Yes — with 4.0 GB to spare

DeepSeek-R1-Distill-Qwen 14B at Q4_K_M fits your GeForce RTX 5060 Ti 16 GB entirely on the GPU at 8K context, at an estimated 33 tokens per second. Past 29K the KV cache pushes it over — quantise the cache to q8_0, or step down a quantisation, to go longer.

Fully on GPU 8K context Q4_K_M · 8.3 GB MIT Released Jan 2025

The 2025 reasoning-per-gigabyte pick for a 12 GB card. Qwen3.5 9B in thinking mode has since overtaken it.

What hardware do I need for DeepSeek-R1-Distill-Qwen 14B? →

The VRAM budget

weights 8.3 GB

Weights 8.3 GB KV cache @ 8K 1.50 GB Runtime overhead 0.6 GB Free 4.0 GB of 14.4 GB

Quantisation ladder

Quant	Weights	Total @ 8K	Max context	Tok/s	Quality	Fit
F16	27.6 GB	29.7 GB	—	~2.1	Reference	15.3 GB over
Q8_0	14.6 GB	16.7 GB	—	~9.1	−0.1% ppl	2.3 GB over
Q6_K	11.3 GB	13.4 GB	13K	24	−0.4% ppl	Fits
Q5_K_M	9.8 GB	11.9 GB	21K	28	−0.8% ppl	Long context
Q4_K_M	8.3 GB	10.4 GB	29K	33	−1.9% ppl	Recommended
Q3_K_M	6.7 GB	8.8 GB	37K	40	−5.4% ppl	Long context
Q2_K	5.8 GB	7.9 GB	42K	47	−15% ppl	Long context

Quality is the published perplexity delta against f16 weights. Max context assumes an f16 KV cache; q8_0 roughly doubles it.

How to run it

Ollama llama.cpp LM Studio vLLM

terminal

$ ollama pull deepseek-r1:14b
$ OLLAMA_CONTEXT_LENGTH=8192 \
    ollama run deepseek-r1:14b

The default. One binary, a model registry, an OpenAI-compatible port. More on Ollama.

01Download is 8.3 GB. Keep it on an SSD — a first load off a spinning disk takes minutes.

02Close anything else holding VRAM. A browser with hardware acceleration can sit on 1–2 GB.

03There is room to go to 29K context on this card.

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