Qwen2.5 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, Qwen2.5 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 Qwen2.5 14B needs
Qwen2.5 14B sits in the gap between 7B-class models and 32B ones, and is a common pick for a 12 or 16 GiB card that should do more than chat.
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. Qwen2.5 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 is a good middle step on 16 GiB, and Q8_0 at 16.9 GiB needs a 24 GiB card.
Pitfalls
With 48 layers its cache costs 192 KiB per token, so context eats a 12 GiB card fast: 16,384 tokens at Q4_K_M already need 12.5 GiB. As with all Qwen2.5 instruct models, contexts past 32,768 tokens rely on YaRN rope scaling.
DeepSeek-R1-Distill-Qwen 14B is this architecture with reasoning training; Mistral Nemo 12B is a smaller alternative.
FAQ
Questions, answered.
Tap a question to expand the answer.
Can Qwen2.5 14B run on a 12 GB GPU?
Yes at Q4_K_M with up to 8,192 tokens: 11.0 GiB. At 16,384 tokens it needs 12.5 GiB, which calls for a 16 GiB card.
What is the best quantization for Qwen2.5 14B on a 16 GB card?
Q5_K_M at 12.3 GiB with 8,192 tokens, or Q4_K_M if you want 32,768 tokens of context (15.5 GiB). Q8_0 at 16.9 GiB does not fit 16 GiB.
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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.