How much VRAM does Qwen 2.5 Coder 7B need?

Qwen 2.5 Coder 7B 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 7B on a consumer GPU?

Yes, Qwen 2.5 Coder 7B can run on consumer GPUs. At Q4_K_M quantization with moderate context length, it fits on a 12 GB GPU. For best performance, use a 12 GB or larger GPU with at least 8 GB VRAM for reasonable context lengths.

What is Qwen 2.5 Coder 7B's context window?

Qwen 2.5 Coder 7B 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 7B?

For Qwen 2.5 Coder 7B, Q4_K_M is the recommended quantization — it provides an excellent quality-to-VRAM ratio at ~0.0 GB. Q8_0 offers slightly better quality at ~2× the VRAM. Q4_K_M is the sweet spot for most users.

How does Qwen 2.5 Coder 7B compare to similar models?

Qwen 2.5 Coder 7B is a 7.6B 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 7B

7.6B

Parameters

32K

Max Context

Dense

Architecture

Nov 12, 2024

Released

Text

Modality

About Qwen 2.5 Coder 7B

Qwen 2.5 Coder 7B is the most popular local coding model in the 7B class. Fine-tuned from Qwen 2.5 7B on a massive code corpus spanning 92 programming languages, it delivers exceptional code completion, bug fixing, and code explanation. At ~4 GB VRAM at Q4_K_M it runs on any GPU. Mature GGUF and MLX support. The Apache 2.0 license makes it safe for commercial development workflows. If you can only run one local coding model, this is the default recommendation.

Code CompletionCode ReviewBug FixingCommercial

Technical Specifications

Total Parameters7.6B

ArchitectureDense

Attention TypeGQA (Grouped Query Attention)

Hidden Dimensiond = 3,584

Transformer Layers28

Attention Heads28

KV Headsn_kv = 4

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	3.99Consumer GPU	5.68Consumer GPU
Q8_01.00 B/W ~100% of FP16	7.92Consumer GPU	9.62Consumer GPU
F162.00 B/W Reference	15.79Consumer GPU	17.48Consumer 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