Family Qwen · 3B parameters

Qwen 2.5 Coder 3B Instruct

Coder 3B. HumanEval 84.1. ⚠ Qwen Research license (non-commercial).

🇨🇳 Alibaba·License Qwen Research License·Context 32k tokens·Output November 2024·Fits within the 128 GB of the GIGABYTE AI TOP ATOM← Catalog

01What it can do

Strengths
  • Lightweight (2 GB VRAM Q4)
  • Apache 2.0
  • Fast code completion
Limitations to know
  • —32k context only
  • —Less capable than the 7B for complex code
Architecture
Dense · 3B · Qwen 2.5 Coder · code optimized
Training
3B code-optimized parameters, 92 programming languages.
Ideal for
Laptop completionPrototyping

04Install

Install Ollama for your OS. Check the model and its quantization before downloading. Start with 4096 tokens of context, then check placement with ollama ps. A command below is not proof that a test was run on your machine.

$ollama run qwen2.5-coder:3b
⚠
First download: between 2 and 40 GB depending on the selected quantization. Plan for sufficient disk space; a stable connection is recommended. Subsequent launches are instant.

02Required memory

Approximate GPU VRAM required to run this model, including 4k tokens of context overhead. For a longer context, add ~1 GB per 8k-token increment.

Q4_K_M
The lightest, ~5% loss
2 GB
Q5_K_M
Good quality/size compromise
2.5 GB
Q8_0
Nearly indistinguishable from FP16
4 GB
FP16
Full precision — server use
6 GB
Fallback CPU · If you don't have a GPU, allow 5 GB of RAM minimum to run this model at reduced speed.

What hardware do you need for Qwen 2.5 Coder 3B Instruct?

To run Qwen 2.5 Coder 3B Instruct locally with Q4 quantization, you need about 2 GB of VRAM. An option to compare: RTX 5060 Ti 16GB (ASUS Prime) — leave some headroom for the system and context; check engine compatibility with the GPU.

Current offer: RTX 5060 Ti 16GB (ASUS Prime)
AmazonSee price →

Affiliate links — commission possible at no extra cost to you; independent recommendations. As an Amazon Associate, BestLLMfor earns from qualifying purchases.

On the go: Qwen 2.5 Coder 3B Instruct also runs on a RTX laptop PC (16 GB of VRAM) →

03Expected speed

Tokens generated per second in Q4_K_M, 4k context. Beyond 20 t/s, reading is comfortable. Below 10 t/s, that's just for testing.

Entry-level
~25t/s
GTX 1650, RX 6600, MBA M2 8GB
Mid-range
~70t/s
RTX 4060, 4070, MBP M3 Pro
High-end
~160t/s
RTX 4090, M4 Max, Radeon 7900