Qwen2.5-Coder-14B-Instruct-GPTQ-Int8
Qwen2.5-Coder-14B-Instruct-GPTQ-Int8 is a 14.7 billion parameter code-focused language model from Alibaba's Qwen team, quantized to 8-bit using GPTQ for reduced memory footprint. It is instruction-tuned for coding tasks and supports up to 131K token context length. Licensed under Apache 2.0, ungated, and designed for deployment in resource-constrained environments.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | Qwen |
| Parameters | 14.8B |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 56.5k |
| Likes | 7 |
| Last updated | 2025-01-12 |
| Source | Qwen/Qwen2.5-Coder-14B-Instruct-GPTQ-Int8 |
What Qwen2.5-Coder-14B-Instruct-GPTQ-Int8 is
This is a GPTQ 8-bit quantized variant of Qwen2.5-Coder-14B-Instruct, a decoder-only transformer with 48 layers, 40 query heads and 8 key-value heads (GQA), RoPE positional embeddings, SwiGLU activation, and RMSNorm. The model was trained on 5.5 trillion tokens including source code, text-code grounding, and synthetic data. It supports long-context handling via YaRN rope scaling up to 131K tokens. Quantization reduces precision to 8-bit INT8 while maintaining instruction-following behavior.
Run Qwen2.5-Coder-14B-Instruct-GPTQ-Int8 locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="Qwen/Qwen2.5-Coder-14B-Instruct-GPTQ-Int8")out = pipe("Explain retrieval-augmented generation in one sentence.", max_new_tokens=128)print(out[0]["generated_text"])Swap in vLLM or Ollama for production-grade serving. DEV.co can stand up the inference stack.
How you'd run it
A typical self-hosted path — open weights, an inference server, your application.
DEV.co builds each layer — from GPU infrastructure to the application.
Best use cases
Running & fine-tuning it
ESTIMATE: 8-bit GPTQ quantization of 14.7B parameters ≈ ~14–16 GB VRAM (including overhead). Verify exact memory usage with your serving framework (vLLM, TGI). Requires GPU with CUDA support for practical throughput. Model card references GPU memory and throughput benchmarks at https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html (consult for production estimates).
Fine-tuning a GPTQ-quantized model is non-trivial. Typically, fine-tuning is applied to the base unquantized model or via QLoRA (Quantized LoRA) adapters. The model card does not explicitly document QLoRA support or fine-tuning workflow. Recommend consulting Qwen's documentation or running experiments; if fine-tuning is required, consider obtaining the unquantized Qwen2.5-Coder-14B-Instruct variant.
When to avoid it — and what to weigh
- Ultra-Low Latency Requirements on CPU — GPTQ 8-bit models require GPU to achieve practical throughput. CPU inference will be slow; consider smaller quantized variants or distilled models if CPU-only deployment is mandatory.
- Strict Performance Parity with Larger Models Required — At 14B parameters, this model is smaller than flagship code models (32B variant, GPT-4o). If benchmark-driven evaluation shows unacceptable accuracy loss on your task, evaluate the 32B variant or closed-source alternatives.
- Multi-Language Code Coverage Beyond English — Model card does not claim comprehensive multilingual code support. If your primary use case involves non-English codebases, verify capability or select alternatives with explicit multilingual training.
- Real-Time Compliance or Audit Logging Requirements — Self-hosted deployment requires internal logging and monitoring infrastructure. If regulatory compliance mandates external audit trails or managed services, this self-hosted approach may not satisfy requirements.
License & commercial use
Apache License 2.0 (OSI-approved permissive license). Permits commercial use, modification, and redistribution under the condition that the license and copyright notice are included. No patent grant or liability limitation beyond standard Apache 2.0 terms.
Apache 2.0 is a permissive, OSI-approved license that explicitly permits commercial use without restriction. You may deploy this model in commercial products, closed-source applications, or SaaS offerings. Ensure you include a copy of the Apache 2.0 license and notice of modifications if distributed. No vendor support or indemnification is provided by Qwen; commercial liability and support are your responsibility.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Strong |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Strong |
| Assessment confidence | High |
No explicit security posture statement in the card. Considerations for self-hosted deployment include: (1) Model provenance: verify authenticity via HuggingFace hub checksums; (2) Input validation: untrusted code input to the model may trigger unexpected behavior or resource exhaustion; (3) Output validation: LLM outputs should not be executed without review (prompt injection, malicious code generation); (4) Dependency supply chain: transformers library and quantization dependencies require regular patching; (5) Data leakage: model may reproduce training data—review for sensitive code exposure if applicable. Perform threat modeling for your specific threat model (e.g., air-gapped vs. internet-connected).
Alternatives to consider
Qwen2.5-Coder-32B-Instruct (or GPTQ variant)
Larger 32B model with improved coding capability; model card claims performance parity with GPT-4o. Use if your hardware can accommodate ~32–40 GB VRAM or if higher accuracy is critical.
CodeLlama-13B-Instruct-hf or Meta's Llama Code models (quantized)
Alternative open-source code LLM from Meta. Smaller footprint (13B); well-established community. Consider if Qwen ecosystem or Alibaba partnership is a concern.
DeepSeek-Coder-6.7B or similar distilled code models
Smaller footprint (~6–7B parameters) for cost-sensitive or edge deployments. Trade-off: reduced capability for significant reduction in latency and memory.
Ship Qwen2.5-Coder-14B-Instruct-GPTQ-Int8 with senior software developers
Evaluate this model on your codebase using vLLM or TGI with your target hardware. Verify memory footprint and latency against your SLAs. For enterprise multi-tenant or compliance-heavy deployments, confirm internal monitoring and audit logging are in place. Consult the Qwen documentation and benchmark data before committing production resources.
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Qwen2.5-Coder-14B-Instruct-GPTQ-Int8 FAQ
Can I use this model in a commercial product?
How much GPU VRAM do I need?
Does this model support fine-tuning or LoRA adaptation?
What is the maximum context length?
Software developers & web developers for hire
Adopting Qwen2.5-Coder-14B-Instruct-GPTQ-Int8 is usually one piece of a larger software development effort. As a software development agency, DEV.co provides software development services and web development expertise — pairing senior software developers and web developers with your team to design, build, and operate open-source llms software in production.
Ready to Deploy Qwen2.5-Coder-14B in Production?
Evaluate this model on your codebase using vLLM or TGI with your target hardware. Verify memory footprint and latency against your SLAs. For enterprise multi-tenant or compliance-heavy deployments, confirm internal monitoring and audit logging are in place. Consult the Qwen documentation and benchmark data before committing production resources.