AI Code Assistant

Qwen 2.5 Coder

Top-ranked Apache 2.0 open-weight coding LLM family (1.5B–32B) rivaling proprietary frontier models on code generation and repair.

Kunjungi Situs
Qwen 2.5 Coder
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Kalkulator VRAM GPU & Kuantisasi

Estimasi VRAM GPU & Spesifikasi Hardware Qwen 2.5 Coder

Calculate required VRAM (model weights + activation/KV cache) across BF16, FP8, INT4, and GGUF Q4_K_M.

Quantization Precision
Context / Image Resolution
Estimated Required VRAM
22.6 GB Apache 2.0 Commercial
Model Weights: 20.2 GB KV / Activation Overhead: 2.4 GB

Kompatibilitas GPU & Apple Silicon

RTX 3060 12GB Insufficient VRAM
RTX 4070/4080 16GB Insufficient VRAM
RTX 4090 24GB Smooth Fit
Mac Apple Silicon 16GB/32GB/64GB Needs 32GB+ Unified
Cloud GPU / API Cloud / API Ready

Open-Weight & Cloud Variant Matrix

Model Variant License Speed / Steps Local vs API Commercial Use
Qwen 2.5 / Coder 7B Apache 2.0 90+ tok/s (RTX 4070 · 128K ctx) Local ~6.0 GB (Q4_K_M) / 16.5 GB (BF16) Yes — Free Apache 2.0 Commercial
Qwen 2.5 / Coder 32B Apache 2.0 38+ tok/s (RTX 4090 · Top Open Coding) Local ~22.6 GB (Q4_K_M) / Mac 32GB+ Yes — Free Apache 2.0 Commercial
Qwen 2.5 72B Qwen License 128K Context Flagship Multilingual Mac 64GB / 2×24GB GPU (INT4) or Cloud API Free Commercial (<100M MAU)

Tentang alat ini

Qwen 2.5 Coder (0.5B, 1.5B, 3B, 7B, 14B, and flagship 32B-Instruct) is pretrained on 5.5 trillion code and technical tokens across 90+ programming languages with full 128K repository context and Apache 2.0 licensing on key commercial tiers.

Local AI IDE Setup

  • 24 GB RTX 3090 / 4090 or 32 GB Mac: Run qwen2.5-coder:32b in GGUF Q4_K_M (~22.6 GB) via Ollama or LM Studio for private, zero-per-token coding agents in Cline, Roo Code, and Continue.
  • 8 GB–16 GB Laptops: Pair qwen2.5-coder:7b or 14b for instant local FIM tab-autocomplete.
#Qwen 2.5 Coder, Local Coding AI, Apache 2.0, Ollama

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Qwen 2.5 Coder

Pertanyaan yang sering diajukan

Which Qwen 2.5 Coder size fits my GPU for local VS Code (Continue / Cline) coding?

For 8GB–12GB GPUs (RTX 3060 / 4060), Qwen 2.5 Coder 7B or 14B in GGUF Q4_K_M delivers instant FIM autocomplete and chat (~6–11 GB VRAM). For 24GB GPUs (RTX 3090/4090) or 32GB+ Macs, Qwen 2.5 Coder 32B (~22.6 GB at 8K ctx) rivals GPT-4o on LiveCodeBench and Aider benchmarks.

How much extra VRAM do I need for 32K repository context in Qwen 2.5 Coder?

Extending KV cache from 8K to 32K tokens on the 32B model adds ~3.8 GB of VRAM overhead (pushing Q4_K_M from ~22.6 GB to ~26.4 GB). On a single 24GB GPU, enable Q8_0 KV cache quantization in Ollama/llama.cpp to fit full 32K repo context cleanly.

Is Qwen 2.5 Coder 32B licensed under Apache 2.0 for proprietary company codebases?

Yes! Every Qwen 2.5 Coder checkpoint from 0.5B, 1.5B, 7B, 14B up to 32B (except 3B) is Apache 2.0 licensed, making it safe for air-gapped enterprise code review and private IDE copilots.

How many programming languages was Qwen 2.5 Coder trained on?

Qwen 2.5 Coder was trained on 5.5 trillion code-centric tokens spanning 92+ programming languages—excelling in Python, TypeScript, Rust, Go, C#, C++, SQL, Shell, and multi-file refactoring.

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