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DeepSeek Hosting: Deploy R1, V2, V3, and Distill Models Efficiently

Name: DeepSeek Hosting: High-Performance GPU Inference
Brand: Cloud Clusters
Price: 269 USD
Availability: InStock
Rating: 4.6 (4866 reviews)

DeepSeek Hosting allows you to deploy, serve, and scale DeepSeek's large language models (LLMs)—such as DeepSeek R1, V2, V3, coder, and Distill variants—in high-performance GPU environments. It enables developers, researchers, and companies to run DeepSeek models efficiently via APIs or interactive applications.

Pre-installed DeepSeek-R1-70B LLM Hosting

Cloud Clusters 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.

CloudClusters currently does not offer pre-installed Llama LLM GPU servers. If you require high-end GPU servers for running LLMs, we recommend visiting our sister brand, Database Mart, which specializes in high-performance GPU server solutions. Database Mart provides powerful GPU configurations (e.g., A6000, A100, H100, RTX 5090, RTX Pro 6000), flexible deployment options, and reliable infrastructure to meet your needs.

Pre-installed DeepSeek-R1-32B LLM Hosting

Cloud Clusters 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.

Enterprise Dedicated GPU Server - RTX 4090

GPU: RTX 4090
CPU: 2 x 18-Core E5-2697v4
Memory: 256GB RAM
Disk: 240GB SSD+2TB NVMe+8TB SATA
Bandwidth: 100Mbps Unmetered
GPU Memory: 24 GB GDDR6X

IP: 1 Dedicated IPv4
Location: USA

1mo3mo12mo24mo

save 34% (Was $549.00)

$ 359.00/mo

DeepSeek Hosting with Ollama — GPU Recommendation

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	Tokens/s
deepseek-coder:1.3b	776MB	P1000 < T1000 < GTX1650 < GTX1660 < RTX2060	28.9-50.32
deepSeek-r1:1.5B	1.1GB	P1000 < T1000 < GTX1650 < GTX1660 < RTX2060	25.3-43.12
deepseek-coder:6.7b	3.8GB	T1000 < RTX3060 Ti < RTX4060 < A4000 < RTX5060 < V100	26.55-90.02
deepSeek-r1:7B	4.7GB	T1000 < RTX3060 Ti < RTX4060 < A4000 < RTX5060 < V100	26.70-87.10
deepSeek-r1:8B	5.2GB	T1000 < RTX3060 Ti < RTX4060 < A4000 < RTX5060 < V100	21.51-87.03
deepSeek-r1:14B	9.0GB	A4000 < A5000 < V100	30.2-48.63
deepseek-v2:16B	8.9GB	A4000 < A5000 < V100	22.89-69.16
deepSeek-r1:32B	20GB	A5000 < RTX4090 < A100-40gb < RTX5090	24.21-45.51
deepseek-coder:33b	19GB	A5000 < RTX4090 < A100-40gb < RTX5090	25.05-46.71
deepSeek-r1:70B	43GB	A40 < A6000 < 2A100-40gb < A100-80gb < H100 < 2RTX5090	13.65-27.03
deepseek-v2:236B	133GB	2A100-80gb < 2H100	--
deepSeek-r1:671B	404GB	6A100-80gb < 6H100	--
deepseek-v3:671B	404GB	6A100-80gb < 6H100	--

DeepSeek Hosting with vLLM + Hugging Face — GPU Recommendation

Hosting DeepSeek models using vLLM and Hugging Face is an efficient solution for high-performance inference, especially in production environments requiring low latency, multi-turn chat, and throughput optimization. vLLM is built for scalable and memory-efficient LLM serving, making it ideal for deploying large DeepSeek models with better GPU utilization.

Model Name	Size (16-bit Quantization)	Recommended GPU(s)	Concurrent Requests	Tokens/s
deepseek-ai/DeepSeek‑R1‑Distill‑Qwen‑1.5B	~3GB	T1000 < RTX3060 < RTX4060 < 2RTX3060 < 2RTX4060 < 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	2A4000 < 2V100 < A5000 < RTX4090	50	1450-2769
deepseek-ai/DeepSeek‑R1‑Distill‑Qwen‑14B	~28GB	3V100 < 2A5000 < A40 < A6000 < A100-40gb < 2*RTX4090	50	449-861
deepseek-ai/DeepSeek‑R1‑Distill‑Qwen‑32B	~65GB	A100-80gb < 2A100-40gb < 2A6000 < H100	50	577-1480
deepseek-ai/deepseek‑coder‑33b‑instruct	~66GB	A100-80gb < 2A100-40gb < 2A6000 < 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	--	--	--

✅ Explanation:

Recommended GPUs: From left to right, performance from low to high
Tokens/s: from benchmark data.

Choose The Best GPU Plans for DeepSeek R1/V2/V3/Distill Hosting

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.

What is DeepSeek Hosting?

DeepSeek Hosting enables users to serve, infer, or fine-tune DeepSeek models (like R1, V2, V3, or Distill variants) through either self-hosted environments or cloud-based APIs. DeepSeek Hosting Types include Self-Hosted Deployment and LLM-as-a-Service (LLMaaS).

✅ Self-hosted deployment means deploying on GPU servers (e.g. A100, 4090, H100) using inference engines such as vLLM, TGI, or Ollama, and users can control model files, batch processing, memory usage, and API logic

✅ LLM as a Service (LLMaaS) uses DeepSeek models through API providers, without deployment, just calling API.

