Qwen3-8B-quantized.w4a16
Qwen3-8B-quantized.w4a16 is an 8.3B-parameter language model compressed to INT4 weights, reducing memory and disk by ~75% while maintaining near-baseline accuracy (93.8–100.7% recovery on OpenLLM benchmarks). Released by RedHat/Neural Magic in May 2025, it supports text generation, reasoning, function calling, and multilingual tasks. Licensed under Apache 2.0 (permissive). Deployable via vLLM with modest hardware; minimal accuracy loss except on MMLU-Pro (74.4% recovery).
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | RedHatAI |
| Parameters | 8.3B |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 33.7k |
| Likes | 5 |
| Last updated | 2026-05-05 |
| Source | RedHatAI/Qwen3-8B-quantized.w4a16 |
What Qwen3-8B-quantized.w4a16 is
Qwen3ForCausalLM architecture with INT4 quantization applied via GPTQ algorithm (per-group size 64, asymmetric scheme). Only linear operator weights quantized; activation remains FP16. Calibrated on 1,024 samples from neuralmagic/LLM_compression_calibration. Context length unknown; max tested 40,960 tokens in reasoning tasks. Serialized as safetensors/compressed-tensors. Evaluated on OpenLLM v1/v2, MGSM, AIME, GPQA, Math, and LiveCodeBench using lm-evaluation-harness and lighteval. Supports vLLM's OpenAI-compatible serving; requires transformers + vllm stack.
Run Qwen3-8B-quantized.w4a16 locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="RedHatAI/Qwen3-8B-quantized.w4a16")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 (verify in your environment):** INT4 quantization of 8.3B params ≈ ~4–5 GB VRAM (activations FP16 + overhead). Original FP16 baseline ≈ 16–17 GB. Requires NVIDIA/AMD GPU with CUDA/ROCm or CPU inference (slower). vLLM supports tensor parallelism; no multi-GPU numbers provided. Disk: ~5 GB (model weights + safetensors).
Model card does not explicitly document LoRA, QLoRA, or continued pretraining compatibility. GPTQ quantization can be sensitive to fine-tuning; llm-compressor supports quantization-aware training but requires recalibration. RedHat example uses oneshot quantization, suggesting post-hoc optimization. Recommend testing QLoRA or unfreezing non-quantized layers (lm_head, embeddings) if fine-tuning is necessary; full retraining will require re-quantization.
When to avoid it — and what to weigh
- Knowledge Benchmark-Heavy Use Cases — MMLU-Pro recovery only 74.4% and BBH only 83.1% indicate measurable degradation on broad knowledge tasks. Avoid if factual breadth is critical (e.g., trivia, general QA without domain fine-tuning).
- Maximum Context or Ultra-High Throughput Demands — Context length is unknown and max tested was 40,960 tokens. No batching, quantization overhead, or throughput (tokens/sec) benchmarks provided. Avoid if you need >100K context or sub-10ms latency guarantees.
- Tasks Requiring Fine-Grained Numerical Precision — W4A16 (4-bit weights, 16-bit activations) implies quantization noise. Models requiring calibrated probability estimates or numerical stability may experience drift.
- Proprietary/Restricted Deployment Without Legal Review — While Apache 2.0 is permissive, base model (Qwen/Qwen3-8B) licensing must be verified independently. RedHat's use of quantization does not override upstream model restrictions.
License & commercial use
Apache License 2.0 (Apache-2.0). This is an OSI-approved, permissive open-source license. Permits commercial use, modification, and distribution with attribution and liability disclaimer. No copyleft or viral clauses.
Apache 2.0 permits commercial use. However, the base model (Qwen/Qwen3-8B, owned by Alibaba) carries its own license terms (not provided in this evaluation data). Verify Qwen3-8B license independently before commercial deployment. RedHat's quantization derivative is Apache-licensed but does not override upstream restrictions. Recommend legal review if vendor lock-in or proprietary usage policies are concerns.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Moderate |
| Documentation | Strong |
| License clarity | Clear |
| Deployment complexity | Low |
| DEV.co fit | Strong |
| Assessment confidence | High |
Quantized LLMs retain base model safety properties and risks (e.g., hallucination, prompt injection, bias). INT4 quantization does not introduce novel attack surface vs. FP16 baseline. Model is not gated; weights are public. No adversarial robustness, jailbreak resistance, or content-filtering evaluation provided. Recommend standard LLM safety practices: input validation, output filtering, human review for high-stakes use. No known CVEs or exploit details disclosed.
Alternatives to consider
Qwen3-8B (unquantized)
Baseline model with higher accuracy (MMLU 71.95 vs. 69.74, MMLU-Pro 34.57 vs. 25.71) but 3–4× larger VRAM. Choose if hardware is available and latency/cost is not critical.
Llama 2 or 3 INT4 quantizations (Meta)
Alternative 7–8B models with comparable or better OpenLLM scores, broader ecosystem support, and clearer commercial licensing. May have more community fine-tuning resources.
Mistral 7B Instruct
Apache-2.0 licensed, slightly smaller, strong instruction-following (IFEval ~90%), and more extensive fine-tuning/merging community. Trade-off: less advanced reasoning on AIME/GPQA tasks.
Ship Qwen3-8B-quantized.w4a16 with senior software developers
Qwen3-8B-quantized offers industry-leading compression with minimal accuracy loss. Verify base model licensing, test on your hardware, and deploy via vLLM. Need custom fine-tuning or RAG integration? Contact our AI team.
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DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If Qwen3-8B-quantized.w4a16 is part of your open-source llms roadmap, our team can implement, customize, migrate, and maintain it.
Ready to Deploy Efficient LLMs?
Qwen3-8B-quantized offers industry-leading compression with minimal accuracy loss. Verify base model licensing, test on your hardware, and deploy via vLLM. Need custom fine-tuning or RAG integration? Contact our AI team.