DeepSeek-R1-0528-Qwen3-8B
deepseek-aimittext

DeepSeek-R1-0528-Qwen3-8B

Updated Jun 29, 2026
Parameters
8.2B
Context
131,072
License
mit
Updated
Jun 29, 2026

Will it run on your hardware?

Pick your GPU memory - see which quantizations fit, and the cheapest card for the rest

Runs on your 24 GB - best at FP16
4 of 4 quantizations fit DeepSeek-R1-0528-Qwen3-8B with real runtime overhead.
Q4_K_M
5 GB
Q5_K_M
6 GB
Q8_0
9 GB
FP16
16 GB
fits tight too big

Need an exact figure for your context length? Use the VRAM calculator.

Run it locally

Copy-paste - running in under a minute

vLLMOpenAI-compatible API
vllm serve deepseek-ai/DeepSeek-R1-0528-Qwen3-8B

New to this? Start with Ollama · serve to many users with vLLM.

Deep dive

Notes, sources, and the full write-up

DeepSeek-R1-0528-Qwen3-8B is a 8.2B-parameter mit model from deepseek-ai. At Q4_K_M it needs roughly 5 GB of VRAM, placing it in the 8–12 GB GPU hardware tier.

Popularity

DeepSeek-R1-0528-Qwen3-8B has 895,476 downloads in the last month on HuggingFace and 1,079 likes.

Frequently asked

Quick answers to common questions

How much VRAM does DeepSeek-R1-0528-Qwen3-8B need?

DeepSeek-R1-0528-Qwen3-8B with 8.2B parameters needs approximately 5 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.

Is DeepSeek-R1-0528-Qwen3-8B better than other deepseek-ai models?

DeepSeek-R1-0528-Qwen3-8B has 8.2B parameters with 131,072 context - a strong choice for general use.

What license is DeepSeek-R1-0528-Qwen3-8B under?

DeepSeek-R1-0528-Qwen3-8B is released under the mit license, making it suitable for most commercial and personal projects.

What hardware runs DeepSeek-R1-0528-Qwen3-8B well?

With 8.2B parameters, DeepSeek-R1-0528-Qwen3-8B requires adequate VRAM. High-end GPUs like the RTX 4090 (24GB), RTX 5090 (32GB), or Mac Studio with unified memory are good options. Check our hardware directory for specific recommendations.

What is the best quantization for DeepSeek-R1-0528-Qwen3-8B?

Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Q5_K_M (~6 GB) is an option if you have spare VRAM. Use our VRAM calculator to compare.

How long can DeepSeek-R1-0528-Qwen3-8B's context window handle?

DeepSeek-R1-0528-Qwen3-8B supports a 131,072-token context window - enough for very long documents, codebases, or multi-turn conversations. Real-world usable context may vary by implementation.

What models compete with DeepSeek-R1-0528-Qwen3-8B?

DeepSeek-R1-0528-Qwen3-8B competes with other models in its class. Browse our model directory for comparisons, benchmarks, and community reviews to find the best fit.

Nearby options

Similar models and compatible hardware by spec

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