Qwen2.5 0.5B Instruct: how much VRAM does it need?

Qwen2.5 0.5B Instruct has 494.0M parameters in 24 layers. It uses grouped-query attention: 14 query heads share 2 key-value heads, so its KV cache is 7× smaller than full multi-head attention would need. It takes up to 32,768 tokens of context; one user at that length needs 384 MiB of KV cache at FP16 on top of the weights.

Qwen2.5 0.5B Instruct memory by precision and context

One user, FP16 KV cache and vLLM's overhead on one GPU at its defaults for a card under 70 GB. Quantizing shrinks the weights, not the KV cache, so the saving narrows as the context grows.

Which single GPUs hold it

• BF16 (1.85 GiB): RTX 3060, NVIDIA T4, RTX 4060 Ti 16GB, RTX 4070 Ti Super
• FP8 (1.52 GiB): RTX 3060, NVIDIA T4, RTX 4060 Ti 16GB, RTX 4070 Ti Super
• INT4 (1.36 GiB): RTX 3060, NVIDIA T4, RTX 4060 Ti 16GB, RTX 4070 Ti Super

At 8k context for one user, the smallest cards first. A discrete card is budgeted at vLLM's default 92% of its memory, a unified-memory device at the share its GPU can address, and each carries vLLM's overhead at its own defaults.

How many users fit

Users at 8k context each, on one card. ✗: the weights leave no room for even one.

Fine-tuning Qwen2.5 0.5B Instruct

Sequence length 2,048, batch size 1, gradient checkpointing on, Adam. Full fine-tuning keeps 16 bytes per parameter before activations; QLoRA stores the frozen base in 4-bit NF4.

Where the numbers come from

Weights are parameters × bytes per parameter. The KV cache is 2 × layers × key-value heads × head dimension × bytes × context × users. For Qwen2.5 0.5B Instruct that is 12.0 KiB per token at FP16. These are capacity-planning figures: the serving engine — vLLM, TGI, llama.cpp — and its paged-attention efficiency still move the real number.

More on KV cache size and on GQA, MQA and MLA.

Work with Qwen2.5 0.5B Instruct

• Size it for your workload
• Fine-tuning memory
• vLLM parameters
• Model anatomy
• Its config.json, explained
• What its files say about serving it

Other Qwen models

• Qwen2.5 Coder 7B
• Qwen2.5 7B Instruct
• Qwen3 8B
• Qwen2.5 14B Instruct
• Qwen3 30B A3B
• Qwen3 Coder 30B A3B
• Qwen2.5 32B Instruct
• Qwen3 32B
• Qwen2.5 72B Instruct