DeepSeek Hosting

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

 

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.

 

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 NameSize (4 bit Quantization)Recommended GPUToken/s
deepseek-coder:1.3b776MBP1000 < T1000 < GTX1650 < GTX1660 < RTX206028.9-50.32
deepSeek-r1:1.5B1.1GBP1000 < T1000 < GTX1650 < GTX1660 < RTX206025.3-43.12
deepseek-coder:6.7b3.8GBT1000 < RTX3060 Ti < RTX4060 < A4000 < RTX5060 < V10026.55-90.02
deepSeek-r1:7B4.7GBT1000 < RTX3060 Ti < RTX4060 < A4000 < RTX5060 < V10026.70-87.10
deepSeek-r1:8B5.2GBT1000 < RTX3060 Ti < RTX4060 < A4000 < RTX5060 < V10021.51-87.03
deepSeek-r1:14B9.0GBA4000 < A5000 < V10030.2-48.63
deepseek-v2:16B8.9GBA4000 < A5000 < V10022.89-69.16
deepSeek-r1:32B20GBA5000 < RTX4090 < A100-40gb < RTX509024.21-45.51
deepseek-coder:33b19GBA5000 < RTX4090 < A100-40gb < RTX509025.05-46.71
deepSeek-r1:70B43GBA40 < A6000 < 2*A100-40gb < A100-80gb < H100 < 2*RTX509013.65-27.03
deepseek-v2:236B133GB2*A100-80gb < 2*H100—
deepSeek-r1:671B404GB6*A100-80gb < 6*H100—
deepseek-v3:671B404GB6*A100-80gb < 6*H100—

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 NameSizeRecommended GPUConcurrent RequestsTokens
deepseek-ai/DeepSeek‑R1‑Distill‑Qwen‑1.5B~3GBT1000 < RTX3060 < RTX4060 < 2*RTX3060 < 2*RTX4060 < A4000 < V100501500-500
deepseek-ai/deepseek‑coder‑6.7b‑instruct~13.4GBA5000 < RTX4090501375-4120
deepseek-ai/Janus‑Pro‑7B~14GBA5000 < RTX4090501333-4009
deepseek-ai/DeepSeek‑R1‑Distill‑Qwen‑7B~14GBA5000 < RTX4090501333-4009
deepseek-ai/DeepSeek‑R1‑Distill‑Llama‑8B~16GB2*A4000 < 2*V100 < A5000 < RTX4090501450-2769
deepseek-ai/DeepSeek‑R1‑Distill‑Qwen‑14B~28GB3*V100 < 2*A5000 < A40 < A6000 < A100-40gb < 2*RTX409050449-861
deepseek-ai/DeepSeek‑R1‑Distill‑Qwen‑32B~65GBA100-80gb < 2*A100-40gb < 2*A6000 < H10050577-1480
deepseek-ai/deepseek‑coder‑33b‑instruct~66GBA100-80gb < 2*A100-40gb < 2*A6000 < H10050570-1470
deepseek-ai/DeepSeek‑R1‑Distill‑Llama‑70B~135GB4*A600050466
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———
Model NameSizeRecommended GPUConcurrent RequestsTokens
deepseek-ai/DeepSeek‑R1‑Distill‑Qwen‑1.5B~3GBT1000 < RTX3060 < RTX4060 < 2*RTX3060 < 2*RTX4060 < A4000 < V100501500-500
deepseek-ai/deepseek‑coder‑6.7b‑instruct~13.4GBA5000 < RTX4090501375-4120
deepseek-ai/Janus‑Pro‑7B~14GBA5000 < RTX4090501333-4009
deepseek-ai/DeepSeek‑R1‑Distill‑Qwen‑7B~14GBA5000 < RTX4090501333-4009
deepseek-ai/DeepSeek‑R1‑Distill‑Llama‑8B~16GB2*A4000 < 2*V100 < A5000 < RTX4090501450-2769
deepseek-ai/DeepSeek‑R1‑Distill‑Qwen‑14B~28GB3*V100 < 2*A5000 < A40 < A6000 < A100-40gb < 2*RTX409050449-861
deepseek-ai/DeepSeek‑R1‑Distill‑Qwen‑32B~65GBA100-80gb < 2*A100-40gb < 2*A6000 < H10050577-1480
deepseek-ai/deepseek‑coder‑33b‑instruct~66GBA100-80gb < 2*A100-40gb < 2*A6000 < H10050570-1470
deepseek-ai/DeepSeek‑R1‑Distill‑Llama‑70B~135GB4*A600050466
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———

