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Secure AI virtual data roomsDeepSeek-R1-0528-Qwen3-8B-GGUF
DeepSeek-R1-0528-Qwen3-8B-GGUF is a quantized 8B parameter language model created by Unsloth, distilled from DeepSeek's larger reasoning model. It is optimized for local inference via GGUF format and targets complex reasoning tasks (math, coding, logic). MIT-licensed, ungated, and actively maintained. Best suited for teams needing on-premise reasoning capabilities without cloud dependency.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | unsloth |
| Parameters | Unknown |
| Context window | Unknown |
| License | mit — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 74.2k |
| Likes | 427 |
| Last updated | 2025-06-16 |
| Source | unsloth/DeepSeek-R1-0528-Qwen3-8B-GGUF |
What DeepSeek-R1-0528-Qwen3-8B-GGUF is
This is a knowledge-distilled derivative of DeepSeek-R1-0528 (reasoning model) applied to Qwen3-8B base. Distributed as GGUF quantization by Unsloth, enabling CPU/GPU inference without vLLM/TGI. Uses a custom chat template with configurable reasoning depth (recommended 0.5–0.7 temperature, 0.95 top-p). Context length and exact parameter count unknown. Distilled chain-of-thought improves reasoning performance; evaluation shows +10.0% over vanilla Qwen3-8B on AIME 2024 (85.7% vs 76.0%).
Run DeepSeek-R1-0528-Qwen3-8B-GGUF locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="unsloth/DeepSeek-R1-0528-Qwen3-8B-GGUF")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: 6–8GB VRAM for Q4_K_XL quantization (inference). CPU-only possible but slower. Multi-GPU required for batching. Exact precision/quantization variants available in Unsloth collection; test on target hardware before production.
Unclear from card whether LoRA/QLoRA training is supported on this GGUF variant. Unsloth specializes in efficient fine-tuning; check Unsloth documentation for LoRA feasibility on quantized model. Base model (Qwen3-8B) is trainable, but downstream tuning of distilled weights requires clarification.
When to avoid it — and what to weigh
- Real-Time, Ultra-Low-Latency Applications — Model performs reasoning via extended token generation (23K tokens avg for AIME). Inference latency will be high even on optimized hardware; not suitable for sub-second response requirements.
- Simple Factual Recall or Retrieval Tasks — SimpleQA score 27.8% indicates weaker performance on straightforward lookup/memorization tasks. For keyword search or FAQ bots, simpler models or BM25 are more cost-effective.
- Multilingual or Vision Tasks — Model is text-only; no multimodal or broad multilingual training documented. Not suited for image understanding or non-English-heavy workloads.
- Unknown Context Length or Minimal Hardware Resources — Context length unspecified in DATA; 8B GGUF will require ~6–8GB VRAM (estimate). Clarify hardware capacity before deployment.
License & commercial use
MIT license. Permissive OSI-approved license allowing commercial use, modification, and distribution with minimal restrictions (attribution required, no warranty).
MIT permits commercial use without additional licensing. Base model (DeepSeek-R1-0528) and distillation (Qwen3-8B) license alignment should be verified: DATA confirms MIT on this Unsloth artifact, but downstream commercial terms depend on DeepSeek and Qwen license compliance. Recommended: review original DeepSeek-R1-0528 and Qwen3-8B license terms for full clarity.
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 | Low |
| DEV.co fit | Strong |
| Assessment confidence | High |
No security vulnerabilities disclosed. GGUF format is standard (no custom serialization risk). Considerations: (1) Local inference reduces data exfiltration risk vs. API-based alternatives; (2) Quantization may reduce model capacity to refuse harmful requests—test on sensitive use cases; (3) No mention of red-teaming or adversarial robustness; (4) Ensure Ollama/llama.cpp runtime is kept updated for security patches.
Alternatives to consider
Qwen3-8B (unquantized)
Same base model, no distillation. Simpler deployment if reasoning depth is not critical; lower reasoning benchmark scores but faster inference and simpler setup.
Phi-4-Reasoning-Plus-14B
14B alternative with AIME 2024 81.3%. Larger, but may fit if GPU memory allows; consider vs. 8B trade-off for reasoning quality.
Llama-2-70B or Mistral-Large (unquantized or other quants)
Larger open models with broader task coverage. Require more resources but may better generalize beyond reasoning benchmarks.
Ship DeepSeek-R1-0528-Qwen3-8B-GGUF with senior software developers
DeepSeek-R1-0528-Qwen3-8B-GGUF brings competitive reasoning (AIME 85.7%) to your infrastructure. Ungated, MIT-licensed, and optimized for Ollama or llama.cpp. Start with Unsloth's one-line setup or evaluate on your hardware today.
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DeepSeek-R1-0528-Qwen3-8B-GGUF FAQ
Can I use this model commercially?
What is the minimum GPU VRAM required?
How do I set up inference locally?
What is the context length of this model?
Custom software development, end to end
Standing up DeepSeek-R1-0528-Qwen3-8B-GGUF properly takes more than a deploy. As a software development agency, DEV.co handles the open-source llms architecture, the web development on top, and the operational work underneath.
Deploy Reasoning Locally—No API Dependencies
DeepSeek-R1-0528-Qwen3-8B-GGUF brings competitive reasoning (AIME 85.7%) to your infrastructure. Ungated, MIT-licensed, and optimized for Ollama or llama.cpp. Start with Unsloth's one-line setup or evaluate on your hardware today.