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SYS · ONLINEUPTIME · 100%2026 · operator-owned
RUNLOCALAI · v38
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  4. /Qwen 2.5 Coder 7B Instruct
qwen
7B parameters
Commercial OK
·Reviewed May 2026

Qwen 2.5 Coder 7B Instruct

Coding-specialized Qwen 2.5 at 7B. The 8-12GB-VRAM coding model — entry-tier autocomplete + IDE assistant. Smaller sibling of the 14B / 32B Coder line.

License: Apache 2.0·Released Nov 12, 2024·Context: 131,072 tokens

Overview

Coding-specialized Qwen 2.5 at 7B. The 8-12GB-VRAM coding model — entry-tier autocomplete + IDE assistant. Smaller sibling of the 14B / 32B Coder line.

Family & lineage

How this model relates to others in its lineage. Family members share architecture and training-data roots; parent / children edges record direct distillation or fine-tune relationships.

Parent / base model
Qwen 2.5 Coder 14B Instruct14B
Consumer
Family siblings (qwen-2.5-coder)
Qwen 2.5 Coder 1.5B1.5B
Edge
Qwen 2.5 Coder 3B3B
Edge
Qwen 2.5 Coder 7B Instruct7B
You are here
Qwen 2.5 Coder 14B Instruct14B
Consumer
Qwen 2.5 Coder 32B Instruct32B
Workstation
Distilled / fine-tuned from this
Qwen 2.5 Coder 1.5B1.5B
Edge
Qwen 2.5 Coder 3B3B
Edge

Strengths

  • Apache 2.0
  • Fits comfortably in 8GB VRAM at Q4_K_M
  • 60-80 tok/s autocomplete on consumer 12-16GB GPUs

Weaknesses

  • Trails 14B / 32B on multi-file refactoring
  • Smaller context coverage than larger siblings

Quantization variants

Each quantization trades model quality for file size and VRAM. Q4_K_M is the most popular starting point.

QuantizationFile sizeVRAM required
Q4_K_M4.7 GB6 GB
Q6_K6.3 GB8 GB

Get the model

Ollama

One-line install

ollama run qwen2.5-coder:7bRead our Ollama review →

HuggingFace

Original weights

huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct

Source repository — direct quantization required.

Benchmarks

Real measurements on real hardware. Numbers ship with the runner version, quant, and date.

1 run on record
HardwareProvenanceQuantCtxTokens / secVRAMTTFTDate
NVIDIA GeForce RTX 3080 16GB (Mobile)(Ollama)
✓EditorialM
Q4_K_M8K
79.4tok/s
——May 10, 26
§How we measure
Every benchmark on this site ships with the runner version, driver version, prompt, and date. Predictions are graded with confidence badges (M / C / ~ / E) so you know which numbers to trust for purchasing decisions. Read the methodology →
Help keep this page accurate

We read every submission. Editorial review takes 1-7 days.

Submit a benchmarkReport outdatedSuggest a correction

What to do next

Got this model running on real hardware? Share what you measured — the form arrives with the model pre-selected.

Submit a benchmark for Qwen 2.5 Coder 7B Instruct
OrBrowse the benchmark roadmapCompare hardware options

Hardware that runs this

Cards with enough VRAM for at least one quantization of Qwen 2.5 Coder 7B Instruct.

NVIDIA GB200 NVL72
13824GB · nvidia
AMD Instinct MI355X
288GB · amd
AMD Instinct MI325X
256GB · amd
AMD Instinct MI300X
192GB · amd
NVIDIA B200
192GB · nvidia
NVIDIA H100 NVL
188GB · nvidia
NVIDIA H200
141GB · nvidia
Intel Gaudi 3
128GB · intel

Frequently asked

What's the minimum VRAM to run Qwen 2.5 Coder 7B Instruct?

6GB of VRAM is enough to run Qwen 2.5 Coder 7B Instruct at the Q4_K_M quantization (file size 4.7 GB). Higher-quality quantizations need more.

Can I use Qwen 2.5 Coder 7B Instruct commercially?

Yes — Qwen 2.5 Coder 7B Instruct ships under the Apache 2.0, which permits commercial use. Always read the license text before deployment.

What's the context length of Qwen 2.5 Coder 7B Instruct?

Qwen 2.5 Coder 7B Instruct supports a context window of 131,072 tokens (about 131K).

How do I install Qwen 2.5 Coder 7B Instruct with Ollama?

Run `ollama pull qwen2.5-coder:7b` to download, then `ollama run qwen2.5-coder:7b` to start a chat session. The default quantization is Q4_K_M.

Source: huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct

Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify model claims.

Related — keep moving

Compare hardware
  • 4060 Ti 16 GB vs 4070 Ti Super →
  • Arc B580 vs 4060 Ti 16 GB →
Buyer guides
  • Best budget GPU — for 7B-13B models →
  • Best GPU for local AI →
  • Best laptop for local AI →
  • Best Mac for local AI →
  • Best used GPU for local AI →
When it doesn't work
  • CUDA out of memory →
  • Ollama running slowly →
  • ROCm not detected →
  • Model keeps crashing →
Recommended hardware
  • NVIDIA GB200 NVL72 →
  • AMD Instinct MI355X →
  • AMD Instinct MI325X →
  • AMD Instinct MI300X →
  • NVIDIA B200 →
Alternatives
Qwen 2.5 Coder 1.5BQwen 2.5 Coder 3BQwen 2.5 Coder 14B InstructQwen 2.5 Coder 32B Instruct
Before you buy

Verify Qwen 2.5 Coder 7B Instruct runs on your specific hardware before committing money.

Will it run on my hardware? →Custom hardware comparison →GPU recommender (4 questions) →
Compare alternatives

Models worth comparing

Same parameter band, plus what's one tier above and below — so you can decide what actually fits your hardware.

Same tier
Models in the same parameter band as this one
  • DeepSeek R1 Distill Qwen 7B
    deepseek · 7B
    unrated
  • DeepSeek R1 Distill Llama 8B
    deepseek · 8B
    unrated
  • Codestral Mamba 7B
    mistral · 7B
    unrated
  • Llama 3.1 8B Instruct
    llama · 8B
    8.7/10
Step up
More capable — bigger memory footprint
  • Qwen 3 14B
    qwen · 14B
    8.8/10
  • Phi-4 14B
    phi · 14B
    8.6/10
Step down
Smaller — faster, runs on weaker hardware
  • Gemma 3 4B
    gemma · 4B
    7.5/10
  • Llama 3.2 3B Instruct
    llama · 3B
    7.4/10

Community benchmarks for this model

Submit your own →

Operator-submitted measurements that have passed editorial review. Each row's provenance badge shows whether it's community-submitted, reproduced by us, or independently reproduced.

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