Express GPU Dedicated Server - P1000

Best For College Project

$84/mo
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Basic GPU Dedicated Server - T1000

For business

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  • 64GB RAM
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  • 100Mbps-1Gbps
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  • 64GB RAM
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  • 100Mbps-1Gbps
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  • 128GB RAM
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  • 100Mbps-1Gbps
  • OS: Windows / Linux

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  • 128GB RAM
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  • 120GB + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux

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  • 128GB RAM
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  • 100Mbps-1Gbps
  • OS: Windows / Linux

Professional GPU VPS - A4000

For Business

$139/mo
  • 32GB RAM
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  • Once per 2 Weeks Backup
  • OS: Linux / Windows 10/ Windows 11

Advanced GPU Dedicated Server - A4000

For business

$289/mo
  • 128GB RAM
  • GPU: Nvidia Quadro RTX A4000
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Advanced GPU Dedicated Server - A5000

For business

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  • 128GB RAM
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  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Enterprise GPU Dedicated Server - A40

For business

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  • 256GB RAM
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  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
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Basic GPU Dedicated Server - RTX 5060

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  • 64GB RAM
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  • 24-Core Platinum 8160
  • 120GB SSD + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux

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  • 256GB RAM
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  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Enterprise GPU Dedicated Server - A100

For business

$809/mo
  • 256GB RAM
  • GPU: Nvidia A100
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  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux

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  • 256GB RAM
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  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Enterprise GPU Dedicated Server - H100

For Business

$2109/mo
  • 256GB RAM
  • GPU: Nvidia H100
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
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Multi-GPU Dedicated Server- 2xRTX 4090

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  • 256GB RAM
  • GPU: 2 x GeForce RTX 4090
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  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server- 2xRTX 5090

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$869/mo
  • 256GB RAM
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  • 240GB SSD + 2TB NVMe + 8TB SATA
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  • 256GB RAM
  • GPU: Nvidia A100
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 2xRTX 3060 Ti

For Business

$329/mo
  • 128GB RAM
  • GPU: 2 x GeForce RTX 3060 Ti
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 2xRTX 4060

For business

$279/mo
  • 64GB RAM
  • GPU: 2 x Nvidia GeForce RTX 4060
  • Eight-Core E5-2690
  • 120GB SSD + 960GB SSD
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server- 2xRTX 5090

For business

$869/mo
  • 256GB RAM
  • GPU: 2 x GeForce RTX 5090
  • Dual Gold 6148
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 2xRTX A4000

For business

$369/mo
  • 128GB RAM
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  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 3xRTX 3060 Ti

For Business

$379/mo
  • 256GB RAM
  • GPU: 3 x GeForce RTX 3060 Ti
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  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 3xV100

For business

$479/mo
  • 256GB RAM
  • GPU: 3 x Nvidia V100
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 3xRTX A5000

For business

$549/mo
  • 256GB RAM
  • GPU: 3 x Quadro RTX A5000
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 3xRTX A6000

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$909/mo
  • 256GB RAM
  • GPU: 3 x Quadro RTX A6000
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Multi-GPU Dedicated Server - 8xRTX A6000

