Qwen 2.5 Coder 1.5B Instruct

Overview

Alibaba's smallest Qwen 2.5 Coder at 1.5B parameters under Apache 2.0, covering 92 programming languages. HumanEval 70.7 makes it a serious on-device completion model.

When to pick this model

• Local inline completion in IDE plugins
• Edge devices and laptops without dedicated GPUs
• Latency-critical code suggestions where 7B is too slow
• Fallback model when bigger coders are unavailable

VRAM requirements by quantization

VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.

In practice, Qwen 2.5 Coder 1.5B Instruct fits an 8 GB consumer card at Q4_K_M (1 GB). Stepping up to Q8_0 nearly doubles the footprint to 2 GB, and unquantized FP16 weights take 3 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.

Without a GPU, Qwen 2.5 Coder 1.5B Instruct needs roughly 3 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 50 tokens/sec on entry-level GPUs, on the order of 150 tokens/sec on a mid-range card, and up to 300 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.

What hardware do you need

The table below matches Qwen 2.5 Coder 1.5B Instruct to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.

Which GPU should you buy to run Qwen 2.5 Coder 1.5B Instruct?

To run Qwen 2.5 Coder 1.5B Instruct locally at Q4, you need ~1 GB of VRAM. The best value for this is a RTX 5060 (8 GB VRAM).

Strengths

• Around 1GB VRAM at Q4 — runs nearly anywhere
• Strong inline completion for a 1.5B model
• Apache 2.0 license
• 92 programming languages covered

Limitations

• 1.5B caps code quality — not for complex generation
• 32k context only
• Outclassed on harder tasks by 7B+ coders

Typical workloads

In our catalog grid, Qwen 2.5 Coder 1.5B Instruct is filed under Inline Completion, CPU/Edge — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: code generation and review (pair it with an editor integration like Continue.dev or Cline).

The 32k-token context window covers long chats and mid-sized documents, though very large retrieval workloads will need chunking. The Apache 2.0 license is permissive, so shipping it inside a commercial product raises no special legal questions.

Architecture & training

Architecture: Dense · 1.5B · Qwen 2.5 Coder · compact code-specialized

Training: 1.5B params, code corpus across 92 languages, ideal for lightweight completion.

Quick start

ollama run qwen2.5-coder:1.5b

Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.

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Frequently asked questions

How much VRAM does Qwen 2.5 Coder 1.5B Instruct need?

At the recommended Q4_K_M quantization, Qwen 2.5 Coder 1.5B Instruct needs about 1 GB of VRAM. Q8_0 takes 2 GB, and unquantized FP16 weights take 3 GB.

Can Qwen 2.5 Coder 1.5B Instruct run without a GPU?

Yes — with roughly 3 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.

What context window does Qwen 2.5 Coder 1.5B Instruct support?

Qwen 2.5 Coder 1.5B Instruct supports a 32k-token context window (32,768 tokens).

Can I use Qwen 2.5 Coder 1.5B Instruct commercially?

Yes. Qwen 2.5 Coder 1.5B Instruct is released under Apache 2.0, a permissive open-source license that allows commercial use, modification and redistribution.

How fast is Qwen 2.5 Coder 1.5B Instruct on consumer hardware?

Our compatibility engine estimates on the order of 150 tokens/sec on a mid-range GPU and up to 300 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.

Which quantization of Qwen 2.5 Coder 1.5B Instruct should I download first?

Start with Q4_K_M (1 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. It already fits an 8 GB card at FP16.