DeepSeek-R1-Distill-Qwen 14B VRAM: what it needs at Q4_K_M, Q8_0 and FP16
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KV cache is counted at FP16. Overhead is a fixed 1.5 GiB estimate for the runtime and compute buffers. A custom size borrows the architecture of the nearest preset.
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GPU tiers
- 8 GiBtoo smallRTX 4060, RTX 3070, RX 7600
- 12 GiBfitsRTX 4070, RTX 3060 12GB, RTX 5070
- 16 GiBfitsRTX 4060 Ti 16GB, RTX 4080, RTX 5080
- 24 GiBfitsRTX 4090, RTX 3090, RX 7900 XTX
- 32 GiBfitsRTX 5090, V100 32GB
- 48 GiBfitsRTX A6000, RTX 6000 Ada, L40S
- 80 GiBfitsA100 80GB, H100 80GB
At Q4_K_M with an 8,192-token context, DeepSeek-R1-Distill-Qwen 14B needs about 11.0 GiB of VRAM: 7.97 GiB of weights, 1.50 GiB of KV cache and 1.50 GiB of runtime overhead. The smallest common GPU tier that holds it is 12 GiB (RTX 4070, RTX 3060 12GB, RTX 5070).
How much VRAM DeepSeek-R1-Distill-Qwen 14B needs
DeepSeek-R1-Distill-Qwen 14B, from January 2025, is fine-tuned from Qwen2.5-14B and is often called the sweet spot of the distills for a single mid-range GPU. The R1 distills are not DeepSeek-R1 itself, which is a 671B mixture-of-experts model; each one is an existing Qwen2.5 or Llama base fine-tuned on reasoning traces generated by R1, so its memory footprint is exactly that of its base architecture.
With 8,192 tokens of context the estimate is 29.1 GiB at FP16, 16.9 GiB at Q8_0 and 11.0 GiB at Q4_K_M. FP16 needs a 32 GiB card, Q8_0 needs a 24 GiB card, and Q4_K_M needs a 12 GiB card. The weights are 14 billion parameters times the bits per weight of each format (16, 8.5 and 4.89), divided by eight; everything is in GiB, the unit GPU memory is sold in.
Context length and the KV cache
The KV cache stores a key and a value vector for every token in every layer. DeepSeek-R1-Distill-Qwen 14B has 48 layers, 8 KV heads and a head dimension of 128, read from its Hugging Face config.json, so at FP16 each token costs 192 KiB. That is 1.50 GiB at 8,192 tokens, and 24.0 GiB at the maximum of 131,072, where the Q4_K_M total becomes 33.5 GiB and needs a 48 GiB card. The cache does not shrink with weight quantization; only a runtime option that quantizes the cache itself does.
Which quantization to pick
Q4_K_M at 11.0 GiB fits a 12 GiB card; Q5_K_M at 12.3 GiB wants 16 GiB, and Q8_0 at 16.9 GiB needs a 24 GiB card.
Pitfalls
Reasoning models write a long chain of thought before the answer, often thousands of tokens, so budget context generously: a reply that runs out of KV cache stops mid-thought. At 192 KiB per token, a 32,768-token budget at Q4_K_M needs 15.5 GiB, which rules out a 12 GiB card for long problems.
Qwen2.5 14B is the base architecture; DeepSeek-R1-Distill-Qwen 7B and 32B bracket it.
FAQ
Questions, answered.
Tap a question to expand the answer.
Can DeepSeek-R1 14B run on a 12 GB GPU?
Yes at Q4_K_M with 8,192 tokens: 11.0 GiB. Long reasoning traces are the limit: 16,384 tokens need 12.5 GiB, beyond 12 GiB.
How much VRAM does DeepSeek-R1 14B need at Q8_0?
16.9 GiB at 8,192 tokens, so a 24 GiB card.
What context should I give DeepSeek-R1 14B on a 16 GB card?
Q4_K_M with 32,768 tokens comes to 15.5 GiB, which fits 16 GiB and leaves room for long chains of thought.
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Estimates only; the KV cache is sized at FP16 and the overhead is a fixed allowance, so always confirm against your own runtime’s report before buying hardware. OpsCanopy is free and open.