Qwen 2.5 Coder 14B Instruct

Overview

Alibaba's Qwen 2.5 Coder 14B under Apache 2.0 with HumanEval 89.6 and LiveCodeBench 37.1. The VRAM sweet spot for serious self-hosted code generation.

When to pick this model

• Self-hosted coding agents on a single 24GB GPU
• Repo-scale code generation needing 131k context
• Permissively licensed alternative to Codestral
• Multi-language production codebases

VRAM requirements by quantization

Quantization	VRAM required
Q4_K_M (recommended)	9 GB
Q5_K_M	11 GB
Q8_0	16 GB
FP16 (no quantization)	28 GB

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 14B Instruct needs a 12 GB card at Q4_K_M (9 GB). Stepping up to Q8_0 nearly doubles the footprint to 16 GB, and unquantized FP16 weights take 28 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.

Without a GPU, Qwen 2.5 Coder 14B Instruct needs roughly 16 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 6 tokens/sec on entry-level GPUs, on the order of 20 tokens/sec on a mid-range card, and up to 55 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 14B 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.

GPU memory	Example cards	Best fit for Qwen 2.5 Coder 14B Instruct
8 GB	RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB	Does not fit — needs 9 GB at Q4_K_M
12 GB	RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop	Q5_K_M (11 GB used)
16 GB	RTX 5080, RTX 4080 Super, Radeon RX 9070 XT	Q8_0 (16 GB used)
24 GB	RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop	Q8_0 (16 GB used)
32 GB	RTX 5090	FP16 (28 GB used)

Which GPU should you buy to run Qwen 2.5 Coder 14B Instruct?

To run Qwen 2.5 Coder 14B Instruct locally at Q4, you need ~9 GB of VRAM. The best value for this is a RTX 5070 (12 GB VRAM).

Published benchmark scores

Benchmark	Score
HumanEval	89.6
MBPP	86.2
LiveCodeBench	37.1

Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.

To put Qwen 2.5 Coder 14B Instruct in context: its HumanEval score of 89.6 ranks #3 of the 26 catalog models with a published HumanEval result (catalog median 81.1). Scores are author-published and measured under different harnesses, so treat gaps of a few points as noise rather than a verdict.

Strengths

• HumanEval 89.6 — competitive with much larger coders
• LiveCodeBench 37.1
• Apache 2.0 license
• 131k context for long-file work

Limitations

• Weaker than general 14B models on non-code chat
• No vision input
• Outscored by frontier closed APIs on the hardest benchmarks

Typical workloads

In our catalog grid, Qwen 2.5 Coder 14B Instruct is filed under Local Copilot, Senior Code, Code Agents — 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 128k-token context window is large enough to hold entire codebases' worth of files or long reports in a single prompt, which is what makes local RAG and document analysis practical. The Apache 2.0 license is permissive, so shipping it inside a commercial product raises no special legal questions.

Architecture & training

Architecture: Dense 14B code · FIM

Training: 5.5T tokens of code.

Quick start

ollama run qwen2.5-coder:14b

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

Similar models worth comparing

Frequently asked questions

How much VRAM does Qwen 2.5 Coder 14B Instruct need?

At the recommended Q4_K_M quantization, Qwen 2.5 Coder 14B Instruct needs about 9 GB of VRAM. Q8_0 takes 16 GB, and unquantized FP16 weights take 28 GB.

Can Qwen 2.5 Coder 14B Instruct run without a GPU?

Yes — with roughly 16 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 14B Instruct support?

Qwen 2.5 Coder 14B Instruct supports a 128k-token context window (131,072 tokens).

Can I use Qwen 2.5 Coder 14B Instruct commercially?

Yes. Qwen 2.5 Coder 14B 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 14B Instruct on consumer hardware?

Our compatibility engine estimates on the order of 20 tokens/sec on a mid-range GPU and up to 55 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.

Which quantization of Qwen 2.5 Coder 14B Instruct should I download first?

Start with Q4_K_M (9 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. On a 24 GB card you can run up to Q8_0.