Qwen3-Coder-30B-A3B-Instruct-FP8
Qwen3-Coder-30B-A3B-Instruct-FP8 is a 30.5-billion-parameter mixture-of-experts coding LLM from Qwen (Alibaba), optimized for code generation, agentic coding tasks, and long-context repository understanding. It supports 256K tokens natively (extendable to 1M), uses FP8 quantization for efficient inference, and is licensed under Apache 2.0 with no gating restrictions. Suitable for self-hosted deployment on moderate GPU infrastructure.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | Qwen |
| Parameters | 30.5B |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 1.5M |
| Likes | 187 |
| Last updated | 2025-12-03 |
| Source | Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8 |
What Qwen3-Coder-30B-A3B-Instruct-FP8 is
Mixture-of-experts (MoE) causal language model with 30.5B total parameters and 3.3B activated per token. Architecture: 48 layers, 32 query heads + 4 key-value heads (GQA), 128 experts with 8 activated. Native context: 262,144 tokens; extendable via Yarn. FP8 fine-grained quantization (block size 128). Trained on code and instruction-following tasks. Does not support thinking/CoT mode. Requires transformers ≥4.51.0. Known distributed-inference FP8 issues in transformers; stability improved in sglang/vLLM.
Run Qwen3-Coder-30B-A3B-Instruct-FP8 locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8")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 (unverified from card): FP8 quantization ≈ 60–80GB VRAM for full inference on single GPU. Recommended: 2× H100 (80GB) or equivalent for production throughput. Batch inference benefits from tensor parallelism. OOM mitigation: reduce max_new_tokens (card suggests 65,536 max) and context length to 32,768.
Unknown. Card does not specify LoRA/QLoRA compatibility, PEFT support, or fine-tuning data requirements. Recommend consulting Qwen documentation or GitHub repo (arxiv:2505.09388, https://github.com/QwenLM/Qwen3-Coder) for guidance. FP8 quantization may complicate gradient updates; bfloat16 base model may be required for efficient fine-tuning.
When to avoid it — and what to weigh
- Strict Real-Time Inference SLA — 30B MoE model requires significant GPU memory and compute. First-token latency and throughput vary by hardware; not suitable for sub-100ms response targets without expert infrastructure tuning.
- Limited GPU Memory (<40GB VRAM) — FP8 quantization helps, but full model inference on consumer GPUs (24GB) requires aggressive context truncation and careful batching. OOM risks without careful configuration.
- Reasoning/Mathematical Proof Tasks — Model does not support thinking/CoT mode and is optimized for code, not symbolic reasoning or formal mathematics. General-purpose reasoning capabilities unknown.
- Mission-Critical Security Without Audit — No third-party security audit or red-team data provided. Code generation can introduce vulnerabilities; output review and validation required for production systems.
License & commercial use
Apache 2.0 (SPDX ID: apache-2.0). Permissive OSI-approved license permitting commercial use, modification, and redistribution with attribution and liability disclaimer.
Apache 2.0 explicitly permits commercial use, including self-hosted deployment and integration into proprietary systems, without licensing fees or vendor approval. No gating. Responsibility lies with user to ensure output (generated code) complies with applicable laws and does not infringe third-party IP. Generated code may inherit licensing obligations depending on training data provenance (not disclosed).
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 |
Code-generation models inherit risks of training data and can reproduce memorized code patterns, introduce subtle vulnerabilities, or suggest unsafe APIs. No red-team or adversarial robustness data provided. Recommend: (1) validate generated code against security linters and SAST tools; (2) conduct internal review before production deployment; (3) isolate inference environment; (4) monitor for prompt injection attacks if exposed via chat interface. FP8 quantization does not materially alter attack surface relative to bfloat16 base model.
Alternatives to consider
Qwen3-Coder-32B (bfloat16 variant)
Same architecture without quantization; higher precision for critical applications; slightly higher VRAM cost; better support for distributed fine-tuning. Use if inference latency/accuracy trade-off of FP8 is unacceptable.
DeepSeek-Coder-V2 (or latest variant)
Alternative open-source code LLM; may offer different performance/cost trade-offs. Requires license review and capability comparison on coding benchmarks.
Meta Llama 3.1 405B or smaller variants
Larger general-purpose LLM with strong coding performance; different licensing (Llama 2/3 community license requires review for commercial use); no native code-specific optimizations; higher inference cost.
Ship Qwen3-Coder-30B-A3B-Instruct-FP8 with senior software developers
Qwen3-Coder-30B-FP8 is production-ready for self-hosted, commercial-use code AI. Evaluate on your codebase with transformers or vLLM, configure for your GPU tier, and integrate into CI/CD or IDE workflows. Review the arXiv technical report and GitHub repo for benchmarks and deployment best practices.
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Qwen3-Coder-30B-A3B-Instruct-FP8 FAQ
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Does it support fine-tuning or LoRA adaptation?
Software developers & web developers for hire
DEV.co helps companies turn open-source tools like Qwen3-Coder-30B-A3B-Instruct-FP8 into production software. Our software development services cover the full lifecycle — architecture, web development, integration, and maintenance — delivered by software developers and web developers who ship. Engage our software development agency to implement or customize it for your open-source llms stack.
Deploy Qwen3-Coder Privately for Code-Generation Workflows
Qwen3-Coder-30B-FP8 is production-ready for self-hosted, commercial-use code AI. Evaluate on your codebase with transformers or vLLM, configure for your GPU tier, and integrate into CI/CD or IDE workflows. Review the arXiv technical report and GitHub repo for benchmarks and deployment best practices.