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B2BHOSTINGCLUB offers best budget GPU servers for DeepSeek-R1 LLMs. You'll get pre-installed Open WebUI + Ollama, it is a popluar way to run DeepSeek-R1 models.
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B2BHOSTINGCLUB offers best budget GPU servers for DeepSeek-R1 LLMs. You'll get pre-installed Open WebUI + Ollama + DeepSeek-R1-32B, it is a popluar way to self-hosted LLM models.
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Deploying DeepSeek models using Ollama is a flexible and developer-friendly way to run powerful LLMs locally or on servers. However, choosing the right GPU is critical to ensure smooth performance and fast inference, especially as model sizes scale from lightweight 1.5B to massive 70B+ parameters.
|
Model Name
|
Size (4-bit Quantization)
|
Recommended GPUs
|
Concurrent Requests
|
Tokens/s
|
|---|---|---|---|---|
| deepseek-ai/DeepSeek‑R1‑Distill‑Qwen‑1.5B | ~3GB | T1000 < RTX3060 < RTX4060 < 2*RTX3060 < 2*RTX4060 < A4000 < V100 | 50 | 1500-5000 |
| deepseek-ai/deepseek‑coder‑6.7b‑instruct | ~13.4GB | A5000 < RTX4090 | 50 | 1375-4120 |
| deepseek-ai/Janus‑Pro‑7B | ~14GB | A5000 < RTX4090 | 50 | 1333-4009 |
| deepseek-ai/DeepSeek‑R1‑Distill‑Qwen‑7B | ~14GB | A5000 < RTX4090 | 50 | 1333-4009 |
| deepseek-ai/DeepSeek‑R1‑Distill‑Llama‑8B | ~16GB | 2*A4000 < 2*V100 < A5000 < RTX4090 | 50 | 1450-2769 |
| deepseek-ai/DeepSeek‑R1‑Distill‑Qwen‑14B | ~28GB | 3*V100 < 2*A5000 < A40 < A6000 < A100-40gb < 2*RTX4090 | 50 | 449-861 |
| deepseek-ai/DeepSeek‑R1‑Distill‑Qwen‑32B | ~65GB | A100-80gb < 2*A100-40gb < 2*A6000 < H100 | 50 | 577-1480 |
| deepseek-ai/deepseek‑coder‑33b‑instruct | ~66GB | A100-80gb < 2*A100-40gb < 2*A6000 < H100 | 50 | 570-1470 |
| deepseek-ai/DeepSeek‑R1‑Distill‑Llama‑70B | ~135GB | 4*A6000 | 50 | 466 |
| deepseek-ai/DeepSeek‑Prover‑V2‑671B | ~1350GB | -- | -- | -- |
| deepseek-ai/DeepSeek‑V3 | ~1350GB | -- | -- | -- |
| deepseek-ai/DeepSeek‑R1 | ~1350GB | -- | -- | -- |
| deepseek-ai/DeepSeek‑R1‑0528 | ~1350GB | -- | -- | -- |
| deepseek-ai/DeepSeek‑V3‑0324 | ~1350GB | -- | -- | -- |
If the pre-installed DeepSeek product does not meet your needs, you can rent a server, install and manage any model by yourself—everything under your control.
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DeepSeek models are state-of-the-art large language models (LLMs) designed for high-performance reasoning, multi-turn conversations, and code generation. Hosting them effectively requires a specialized combination of hardware and software due to their size, complexity, and compute demands.
Model sizes range from 1.5B to 70B+ parameters, with FP16 memory footprints reaching up to 100+ GB. Larger models like DeepSeek-R1-32B or 236B require multi-GPU setups or high-end GPUs with large VRAM.
GPU VRAM needs to be greater than 1.2 times the model size, e.g. RTX4090 (24gb vram) cannot infer LLMs larger than 20gb.
Serving DeepSeek models efficiently requires optimized backends, for example: vLLM is best for high throughput and concurrent request processing. TGI is scalable and supports Hugging Face natively. Ollama is great for local testing and development environments, and TensorRT-LLM/GGML is used for advanced low-level optimizations.
For production or research workloads, DeepSeek hosting requires containerization (Docker, NVIDIA runtime), orchestration (Kubernetes, Helm), API gateway and load balancing (Nginx, Traefik), monitoring and autoscaling (Prometheus, Grafana).
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