DeepSeek-R1-0528-Qwen3-8B
DeepSeek-R1-0528-Qwen3-8B is an 8.2B parameter open-source LLM distilled from DeepSeek-R1's reasoning capabilities into Qwen3's base model. It achieves strong performance on mathematics and coding benchmarks (AIME 2024: 86.0%, LiveCodeBench: 60.5%) while maintaining a compact footprint suitable for local deployment. MIT-licensed, ungated, and available on HuggingFace.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | deepseek-ai |
| Parameters | 8.2B |
| Context window | Unknown |
| License | mit — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 1.6M |
| Likes | 1.1k |
| Last updated | 2025-05-29 |
| Source | deepseek-ai/DeepSeek-R1-0528-Qwen3-8B |
What DeepSeek-R1-0528-Qwen3-8B is
Distilled reasoning model (Qwen3-8B base + DeepSeek-R1-0528 chain-of-thought post-training). Uses DeepSeek-R1-0528 tokenizer. Supports system prompts and reasoning patterns via <think> tokens. Max generation: 64K tokens. Evaluated at temperature 0.6, top-p 0.95. 1.56M downloads, last updated 2025-05-29.
Run DeepSeek-R1-0528-Qwen3-8B locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="deepseek-ai/DeepSeek-R1-0528-Qwen3-8B")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: 16–24GB VRAM for fp16 full inference; 8–12GB with 8-bit quantization; 4–6GB with 4-bit quantization (e.g., via llama.cpp, GPTQ). Exact requirements depend on sequence length and batch size. Verify with model card's local-run guidance or benchmarks before production deployment.
LoRA/QLoRA feasibility is plausible given 8B size and standard Qwen3 architecture, but model card does not explicitly document fine-tuning instructions. Uses DeepSeek-R1-0528 tokenizer (not original Qwen3), requiring careful config management. No official LoRA weights or adaptation guides provided in excerpt. Requires review of full repo.
When to avoid it — and what to weigh
- Real-Time Latency-Critical Applications — Reasoning models generate 20K+ tokens per complex query (average 23K per AIME question). Inference speed unknown but likely slower than non-reasoning baselines. Not suitable for sub-second response SLAs.
- Multi-Modal or Long-Context Requirements — Text-generation only; no vision capability. Context length unknown; no evidence of extended context window optimization beyond 64K max generation length.
- Hallucination-Sensitive Production Systems — Model card notes 'reduced hallucination rate' vs. prior version but provides no metrics. SimpleQA score (27.8%) indicates factual accuracy gaps. Unsuitable for high-stakes factual retrieval without verification mechanisms.
- Tasks Requiring Recent Knowledge or Web Access — Model evaluation frozen at 2025-05-29. No built-in web search or knowledge update mechanism documented. Card references web search prompts but does not claim integrated retrieval capability.
License & commercial use
MIT license. OSI-approved, permissive license allowing commercial use, modification, and redistribution with attribution. No copyleft restrictions.
MIT license explicitly permits commercial use, including closed-source applications and commercial services. Requires preservation of license notice and copyright attribution. No gating mechanism enforced. Commercial deployment is legally clear under MIT terms; however, practical deployment considerations (cost of inference, performance on specific tasks) remain independent of licensing.
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 | Moderate |
| DEV.co fit | Good |
| Assessment confidence | High |
Card does not disclose security audits, red-teaming results, or adversarial robustness testing. Reasoning models with 20K+ token generations may amplify prompt injection or jailbreak effectiveness. No data provenance, synthetic data ratio, or filtering methodology documented. Use in sensitive contexts (e.g., medical, financial advice) requires independent safety evaluation. Self-hosted deployment removes third-party monitoring; apply standard LLM safety practices (input validation, output filtering, usage monitoring).
Alternatives to consider
Qwen3-8B (base, non-reasoning)
Lower inference latency; comparable general-knowledge performance (AIME 2024: 76% vs. 86%). Choose if reasoning overhead is unaffordable.
Phi-4-Reasoning-Plus-14B
Slightly smaller reasoning model (14B); AIME 2024: 81.3%, LiveCodeBench capable. Larger parameter count may offset inference cost savings.
Gemini 2.5 Flash Thinking (closed-source API)
Comparable or better benchmark performance; managed inference. Trade-off: no self-hosting, commercial API costs, vendor lock-in, but potentially lower latency and operational burden.
Ship DeepSeek-R1-0528-Qwen3-8B with senior software developers
DeepSeek-R1-0528-Qwen3-8B is MIT-licensed and immediately available on HuggingFace. Estimate your hardware needs, test quantization strategies, and check the GitHub repo for local-run examples. Devco can help integrate custom-tuning and optimized serving for your use case.
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DeepSeek-R1-0528-Qwen3-8B FAQ
Can I use this model commercially in a closed-source product?
What VRAM do I need to run this locally?
Why does the model generate so many tokens for math problems?
Can I fine-tune this model on my own data?
Software development & web development with DEV.co
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 DeepSeek-R1-0528-Qwen3-8B is part of your open-source llms roadmap, our team can implement, customize, migrate, and maintain it.
Ready to Deploy a Reasoning-Capable 8B Model?
DeepSeek-R1-0528-Qwen3-8B is MIT-licensed and immediately available on HuggingFace. Estimate your hardware needs, test quantization strategies, and check the GitHub repo for local-run examples. Devco can help integrate custom-tuning and optimized serving for your use case.