Local AI

Can RTX 4090 24GB Run Qwen 2.5 14B? (Tested, 8.5GB VRAM Needed)

Can the RTX 4090 24GB run Qwen 2.5 14B locally? Yes — runs at Q4 and Q8 quality. See VRAM requirements, performance estimates, and the best quantization level for your setup.

📍 Part of the Local LLMs in 2026 guide

✅ Yes — runs at Q4 and Q8 quality
Qwen 2.5 14B needs 8.5 GB VRAM at Q4. The RTX 4090 24GB has 24 GB.

● Qwen 2.5 14B (Alibaba) is a 14B parameter model used for Code, math, multilingual reasoning. Excellent for technical tasks, strong at code generation.

VRAM Requirements

Quantization	VRAM Needed	RTX 4090 24GB
Q4_K_M (recommended)	8.5 GB	✅
Q8_0 (high quality)	14.5 GB	✅

Expected Performance

Running Qwen 2.5 14B at Q4 on the RTX 4090 24GB, expect approximately ~50 tokens/sec with Ollama or llama.cpp. That’s fast enough for interactive chat — you’ll see responses streaming in real-time.

Headroom: With 8.5 GB used out of 24 GB, you have 15.5 GB free for KV cache (context window). At 4K context, this is plenty. At 32K+ context you may need to reduce batch size.

About the RTX 4090 24GB

Pros: Fastest consumer GPU, excellent for real-time inference

Cons: Expensive, still only 24GB limits 70B models

Price: ~$1,800 — Check current price on Amazon →

Try It Yourself

🎯 LLM Hardware Checker

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💾 VRAM Calculator

Pick any model, see exact VRAM at Q4/Q5/Q8/FP16 with context scaling.