Qwen3-14B-GPTQ-Int4
Qwen3-14B-GPTQ-Int4 is a 4-bit quantized version of Alibaba's Qwen3-14B language model. It reduces model size and memory footprint while maintaining inference speed through GPTQ quantization (group size 128, desc_act=False). The base model supports both 'thinking mode' (for reasoning tasks) and 'non-thinking mode' (for fast responses), handles 100+ languages, and can process up to 131K tokens with positional interpolation. This quantized variant is optimized for vLLM serving and local deployment.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | JunHowie |
| Parameters | 14.8B |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 197.1k |
| Likes | 4 |
| Last updated | 2025-09-05 |
| Source | JunHowie/Qwen3-14B-GPTQ-Int4 |
What Qwen3-14B-GPTQ-Int4 is
Qwen3-14B-GPTQ-Int4 is a GPTQ-quantized derivative of Qwen/Qwen3-14B (14.8B parameters, 40 layers, GQA with 40 Q-heads and 8 KV-heads). Quantization to 4-bit with group size 128 and desc_act=False configuration claims improved token throughput over earlier quantized versions. Native context length is 32,768 tokens, extendable to 131,072 via YaRN. Model card indicates support for vLLM (>=0.9.2), transformers (>=4.51.0), and mentions compatibility with sglang, Ollama, LMStudio, and llama.cpp. Last modified 2025-09-05.
Run Qwen3-14B-GPTQ-Int4 locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="JunHowie/Qwen3-14B-GPTQ-Int4")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: 4-bit Int4 quantization of 14.8B parameters typically requires 7–9 GB VRAM (fp16 baseline ~28–30 GB). Actual footprint depends on vLLM batch size, max_model_len, and KV cache strategy. Model card does not specify exact VRAM. Requires transformers>=4.51.0 and vLLM>=0.9.2 for thinking mode support. Verify on target hardware before production.
Unknown. Model card does not address LoRA, QLoRA, or instruction-tuning feasibility on the quantized variant. Quantized models can be fine-tuned in some frameworks (e.g., via bitsandbytes QLoRA), but support depends on downstream library (vLLM, transformers version). Contact maintainer or consult Qwen documentation for guidance.
When to avoid it — and what to weigh
- Extreme Latency Sensitivity + Thinking Mode Required — Thinking mode intentionally generates reasoning tokens (wrapped in <think>...</think>), which increases output length and latency. If sub-second response times are mandatory and reasoning is needed, non-thinking mode sacrifices quality.
- Minimal VRAM Budget (<8 GB) — Even 4-bit quantized, 14B parameters still requires meaningful VRAM. Exact footprint depends on batch size and context length. Verify hardware before deployment.
- No Access to Upstream Bug Fixes or Security Patches — This is a community quantization. If critical issues arise in base Qwen3-14B, fixes may lag or depend on JunHowie's maintenance. No SLA or guaranteed support.
- Strict Compliance or Safety Audit Requirements — Quantization and community repackaging introduce additional layers. If your use case requires traced provenance or formal safety certification, evaluate base model licensing and quantization methodology first.
License & commercial use
Apache-2.0 license (OSI-approved). Permits commercial use, modification, and distribution under Apache-2.0 terms. Quantization is derivative work; original Qwen3-14B is also Apache-2.0 (Alibaba).
Apache-2.0 permits commercial use and redistribution. However: (1) This is a community quantization by JunHowie, not an official Alibaba release. (2) Quantization changes the model behavior and output; verify acceptability in your compliance/product requirements. (3) No SLA, support, or indemnification from JunHowie or Alibaba. Consult legal review before production deployment.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Moderate |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Good |
| Assessment confidence | Medium |
Model card does not disclose security review, adversarial testing, or jailbreak mitigations. Quantization itself is a lossy compression technique; verify output stability on adversarial inputs. Community derivative; no formal security audit published. If deploying in security-sensitive contexts (e.g., healthcare, finance), conduct threat modeling and input validation independently.
Alternatives to consider
Qwen/Qwen3-14B (unquantized)
Official model with full precision (FP16/BF16). Better quality/consistency, but 2–3× higher VRAM (~28–30 GB). Choose if hardware permits and reasoning quality is critical.
Meta Llama-2-13B or Llama-3-8B (quantized)
Smaller, widely-deployed alternatives with mature ecosystem. Llama license is non-commercial-restrictive for redistribution; verify compliance. No thinking mode, but faster and lower VRAM.
Mistral-7B (quantized)
Smaller footprint, strong performance on reasoning and code. Apache-2.0 licensed. Lacks thinking mode but offers speed/cost tradeoff for inference-only workloads.
Ship Qwen3-14B-GPTQ-Int4 with senior software developers
Test this quantized model on your hardware with vLLM or Ollama. Verify reasoning quality and latency tradeoffs in thinking mode. For custom tuning or RAG integration, consult Devco's AI engineering team.
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Qwen3-14B-GPTQ-Int4 FAQ
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How does this differ from the base Qwen3-14B?
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Ready to Deploy Qwen3-14B-GPTQ-Int4?
Test this quantized model on your hardware with vLLM or Ollama. Verify reasoning quality and latency tradeoffs in thinking mode. For custom tuning or RAG integration, consult Devco's AI engineering team.