Qwen2.5 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, Qwen2.5 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 Qwen2.5 7B needs
Qwen2.5 7B, from Alibaba's September 2024 release, is a strong general and multilingual 7B with unusually good maths and coding for its size.
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. Qwen2.5 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 leaves headroom on an 8 GiB card; Q8_0 at 8.86 GiB needs a 12 GiB card and is the quality pick; FP16 at 15.0 GiB needs a 16 GiB card.
Pitfalls
This model has only 4 KV heads, so its cache costs 56 KiB per token, less than half of Llama 3.1 8B's. The base config allows 131,072 tokens, but the instruct model ships configured for 32,768; Qwen documents YaRN rope scaling for longer inputs, which some runtimes apply statically and which can slightly hurt short prompts.
DeepSeek-R1-Distill-Qwen 7B has the identical shape; Llama 3.1 8B and Mistral 7B v0.3 are the usual comparisons.
FAQ
Questions, answered.
Tap a question to expand the answer.
Can Qwen2.5 7B run on a 6 GB GPU?
At Q4_K_M with 8,192 tokens the estimate is 5.92 GiB, just inside 6 GiB with little to spare. Q3_K_M at 5.20 GiB gives more headroom at some quality cost.
How much VRAM does Qwen2.5 7B need for 32k context?
7.23 GiB at Q4_K_M, of which 1.75 GiB is KV cache, so it still fits an 8 GiB card.
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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.