DeepSeek-R1-Distill-Qwen 7B 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 GiBfitsRTX 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 7B needs about 5.92 GiB of VRAM: 3.98 GiB of weights, 0.44 GiB of KV cache and 1.50 GiB of runtime overhead. The smallest common GPU tier that holds it is 8 GiB (RTX 4060, RTX 3070, RX 7600).
How much VRAM DeepSeek-R1-Distill-Qwen 7B needs
DeepSeek-R1-Distill-Qwen 7B was released in January 2025 alongside DeepSeek-R1, fine-tuned from Qwen2.5-Math-7B to reason step by step, and is strongest at maths. 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 15.0 GiB at FP16, 8.86 GiB at Q8_0 and 5.92 GiB at Q4_K_M. FP16 needs a 16 GiB card, Q8_0 needs a 12 GiB card, and Q4_K_M needs an 8 GiB card. The weights are 7 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 7B has 28 layers, 4 KV heads and a head dimension of 128, read from its Hugging Face config.json, so at FP16 each token costs 56 KiB. That is 0.44 GiB at 8,192 tokens, and 7.00 GiB at the maximum of 131,072, where the Q4_K_M total becomes 12.5 GiB and needs a 16 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 5.92 GiB fits an 8 GiB card; Q8_0 at 8.86 GiB needs a 12 GiB card. Because answers depend on long, exact reasoning chains, Q5_K_M or better is worth it when memory allows.
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. With only 4 KV heads this model's cache is cheap, 56 KiB per token, so 32,768 tokens at Q4_K_M still come to 7.23 GiB. DeepSeek recommends a temperature around 0.6 and no system prompt.
Qwen2.5 7B is the same architecture; DeepSeek-R1-Distill-Qwen 14B and DeepSeek-R1-Distill-Llama 8B are the neighbouring distills.
FAQ
Questions, answered.
Tap a question to expand the answer.
Can DeepSeek-R1 7B run on an 8 GB GPU?
Yes. Q4_K_M needs 5.92 GiB at 8,192 tokens and 7.23 GiB at 32,768 tokens, enough for long reasoning traces. Q8_0 at 8.86 GiB needs a 12 GiB card.
Is DeepSeek-R1 7B the same as DeepSeek-R1?
No. It is Qwen2.5-Math-7B fine-tuned on reasoning data generated by DeepSeek-R1. The full R1 is a 671B mixture-of-experts model that needs hundreds of GiB even quantized.
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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.