Qwen 3 14B
By Alibaba · China
Updated 2026-07-13
Overview
A 14B dense model from Alibaba that matches Qwen 2.5 32B Base on STEM and code, with the same hybrid thinking system as the rest of the Qwen 3 family. The pragmatic sweet spot for a single 24GB GPU.
When to pick this model
- You have a single 24GB GPU and want the strongest dense Qwen 3 that fits
- You need solid STEM and coding performance without jumping to a 32B
- You want a toggleable thinking mode for harder problems
- You need 131K context for long documents or codebases
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 9 GB |
| Q5_K_M | 11 GB |
| Q8_0 | 16 GB |
| FP16 (no quantization) | 28 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
In practice, Qwen 3 14B needs a 12 GB card at Q4_K_M (9 GB). Stepping up to Q8_0 nearly doubles the footprint to 16 GB, and unquantized FP16 weights take 28 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Qwen 3 14B needs roughly 16 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 6 tokens/sec on entry-level GPUs, on the order of 20 tokens/sec on a mid-range card, and up to 55 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Qwen 3 14B to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for Qwen 3 14B |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Does not fit — needs 9 GB at Q4_K_M |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Q5_K_M (11 GB used) |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Q8_0 (16 GB used) |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | Q8_0 (16 GB used) |
| 32 GB | RTX 5090 | FP16 (28 GB used) |
Which GPU should you buy to run Qwen 3 14B?
To run Qwen 3 14B locally at Q4, you need ~9 GB of VRAM. The best value for this is a RTX 5070 (12 GB VRAM).
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Published benchmark scores
| Benchmark | Score |
|---|---|
| MMLU (base) | 81.05 |
| SuperGPQA | 34.27 |
Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.
Strengths
- Matches Qwen 2.5 32B Base on STEM and code at less than half the size
- Hybrid thinking mode for harder reasoning passes
- 131K context window
- Apache 2.0
Limitations
- Still trails dedicated reasoners like QwQ-32B on AIME-class problems
- Thinking mode output can balloon for simple prompts
Typical workloads
In our catalog grid, Qwen 3 14B is filed under Reasoning, Advanced Chat, Code — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: multi-step reasoning and math-flavoured tasks; multilingual workloads.
The 128k-token context window is large enough to hold entire codebases' worth of files or long reports in a single prompt, which is what makes local RAG and document analysis practical. The Apache 2.0 license is permissive, so shipping it inside a commercial product raises no special legal questions.
Architecture & training
Architecture: Dense · GQA · hybrid thinking
Training: 36T token corpus.
The smartest dense 14B you can run locally — ideal for a single high-end consumer GPU.
Quick start
ollama run qwen3:14bOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does Qwen 3 14B need?
At the recommended Q4_K_M quantization, Qwen 3 14B needs about 9 GB of VRAM. Q8_0 takes 16 GB, and unquantized FP16 weights take 28 GB.
Can Qwen 3 14B run without a GPU?
Yes — with roughly 16 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does Qwen 3 14B support?
Qwen 3 14B supports a 128k-token context window (131,072 tokens).
Can I use Qwen 3 14B commercially?
Yes. Qwen 3 14B is released under Apache 2.0, a permissive open-source license that allows commercial use, modification and redistribution.
How fast is Qwen 3 14B on consumer hardware?
Our compatibility engine estimates on the order of 20 tokens/sec on a mid-range GPU and up to 55 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Qwen 3 14B should I download first?
Start with Q4_K_M (9 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. On a 24 GB card you can run up to Q8_0.