DeepSeek-R1-0528
DeepSeek-R1-0528 is a 685B parameter open-source reasoning model licensed under MIT. It demonstrates strong performance on math, code, and general reasoning benchmarks, with notable improvements over its predecessor (e.g., AIME 2025: 87.5% vs. 70%). The model uses chain-of-thought reasoning, requires significant compute resources, and is available for self-hosting without gating restrictions.
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 | 684.5B |
| Context window | Unknown |
| License | mit — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 1.8M |
| Likes | 2.5k |
| Last updated | 2025-05-29 |
| Source | deepseek-ai/DeepSeek-R1-0528 |
What DeepSeek-R1-0528 is
A 685B-parameter dense transformer optimized for extended reasoning tasks. Supports 64K token generation, custom code execution, and function calling. Distributed via HuggingFace in safetensors format with FP8 quantization available. Requires external hosting infrastructure; no context length officially stated. Last updated May 29, 2025.
Run DeepSeek-R1-0528 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")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: 685B parameters in FP8 quantization ≈ 680–750 GB VRAM. Practical self-hosting requires 8× A100 80GB or equivalent distributed setup (e.g., 16× RTX 6000 Ada). Inference latency unknown; 23K average reasoning tokens per query implies multi-minute response times on typical hardware. FP8 quantization available; precise memory/compute requirements require benchmarking on target infrastructure.
Model card does not document LoRA or QLoRA fine-tuning pathways. 685B parameter count suggests full fine-tuning is impractical; parameter-efficient tuning (LoRA) would require custom implementation. Chain-of-thought distillation demonstrated (Qwen3-8B variant), indicating reasoning patterns can be transferred to smaller models, but native fine-tuning guidance is absent. Requires external resources or community implementations.
When to avoid it — and what to weigh
- Real-Time Inference on Commodity Hardware — 685B parameters exceed consumer-grade GPU memory (requiring A100 80GB clusters or distributed serving). Not suitable for edge deployment or latency-critical applications without quantization and model distillation.
- Fact Recall and Current-Event Queries — SimpleQA benchmark shows 27.8% accuracy. Model performs poorly on factual recall requiring up-to-date knowledge, preferring reasoning over retrieval. Requires RAG or external knowledge integration.
- Proprietary IP Protection via Model Weights — MIT license and public weights make model weights transparent. Unsuitable if confidential logic or training data must remain opaque; consider API-only closed models or custom fine-tuning in private infrastructure.
- Low-Cost or Energy-Constrained Deployments — Model scale and reasoning token overhead (avg. 23K tokens per AIME query) result in high inference cost and latency. Consider distilled variants like DeepSeek-R1-0528-Qwen3-8B (8B parameters) for resource-constrained use cases.
License & commercial use
MIT license (permissive, OSI-approved). Permits commercial use, modification, and distribution with attribution. No explicit proprietary restrictions stated in card. Model weights and code publicly available on HuggingFace without gating.
MIT license explicitly allows commercial use without additional licensing fees or restrictions. No gating or access controls. However, deployment costs (compute, inference infrastructure) are the primary commercial constraint, not licensing. Verify compliance with upstream dependencies (tokenizer, frameworks). Model card does not detail liability, warranty, or indemnification; standard MIT terms apply: use at own risk, no warranties provided.
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 | High |
| DEV.co fit | Good |
| Assessment confidence | High |
Public model weights and code allow adversarial input analysis and prompt injection testing; no security audit results disclosed. Chain-of-thought verbosity (23K avg. tokens) may expose intermediate reasoning, increasing prompt leakage risk. FP8 quantization lowers numerical precision, potential impact on cryptographic or precision-critical logic unknown. No stated content filtering or safety guidelines; depends on downstream system prompt and guardrails. Standard risks: model memorization of training data (unknown composition), fairness concerns in multilingual/cross-cultural reasoning (evaluation focuses on English/math benchmarks). Recommend threat modeling for sensitive deployment contexts.
Alternatives to consider
DeepSeek-R1-0528-Qwen3-8B
8B-parameter distilled variant achieving competitive math performance (AIME 2024: 86.0%) with 85× lower parameter count. Suitable for resource-constrained production; trade-off: lower absolute performance on complex reasoning.
OpenAI o3-mini (API)
Closed-source, managed inference eliminating deployment complexity. Comparable reasoning performance (AIME 2025: 76.7%); trading open-source control and cost predictability for availability and support.
Qwen3-235B-A22B
Dense baseline with SOTA general reasoning (AIME 2024: 85.7%). Lighter than DeepSeek-R1-0528 (235B vs. 685B parameters); lacks explicit chain-of-thought optimization, potentially lower inference cost.
Ship DeepSeek-R1-0528 with senior software developers
Evaluate this model for your reasoning-heavy workloads. Contact our engineering team to architect distributed inference, quantization strategies, and fine-tuning pipelines tailored to your infrastructure and use case.
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DeepSeek-R1-0528 FAQ
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Software development & web development with DEV.co
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Deploy DeepSeek-R1-0528 at Scale
Evaluate this model for your reasoning-heavy workloads. Contact our engineering team to architect distributed inference, quantization strategies, and fine-tuning pipelines tailored to your infrastructure and use case.