Qwen3-30B-A3B-FP8-Dynamic
Qwen3-30B-A3B-FP8-Dynamic is a quantized version of Alibaba's Qwen3 30B model, optimized for memory and speed. Both weights and activations are compressed to 8-bit floating point (FP8), cutting GPU memory needs by ~50% and doubling compute throughput. It retains 99.9% accuracy on standard benchmarks. The model is open-source under Apache 2.0, unfiltered, and ready to deploy with vLLM.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | BCCard |
| Parameters | 30.6B |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 49.9k |
| Likes | 0 |
| Last updated | 2025-06-20 |
| Source | BCCard/Qwen3-30B-A3B-FP8-Dynamic |
What Qwen3-30B-A3B-FP8-Dynamic is
FP8 quantized derivative of Qwen/Qwen3-30B-A3B (Mixture-of-Experts architecture). Quantization applied symmetrically to weights (static, per-channel) and activations (dynamic, per-token) using llm-compressor, excluding the language model head. Released 05/05/2025. Evaluated on OpenLLM v1 benchmarks via lm-evaluation-harness; reports 99.9% accuracy recovery versus the base model (average across MMLU, ARC, GSM-8K, Hellaswag, Winogrande, TruthfulQA). Context length unknown. 49,935 downloads, no community likes recorded.
Run Qwen3-30B-A3B-FP8-Dynamic locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="BCCard/Qwen3-30B-A3B-FP8-Dynamic")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: ~15–16 GB VRAM (FP8, 30.5B params, ~8 bytes per param after quantization, plus KV cache and overhead). Base FP16 would require ~60 GB. Single GPU deployment viable per model card (tensor_parallel_size=1 example in vLLM code). Exact context length and batch-size impact unknown; requires hands-on testing.
Model card explicitly lists fine-tuning as an intended use case ('subject matter experts via fine-tuning'). Quantized weights may complicate gradient computation; verify llm-compressor or vLLM integration with QLoRA. Standard HuggingFace transformers fine-tuning workflow possible but may require custom quantization-aware training. No LoRA-specific guidance in card.
When to avoid it — and what to weigh
- Ultra-low quantization targets (INT4, INT8 activations) — This model is FP8; further quantization not addressed in card. Requires separate evaluation.
- Streaming or real-time token generation with strict latency SLAs — No streaming latency benchmarks provided. vLLM supports batching; single-token SLAs unknown.
- Critical applications requiring formal security audits — No security evaluation, adversarial robustness tests, or audit trail documented. Model is unfiltered; output filtering/safety rails not addressed.
- Offline/edge deployment without GPU infrastructure — 30B parameter model with FP8 still requires substantial compute. llama.cpp or CPU inference not mentioned; assume GPU-only.
License & commercial use
Apache License 2.0 (apache-2.0). Permissive OSI-approved license. Derivative work of Qwen/Qwen3-30B-A3B (no license restriction stated on base model in provided data). Model is not gated.
Apache 2.0 permits commercial use, redistribution, and modification under standard terms (attribution, license notice). No explicit commercial restrictions stated in card. Verify compliance with Qwen3-30B-A3B base model license if not publicly stated. Recommended: review Qwen/Qwen3-30B-A3B license explicitly before production deployment.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Low |
| DEV.co fit | Strong |
| Assessment confidence | High |
No security audit, adversarial robustness evaluation, or prompt injection testing documented. Model is unfiltered; no safety guardrails or output filtering mentioned. Quantization to FP8 may introduce subtle numeric artifacts; impact on safety and alignment unknown. Data sourcing and training details unknown. Recommend isolated testing and output validation before production exposure.
Alternatives to consider
Qwen/Qwen3-30B-A3B (base model, unquantized)
Full precision (FP16 or BF16) if memory/cost not primary constraint and max accuracy needed; trade-off is ~2–4× VRAM and compute.
vLLM-quantized Llama-3.1-70B or Mixtral-8x22B
Larger alternatives with similar FP8 optimization; better evaluated on common benchmarks if scale/capability trumps memory savings.
Qwen2.5-32B or Qwen2-32B (unquantized or AWQ-quantized variants)
Earlier Qwen generation; more mature ecosystem, higher HuggingFace adoption/scrutiny, and established production deployments if Qwen3 novelty risk is concern.
Ship Qwen3-30B-A3B-FP8-Dynamic with senior software developers
Use Qwen3-30B-A3B-FP8-Dynamic for memory-efficient multilingual inference, fine-tuning, and reasoning tasks. Start with vLLM and test on your infrastructure—evaluate latency and accuracy for your use case.
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Qwen3-30B-A3B-FP8-Dynamic FAQ
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Deploy a Quantized Qwen3 Model Today
Use Qwen3-30B-A3B-FP8-Dynamic for memory-efficient multilingual inference, fine-tuning, and reasoning tasks. Start with vLLM and test on your infrastructure—evaluate latency and accuracy for your use case.