Best GPU for Qwen 2.5 Hosting
Qwen 2.5 ships in 0.5B through 72B variants. The right GPU depends on which size you serve and at what precision. Most production deployments land on Qwen 2.5 14B at FP16, which fits comfortably on a 32 GB card.
The short answer: the RTX 5090 is the best GPU for self-hosting Qwen 2.5 (14B class) on a dedicated server. It has the right VRAM (32 GB) for the model, modern tensor cores, and the best cost-per-token in our catalogue for this workload.
Ranking — Best to Worst for This Workload
From best to worst for this specific workload, with the reason in plain English.
RTX 5090 Top Pick
32 GB fits Qwen 2.5 14B FP16 plus 32K context. Best cost-per-token in the catalogue.
32 GB · Blackwell · from £359/mo
RTX 6000 Pro 96 GB Best for 72B
96 GB runs Qwen 2.5 72B at FP8 single-card. The only single-GPU option for the flagship.
96 GB · Blackwell · from £1099/mo
RTX 3090 Budget Pick
24 GB fits Qwen 2.5 7B FP16 with comfortable context. The cheapest practical card.
24 GB · Ampere · from £179/mo
RTX 5080 Mid Tier
16 GB needs INT4/AWQ for 14B. Fine for 7B FP16.
16 GB · Blackwell · from £189/mo
RTX 4090 Older Flagship
24 GB Ada — strong but pushed out by Blackwell on cost-per-token.
24 GB · Ada Lovelace · from £279/mo
Background & Sizing
Qwen 2.5 is the strongest open-weight model family for general LLM work outside the Llama lineage. Alibaba ships seven sizes (0.5B, 1.5B, 3B, 7B, 14B, 32B, 72B) and a coding variant. For self-hosting, the right GPU comes down to which size you actually need.
Picking the right Qwen size
- Qwen 2.5 7B — fits a 24 GB GPU with room to spare. Use for most chatbots.
- Qwen 2.5 14B — needs 32 GB for FP16. The mainstream production size — competitive with Llama 3 8B+ tools.
- Qwen 2.5 32B — needs 64 GB for FP16 (multi-GPU or 6000 Pro), or 24 GB at INT4.
- Qwen 2.5 72B — needs 144 GB for FP16. Single-card only on the 6000 Pro at FP8.
For a deeper benchmark see our best GPU for LLM inference ranking and the model deployment notes in our self-host LLM guide.
Frequently Asked Questions
The questions buyers actually ask before committing to a GPU server.
Can I run Qwen 2.5 14B on an RTX 4090?
Yes at FP16, with tight context. 24 GB barely fits — drop to 8K context or use INT4 quantisation.
Does Qwen 2.5 support function calling?
Yes — native tool-use across all sizes 7B and up. Compatible with OpenAI’s tools API via vLLM.
Qwen 2.5 vs Llama 3.1 — which is better?
Workload-dependent. Qwen 2.5 14B is broadly stronger than Llama 3.1 8B. Llama 3.1 70B is broadly stronger than Qwen 2.5 32B at the cost of much more VRAM.
Can I run Qwen 2.5 72B on a single GPU?
Only on the RTX 6000 Pro 96 GB at FP8. Otherwise it needs multi-GPU cluster.
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