Qwen3-14B Instruct
Qwen · Apache 2.0
Qwen 3 dense 14B instruct: capable general-purpose performance with 32K default and 128K max context. Apache 2.0.
- Parameters
- 14B
- Architecture
- Dense
- Context
- 131,072 tokens
- Released
- 2025-04-29
- Engines
- llama.cpp, ollama, vLLM
- Builder Tools
- Continue, LM Studio, Open WebUI
Parameters
14B
VRAM
10 GB
Context
128K
Formats
4
GPUs
26
Qwen3-14B Instruct (14B) requires 10 GB VRAM at recommended quality (Q5_K_M). At efficient quality (Q4_K_M), it fits in 8.5 GB VRAM, making it compatible with the NVIDIA RTX 4060 Laptop (40-60W). On NVIDIA Grace Blackwell Ultra GB300, expect approximately 230 tok/s at Q8_0. For the best experience, Starter AI Desktop ($582) is recommended.
Source: OwnRig methodology
10 GB
Q5_K_M
9.5 GB
128K tokens
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VRAM Requirements
| Quality | Quantization | VRAM | File Size |
|---|---|---|---|
| full | Q8_0 | 15 GB | 14 GB |
| recommended | Q5_K_M | 10 GB | 9.5 GB |
| efficient | Q4_K_M | 8.5 GB | 8 GB |
| compressed | Q3_K_M | 7 GB | 6.5 GB |
Context Length Impact
KV cache VRAM at Q5_K_M quality. Longer context = more memory.
| Context | KV Cache | Total VRAM |
|---|---|---|
| 2K | 205 MB | 10.2 GB |
| 4K | 410 MB | 10.4 GB |
| 8K | 717 MB | 10.7 GB |
| 16K | 1.4 GB | 11.4 GB |
| 32K | 2.9 GB | 12.9 GB |
| 64K | 5.8 GB | 15.8 GB |
| 128K | 11.5 GB | 21.5 GB |
Compatible GPUs
26 devicesShowing 26 of 26 entries
Builder Context
Qwen3-14B Instruct is commonly used with Continue, LM Studio, Open WebUI. For an AI coding workflow, pair it with an embedding model like nomic-embed-text for local RAG.
Frequently Asked Questions
- How much VRAM does Qwen3-14B Instruct need?
- Qwen3-14B Instruct requires 10 GB VRAM at recommended quality (Q5_K_M). At lower quality settings, it can fit in as little as 7 GB.
- What is the best GPU for Qwen3-14B Instruct?
- The NVIDIA Grace Blackwell Ultra GB300 delivers the best performance for Qwen3-14B Instruct, achieving 230 tok/s at Q8_0 with an excellent rating.
- Can I run Qwen3-14B Instruct on an RTX 4060 Ti?
- Yes. On the NVIDIA GeForce RTX 4060 Ti 16GB, Qwen3-14B Instruct runs at 16 tok/s (Q8_0, acceptable).
- What quantization should I use for Qwen3-14B Instruct?
- For the best quality, use Q5_K_M (10 GB VRAM). If your GPU has limited VRAM, Q3_K_M (7 GB) is the most efficient option with acceptable quality.
- Is Qwen3-14B Instruct good for coding?
- Yes. Qwen3-14B Instruct is used with Continue, LM Studio, Open WebUI for local AI coding. For the best coding experience, pair it with an embedding model for local RAG.
Data confidence: estimated. Source
VRAM requirements are calculated from model parameters and may vary by inference engine, context length, and batch size. Performance estimates are based on community benchmarks and should be verified for your specific configuration.Qwen is a trademark of its respective owner. OwnRig is not affiliated with or endorsed by the model creator.