K context
DeepSeek R1 Distill Qwen 14B Q8_08K~17.8 GB · 1.9–3.5 tok/s estimatePossible but slow
DeepSeek R1 Distill Qwen 14B Q6_K8K~14.3 GB · 3–5.5 tok/s estimateCPU/GPU offload
DeepSeek R1 Distill Qwen 14B Q5_K_M8K~12.7 GB · 3.5–7 tok/s estimateCPU/GPU offload
DeepSeek R1 Distill Qwen 14B Q4_K_M8K~11.2 GB · 5–9.5 tok/s estimateCPU/GPU offload
DeepSeek R1 Distill Qwen 14B IQ4_XS8K~10.4 GB · 6.5–12 tok/s estimateCPU/GPU offload
DeepSeek R1 Distill Qwen 14B Q3_K_M8K~9.6 GB · 8–15 tok/s estimateCPU/GPU offload

Alternatives

Hardware that runs DeepSeek R1 Distill Qwen 14B Q4_K_M entirely in memory

Prices and links follow the market you choose.

GeForce RTX 4060 Ti 16 GB

NVIDIA

ExcellentRuns DeepSeek R1 Distill Qwen 14B Q4_K_M entirely in memory

Price not available

Availability unknown

View on amazon.com

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Radeon RX 7600 XT

AMD

ExcellentRuns DeepSeek R1 Distill Qwen 14B Q4_K_M entirely in memory

Price not available

Availability unknown

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MacBook Pro 14 M3 Pro

Apple Silicon

CompatibleRuns DeepSeek R1 Distill Qwen 14B Q4_K_M entirely in memory

Price not available

Availability unknown

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AMD Ryzen AI Max+ 395

Unified memory PC

ExcellentRuns DeepSeek R1 Distill Qwen 14B 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 8 GB run DeepSeek R1 Distill Qwen 14B?
The model needs about 11.2 GB but only 7.7 GB of VRAM is usable: 3.6 GB must be loaded into system RAM. It runs, 68% on the GPU, but more slowly.
Which quantization should I use on the RTX 4060 Ti 8 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 8 GB?
At Q4_K_M, the model does not fit entirely in VRAM even at a short context.
How fast is DeepSeek R1 Distill Qwen 14B (RTX 4060 Ti 8 GB)?
We estimate 5–9.5 tok/s (estimate, confidence low). Based on: memory bandwidth (288 GB/s) and model size, system RAM bandwidth (~51 GB/s).