How much VRAM does Qwen 2.5 14B Instruct need?

Qwen 2.5 14B Instruct requires 12.7 GB VRAM at recommended quality (Q6_K). At lower quality settings, it can fit in as little as 6.9 GB.

What is the best GPU for Qwen 2.5 14B Instruct?

The NVIDIA Grace Blackwell Ultra GB300 delivers the best performance for Qwen 2.5 14B Instruct, achieving 220 tok/s at Q8_0 with an excellent rating.

Can I run Qwen 2.5 14B Instruct on an RTX 4060 Ti?

Yes. On the NVIDIA GeForce RTX 4060 Ti 16GB, Qwen 2.5 14B Instruct runs at 30 tok/s (Q4_K_M, good).

What quantization should I use for Qwen 2.5 14B Instruct?

For the best quality, use Q6_K (12.7 GB VRAM). If your GPU has limited VRAM, Q3_K_M (6.9 GB) is the most efficient option with acceptable quality.

Is Qwen 2.5 14B Instruct good for coding?

Yes. Qwen 2.5 14B Instruct is used with Cursor, Continue, Aider, Open WebUI, LM Studio for local AI coding. For the best coding experience, pair it with an embedding model for local RAG.

ChatCodingReasoningMulti-purpose14.77B

Chat

Qwen 2.5 14B Instruct

Qwen · Apache 2.0

Capable general-purpose model with balanced coding and reasoning.

Parameters: 14.77B
Architecture: Dense
Context: 32,768 tokens
Released: 2024-09-19
Engines: llama.cpp, ollama, vLLM, TGI
Builder Tools: Cursor, Continue, Aider, Open WebUI, LM Studio

Parameters

14.77B

VRAM

12.7 GB

Context

32K

Formats

GPUs

Qwen 2.5 14B Instruct (14.77B) requires 12.7 GB VRAM at recommended quality (Q6_K). 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 220 tok/s at Q8_0. For the best experience, Budget Home AI Server ($1,162) is recommended.

Source: OwnRig methodology

VRAM (Recommended)

12.7 GB

Quantization

Q6_K

File Size

11 GB

Max Context

32K tokens

Primary Use

Chat

Memory

VRAM Requirements

Quality	Quantization	VRAM	File Size
full	Q8_0	16.3 GB	14.5 GB
recommended	Q6_K	12.7 GB	11 GB
recommended	Q5_K_M	10.6 GB	9 GB
efficient	Q4_K_M	8.5 GB	7.2 GB
compressed	Q3_K_M	6.9 GB	5.9 GB

Scaling

Context Length Impact

KV cache VRAM at Q6_K quality. Longer context = more memory.

Context	KV Cache	Total VRAM
2K	205 MB	12.9 GB
4K	512 MB	13.2 GB
8K	1 GB	13.7 GB
16K	1.9 GB	14.6 GB
32K	3.8 GB	16.5 GB

Compatible GPUs

22 devices


NVIDIA Grace Blackwell Ultra GB300	Q8_0	220 tok/s	Excellent
NVIDIA GeForce RTX 4090	Q5_K_M	55 tok/s	Excellent
AMD Radeon Pro W7900	Q5_K_M	59 tok/s	Excellent
Apple M4 Max (36GB Unified)	Q5_K_M	38 tok/s	Good
NVIDIA GeForce RTX 4060 Ti 16GB	Q4_K_M	30 tok/s	Good
NVIDIA GeForce RTX 4070 Ti 12GB	Q4_K_M	30 tok/s	Good
NVIDIA RTX 4090 Laptop (150-175W)	Q4_K_M	26 tok/s	Good
AMD Radeon RX 7900 XTX	Q5_K_M	47 tok/s	Good
NVIDIA RTX PRO 6000 Blackwell	Q8_0	66 tok/s	Good
NVIDIA RTX PRO 6000 Blackwell Max-Q	Q8_0	61 tok/s	Good
AMD Radeon RX 9070	Q4_K_M	52 tok/s	Good
NVIDIA GeForce RTX 5060 Ti 16GB	Q4_K_M	34 tok/s	Good
NVIDIA GeForce RTX 3080 10GB	Q3_K_M	24 tok/s	Acceptable
NVIDIA RTX 4060 Laptop (40-60W)	Q3_K_M	10 tok/s	Acceptable
NVIDIA RTX 4070 Laptop (80-115W)	Q3_K_M	12 tok/s	Acceptable
NVIDIA RTX 4080 Laptop (120-150W)	Q4_K_M	21 tok/s	Acceptable
AMD Radeon RX 9060 XT 16GB	Q4_K_M	26 tok/s	Acceptable
Apple M3 Pro (18GB Unified)	Q3_K_M	5 tok/s	Marginal
NVIDIA GeForce RTX 4060 8GB	Q3_K_M	17 tok/s	Marginal
AMD Radeon RX 7600	Q3_K_M	4 tok/s	Marginal
NVIDIA GeForce RTX 5060 8GB	Q3_K_M	20 tok/s	Marginal
AMD Radeon RX 9060 XT 8GB	Q4_K_M	–	Not viable

Showing 22 of 22 entries

Builder Context

Qwen 2.5 14B Instruct is commonly used with Cursor, Continue, Aider, Open WebUI, LM Studio. For an AI coding workflow, pair it with an embedding model like nomic-embed-text for local RAG.

FAQ

Frequently Asked Questions

How much VRAM does Qwen 2.5 14B Instruct need?: Qwen 2.5 14B Instruct requires 12.7 GB VRAM at recommended quality (Q6_K). At lower quality settings, it can fit in as little as 6.9 GB.
What is the best GPU for Qwen 2.5 14B Instruct?: The NVIDIA Grace Blackwell Ultra GB300 delivers the best performance for Qwen 2.5 14B Instruct, achieving 220 tok/s at Q8_0 with an excellent rating.
Can I run Qwen 2.5 14B Instruct on an RTX 4060 Ti?: Yes. On the NVIDIA GeForce RTX 4060 Ti 16GB, Qwen 2.5 14B Instruct runs at 30 tok/s (Q4_K_M, good).
What quantization should I use for Qwen 2.5 14B Instruct?: For the best quality, use Q6_K (12.7 GB VRAM). If your GPU has limited VRAM, Q3_K_M (6.9 GB) is the most efficient option with acceptable quality.
Is Qwen 2.5 14B Instruct good for coding?: Yes. Qwen 2.5 14B Instruct is used with Cursor, Continue, Aider, Open WebUI, LM Studio for local AI coding. For the best coding experience, pair it with an embedding model for local RAG.

All models

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.