How much VRAM does Qwen 2.5 Coder 14B need?

Qwen 2.5 Coder 14B requires approximately 0.0 GB VRAM at Q4_K_M quantization with 1K context and 10% overhead. At Q8_0 it needs ~0.0 GB, and at FP16 ~0.0 GB. Use the PCPARTGUIDE VRAM Calculator for exact requirements at any context length and overhead setting.

Can I run Qwen 2.5 Coder 14B on a consumer GPU?

Yes, Qwen 2.5 Coder 14B can run on consumer GPUs. At Q4_K_M quantization with moderate context length, it fits on a 24 GB GPU. For best performance, use a 24 GB or larger GPU with at least 8 GB VRAM for reasonable context lengths.

What is Qwen 2.5 Coder 14B's context window?

Qwen 2.5 Coder 14B supports a maximum context window of 32,768 tokens. The default context is 32,768 tokens. Larger contexts require proportionally more VRAM for the KV cache — use the VRAM Calculator to estimate exact requirements at your target context length.

What quantization level should I use for Qwen 2.5 Coder 14B?

For Qwen 2.5 Coder 14B, start with Q4_K_M (~0.0 GB) if VRAM-limited. Q8_0 offers better quality at ~2× the VRAM. If you have enough VRAM, Q6_K (~1.5× Q4 VRAM) provides a good balance. For models this large, Q3_K_M may be necessary to fit on consumer hardware.

How does Qwen 2.5 Coder 14B compare to similar models?

Qwen 2.5 Coder 14B is a 14.7B Dense model from the Qwen family. Browse other Qwen models on the PCPARTGUIDE directory for comparable alternatives with different parameter counts and VRAM requirements. Use the Model Transparency scores to assess documentation quality and reproducibility.

QwenDenseApache 2.0

Qwen 2.5 Coder 14B

Qwen 2.5 Coder 14B is a dense transformer language model from the Qwen family, containing 14.7B parameters across 48 layers. It supports up to 33K tokens of context with a hidden dimension of 5120 and 8 KV heads for efficient grouped-query …

14.7B

Parameters

32K

Max Context

Dense

Architecture

—

Released

Text

Modality

About Qwen 2.5 Coder 14B

Code

Technical Specifications

Total Parameters14.7B

ArchitectureDense

Attention TypeGQA (Grouped Query Attention)

Hidden Dimensiond = 5,120

Transformer Layers48

Attention Heads40

KV Headsn_kv = 8

Head Dimensiond_head = 128

Activation FunctionSwiGLU

NormalizationRMSNorm

Position EmbeddingRoPE

System Requirements

Estimated VRAM at 10% overhead for different quantization methods and context sizes.

Quantization	1K ctx	32K ctx
Q4_K_M0.50 B/W ~97% of FP16	7.79Consumer GPU	13.60Consumer GPU
Q8_01.00 B/W ~100% of FP16	15.38Consumer GPU	21.20Consumer GPU
F162.00 B/W Reference	30.58Datacenter GPU	36.39Datacenter GPU

Use the interactive VRAM Calculator to see exactly how much memory you need at any quantization level, context length, and overhead setting.

Open VRAM Calculator Best GPU Guide VRAM Guide