LLM Benchmark Test Results for DeepSeek R1, V2, V3, and Distill Hosting

Each DeepSeek variant is tested under multiple deployment backends — including vLLM, Ollama, and Text Generation Inference (TGI) — across different GPU configurations (e.g., A100, RTX 4090, H100). The benchmark includes both full-precision and quantized (e.g., int4/ggml) versions of the models to simulate cost-effective hosting scenarios.

Ollama Benchmark for Deepseek

Each model—from the lightweight DeepSeek-R1 1.5B to the larger 7B, 14B, and 32B versions—is evaluated on popular GPUs such as RTX 3060, 3090, 4090, and A100. This helps users choose the best GPU for both performance and cost-effectiveness when running DeepSeek models with Ollama.

vLLM Benchmark for Deepseek

This benchmark evaluates the performance of DeepSeek models hosted on vLLM, covering models from the DeepSeek-R1, V2, V3, and Distill families, and using a variety of GPU types, from RTX 4090, A100, and H100, to multi-GPU configurations for large models such as DeepSeek-R1 32B+.

How to Deploy DeepSeek LLMs with Ollama/vLLM

Install and Run DeepSeek-R1 Locally with Ollama >

Ollama is a self-hosted AI solution to run open-source large language models, such as DeepSeek, Gemma, Llama, Mistral, and other LLMs locally or on your own infrastructure.

Install and Run DeepSeek-R1 Locally with vLLM v1 >

vLLM is an optimized framework designed for high-performance inference of Large Language Models (LLMs). It focuses on fast, cost-efficient, and scalable serving of LLMs.

What Does DeepSeek Hosting Stack Include?

Hosting DeepSeek models efficiently requires a robust software and hardware stack. A typical DeepSeek LLM hosting stack includes the following components:

Model Backend (Inference Engine)

vLLM — For high-throughput, low-latency serving

Ollama — Lightweight local inference with simple CLI/API

TGI — Hugging Face’s production-ready server

TensorRT-LLM / FasterTransformer — For optimized GPU serving

Model Format

FP16 / BF16 — Full precision, high accuracy

INT4 / GGUF — Quantized formats for faster, smaller deployments

Safetensors — Secure, fast-loading file format

Models usually pulled from Hugging Face Hub or local registry

Serving Infrastructure

Docker — For isolated, GPU-accelerated containers

CUDA (>=11.8) + cuDNN — Required for GPU inference

Python (>=3.10) — vLLM and Ollama runtime

FastAPI / Flask / gRPC — Optional API layer for integration

Nginx / Traefik — As reverse proxy for scaling and SSL

Hardware (GPU Servers)

High VRAM GPUs (A100, H100, 4090, 3090, etc.)

Multi-GPU or NVLink setups for models ≥32B

Dedicated Inference Nodes with 24GB+ VRAM recommended

Why DeepSeek Hosting Needs a Specialized Hardware + Software Stack

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.

DeepSeek Models Are Large and Compute-Intensive

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.

Powerful GPUs Are Required

GPU VRAM needs to be greater than 1.2 times the model size, e.g. RTX4090 (24gb vram) cannot infer LLMs larger than 20gb.

Efficient Inference Engines Are Critical

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.

Scalable Infrastructure Is a Must

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).

Self-hosted DeepSeek Hosting vs. DeepSeek LLM as a Service

In addition to GPU-based dedicated servers that host LLM models themselves, there are also many LLM API (Large Model as a Service) solutions on the market, which have become one of the mainstream ways to use models.

Feature / Aspect	🖥️ Self-hosted DeepSeek Hosting	☁️ DeepSeek LLM as a Service (LLMaaS)
Deployment Location	On your own GPU server (e.g., A100, 4090, H100)	Cloud-based, via API platforms
Model Control	✅ Full control over weights, versions, updates	❌ Limited — only exposed models via provider
Customization	Full — supports fine-tuning, LoRA, quantization	None or minimal customization allowed
Privacy & Data Security	✅ Data stays local — ideal for sensitive data	❌ Data sent to third-party cloud API
Performance Tuning	Full control: batch size, concurrency, caching	Predefined, limited tuning
Supported Models	Any DeepSeek model (R1, V2, V3, Distill, etc.)	Only what the provider offers
Inference Engine Options	vLLM, TGI, Ollama, llama.cpp, custom stacks	Hidden — provider chooses backend
Startup Time	Slower — requires setup and deployment	Instant — API ready to use
Scalability	Requires infrastructure management	Scales automatically with provider's backend
Cost Model	Higher upfront (hardware), lower at scale	Pay-per-call or token-based — predictable, but expensive at scale
Use Case Fit	Ideal for R&D, private deployment, large workloads	Best for prototypes, demos, or small-scale usage
Example Platforms	Dedicated GPU servers, on-premise clusters	DBM, Together.ai, OpenRouter.ai, Fireworks.ai, Groq

FAQs of DeepSeek R1, V2, V3, and Distill Models Hosting

What are the hardware requirements for hosting DeepSeek models?

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What inference engines are compatible with DeepSeek models?

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Where can I download DeepSeek models?

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Are quantized versions available?

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Can I fine-tune or LoRA-adapt DeepSeek models?

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What's the difference between R1, V2, V3, and Distill?

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Which model is best for lightweight deployment?

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How do I expose DeepSeek models as APIs?

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Can I host multiple DeepSeek models on the same GPU?

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Is DeepSeek hosting available as a managed service?

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