Qwen3 14B
Q4 GGUF download: Qwen/Qwen3-14B-GGUF - 9 GB on disk, read 2026-09-27. VRAM figures above are estimates; this is the actual file size.
Intelligence benchmarks
Artificial Analysis indexes - compared with the best open and proprietary models
Intelligence
6.7
AA Index
Intelligence Index - Qwen3 14B vs. the field
Best open-weight models (you can run locally) and leading proprietary models for context.
Benchmark data from Artificial Analysis · updated 2026-10-19.
Hosted API cost & speed
What it costs to rent this model instead of running it locally
| Metric | Value |
|---|---|
| Input (per 1M tokens) | $0.12 |
| Output (per 1M tokens) | $0.24 |
| Median speed (hosted API) | 66 tok/s |
Median across hosted providers, measured by Artificial Analysis - updated 2026-10-19. Local llama.cpp speed depends on your hardware — see the hardware pages for estimates.
Standard benchmarks
Performance across standard evaluations
| Benchmark | Score |
|---|---|
| MMLU | 77 |
| HumanEval | 80.5 |
| MT-Bench | 8.5 |
| GSM8K | 88 |
Other scores are carried over from the site's initial dataset - no source on record, not re-verified.
Will it run on your hardware?
Pick your GPU memory - see which quantizations fit, and the cheapest card for the rest
Need an exact figure for your context length? Use the VRAM calculator.
Run it locally
Copy-paste - running in under a minute
ollama run qwen3:14bvllm serve Qwen/Qwen3-14BNew to this? Start with Ollama · serve to many users with vLLM.
Deep dive
Notes, sources, and the full write-up
Qwen3 14B
Qwen3 14B is the best all-rounder in Qwen3's lineup. At 14.8 billion parameters with thinking mode and Apache 2.0 license, it delivers GPT-3.5-class performance in a package that fits on a 12GB GPU.
Why it's the sweet spot
- Fits on 12GB GPUs - 8 GB at Q4_K_M means RTX 3060 12GB owners can run it
- 77 MMLU - competitive with much larger models
- Thinking mode - optional chain-of-thought for complex problems
- 1.7M+ monthly downloads - proven popularity
- Apache 2.0 - commercial use with no strings attached
VRAM math
| Quant | VRAM | Recommended Hardware |
|---|---|---|
| Q4_K_M | ~8 GB | RTX 3060 12GB, RTX 3090 |
| Q5_K_M | ~10 GB | RTX 3090, RTX 4090 |
| Q8_0 | ~16 GB | RTX 4090 |
| FP16 | ~28 GB | RTX 5090, dual 3090 |
How to run
ollama run qwen3:14bFrom the community
What users are saying
“Not a problem with the model — a serving-layer issue that had me attributing weeks of results to the model itself, so posting in case anyone else is in the same position. On ollama 0.32.9, ollama show qwen3:14b reports a context length of 40960.”
Frequently asked
Quick answers to common questions
How much VRAM does Qwen3 14B need?
Qwen3 14B with 14B parameters needs approximately 8 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.
Is Qwen3 14B better than other Qwen models?
Qwen3 14B scores 77 on MMLU and 80.5 on HumanEval. It has 14B parameters with 32,768 context - a strong choice for general-purpose, coding, agents.
What license is Qwen3 14B under?
Qwen3 14B is released under the Apache 2.0 license, making it suitable for most commercial and personal projects.
What hardware runs Qwen3 14B well?
With 14B parameters, Qwen3 14B requires adequate VRAM. High-end GPUs like the RTX 4090 (24GB), RTX 5090 (32GB), or Mac Studio with unified memory are good options. Check our hardware directory for specific recommendations.
What is the best quantization for Qwen3 14B?
Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Q5_K_M (~10 GB) is an option if you have spare VRAM. Use our VRAM calculator to compare.
How long can Qwen3 14B's context window handle?
Qwen3 14B supports a 32,768-token context window - enough for most medium-length documents and conversations. Real-world usable context may vary by implementation.
What models compete with Qwen3 14B?
Qwen3 14B competes with other models in its class. Browse our model directory for comparisons, benchmarks, and community reviews to find the best fit.
Compare & pair with
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Nearby options
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