Open-Weight LLM · Private & Custom AI
DeepSeek-R1-0528-Qwen3-8B
8B reasoning model distilled from DeepSeek-R1-0528: reasoning-grade math/code performance at small-model cost, deployable privately on modest hardware.
DeepSeek-R1-0528-Qwen3-8B is an 8.2B-parameter open-weight LLM that combines Qwen3's base architecture with chain-of-thought reasoning distilled from DeepSeek's flagship R1 model. It trades inference speed for stronger reasoning on math, coding, and logic tasks—making it viable for companies that need private, lightweight reasoning agents without the 70B+ footprint.
Model facts
Private deployment
Run DeepSeek-R1-0528-Qwen3-8B in your own environment
At 8.2B parameters, this model runs on a single A100 (40GB) or two RTX 4090s in quantized form, keeping all reasoning and outputs within your infrastructure. No data leaves your environment; you control inference, fine-tuning, and integration with internal systems. Deployment via vLLM, TGI, or Ollama is standard. Trade-off: reasoning-heavy workloads will have higher latency than fast inference models.
Operational AI use cases
Internal technical support automation
Route troubleshooting tickets to this model for multi-step reasoning on code bugs, infrastructure issues, or product setup questions. Reasoning chains reduce hallucinations vs. vanilla 8B models; results stay in your ticketing system without cloud dependency.
Financial reconciliation & validation workflows
Use for multi-step verification of expense reports, invoice discrepancies, or compliance-related logic. The model's improved math reasoning handles percentage calcs, formula checks, and rule-based decisions; logs and chains are retained on-prem for audit trails.
Internal documentation Q&A with reasoning
Build a private RAG agent over internal wikis, policies, and architecture docs. Reason through multi-part questions (e.g., 'which team handles X and what's their approval threshold?'); avoids exposing company docs to external APIs.
Custom AI
As a base for custom AI
Strong foundation for building custom domain-specific reasoning systems. Distilled reasoning behavior makes it viable for fine-tuning on proprietary tasks (contract analysis, domain-specific logic, technical diagnostics) without 70B model costs. Chain-of-thought weights are learnable; companies can layer domain data to create specialized reasoning agents.
In the operating system
Where it fits
Sits in the **reasoning/agent layer** of an AI ops stack. Too slow for real-time chat completions, ideal for batch reasoning tasks, internal agents, and decision workflows. Pair with a fast 3-7B model for chat/triage, use this for high-stakes reasoning steps.
Data control & security
Private deployment ensures all data, intermediate reasoning, and outputs stay within your infrastructure. No model telemetry or inference logs sent externally. However, the model itself has no built-in encryption, PII filtering, or compliance attestation—security depends on your network/storage controls and how you integrate it. Suitable for moderately sensitive internal work; highly regulated use requires additional governance layers.
Hardware footprint
**Estimate—verify with your setup:** FP32: ~33GB VRAM | BF16: ~17GB | INT8: ~9GB | INT4: ~5GB. On RTX 4090 (24GB), run INT8 or INT4 with moderate batch sizes. A100 40GB: comfortable at BF16 with batching. Inference latency increases with reasoning depth (avg 23K tokens per query in AIME tests).
Integration
Supports OpenAI-compatible APIs via vLLM. Requires sharing tokenizer config from DeepSeek repo (not Qwen3 base). Compatible with LlamaIndex, LangChain, and custom Python inference loops. Expect 5–50s latency per request depending on input length and hardware; design workflows around batch processing or low-concurrency, high-tolerance scenarios. File upload templates and system prompts documented in model card.
When it's not the right fit
- —You need sub-second response times for chat or real-time user-facing applications—reasoning overhead makes latency 5–50s typical.
- —Your tasks require up-to-date world knowledge or factual recall on recent events; model has fixed knowledge cutoff and SimpleQA score (27.8%) reflects this weakness.
- —You have severe token/cost constraints and can't justify slow inference; a smaller, faster model (3B) may be better if reasoning isn't critical.
- —You need proven compliance certifications or regulatory sign-off; open-source models lack formal security/privacy audits required by some orgs.
Alternatives to consider
Phi-4-Reasoning-Plus-14B
14B reasoning model from Microsoft; comparable AIME performance (81.3% vs. 86.0% here) but larger footprint. Fewer downloads/community support than DeepSeek.
Qwen3-32B
Sibling without reasoning distillation; faster inference, lower reasoning quality. Chose this if latency beats accuracy and you're doing lighter tasks.
Gemini-2.5-Flash-Thinking (quantized OSS proxy, if available)
Closed-source, but benchmarks comparable. No private self-hosted option; included for context on reasoning-model tier, not recommendation for LLM.co use case.
Related open models
FAQ
Can we run this on-prem without internet connectivity?
Yes. Download weights (~16–33GB depending on precision), tokenizer config, and any inference engine (vLLM, TGI) to your private infrastructure. No cloud callbacks or license checks. Once loaded, inference is fully local.
Is this model licensed for commercial use?
License is MIT, which permits commercial use, redistribution, and modification. No restrictions on building products or services with it. Verify compliance with your legal team if you embed it in customer-facing tools.
How does reasoning latency compare to non-reasoning models?
Expect 5–50s per request (vs. <1s for a fast 8B). Reasoning uses ~23K tokens on math problems; this isn't suitable for low-latency chat. Design workflows around batch jobs, background agents, or user scenarios where 10–30s wait is acceptable.
Can we fine-tune this on our internal data?
Yes. The model is trainable and weights are accessible. Full fine-tuning on domain data or continued training with your proprietary reasoning examples is possible. Start with LoRA or QLoRA to reduce cost; full training requires 40GB+ VRAM setups.
Build reasoning AI your way.
Deploy DeepSeek-R1-0528-Qwen3-8B in your private ops stack. LLM.co helps you integrate, fine-tune, and scale reasoning agents on infrastructure you control. Talk to us about custom AI for your workflows.