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$2109/mo
  • 512GB RAM
  • GPU: 8 x Quadro RTX A6000
  • Dual 22-Core E5-2699v4
  • 240GB SSD + 4TB NVMe + 16TB SATA
  • 1Gbps
  • OS: Windows / Linux
deepseek-1

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
Read more

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
Read more

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 LocationOn 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
    CustomizationFull — supports fine-tuning, LoRA, quantizationNone or minimal customization allowed
    Privacy & Data Security✅ Data stays local — ideal for sensitive data❌ Data sent to third-party cloud API
    Performance TuningFull control: batch size, concurrency, cachingPredefined, limited tuning
    Supported ModelsAny DeepSeek model (R1, V2, V3, Distill, etc.)Only what the provider offers
    Inference Engine OptionsvLLM, TGI, Ollama, llama.cpp, custom stacksHidden — provider chooses backend
    Startup TimeSlower — requires setup and deploymentInstant — API ready to use
    ScalabilityRequires infrastructure managementScales automatically with provider's backend
    Cost ModelHigher upfront (hardware), lower at scalePay-per-call or token-based — predictable, but expensive at scale
    Use Case FitIdeal for R&D, private deployment, large workloadsBest for prototypes, demos, or small-scale usage
    Example PlatformsDedicated GPU servers, on-premise clustersDBM, 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?

    Hardware needs vary by model size:

    • Small models (1.5B – 7B): ≥16GB VRAM (e.g., RTX 3090, 4090)
    • Medium models (8B – 14B): ≥24–48GB VRAM (e.g., A40, A100, 4090)
    • Large models (32B – 70B+): Multi-GPU setup or high-memory GPUs (e.g., A100 80GB, H100)

    What inference engines are compatible with DeepSeek models?

    You can serve DeepSeek models using:

    • vLLM (high throughput, optimized for production)
    • Ollama (simple local inference, CLI-based)
    • TGI (Text Generation Inference)
    • Exllama / GGUF backends (for quantized models)

    Where can I download DeepSeek models?

    Most DeepSeek models are available on the Hugging Face Hub. Popular variants include:

    • deepseek-ai/deepseek-llm-r1-7b
    • deepseek-ai/deepseek-llm-v2-14b
    • deepseek-ai/deepseek-coder-v3
    • deepseek-ai/deepseek-llm-r1-distill

    Are quantized versions available?

    Yes. Many DeepSeek models have int4 / GGUF quantized versions, making them suitable for lower-VRAM GPUs (8–16GB). These versions can be run using tools like llama.cpp, Ollama, or exllama.

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

    Yes. Most models support parameter-efficient fine-tuning (PEFT) such as LoRA or QLoRA. Make sure your hosting stack includes libraries like PEFT, bitsandbytes, and that your server has enough RAM + disk space for checkpoint storage.

    Can I host multiple DeepSeek models on the same GPU?

    Yes, but only if you have high VRAM GPUs (e.g., 80–100GB A100)

    How do I expose DeepSeek models as APIs?

    You can serve models via RESTful APIs using:

    • vLLM + FastAPI / OpenLLM
    • TGI with built-in OpenAI-compatible API
    • Custom Flask app over Ollama
    • For production workloads, pair with Nginx or Traefik for reverse proxy and SSL.

    Which model is best for lightweight deployment?

    The DeepSeek-R1-Distill-Llama-8B or Qwen-7B models are ideal for fast inference with good instruction-following ability. These can run on RTX 3060+ or T4 with quantization.

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

    • R1: The first release of general-purpose chat/instruction models
    • V2: Improved alignment, larger context length, better reasoning
    • V3 (Coder): Optimized for code generation and understanding
    • Distill: Smaller, faster versions distilled from R1 for inference efficiency

    Is DeepSeek hosting available as a managed service?

    At present, DeepSeek does not offer first-party hosting. However, many cloud GPU providers and inference platforms (e.g., vLLM on Kubernetes, Modal, Banana, Replicate) allow you to host these models easily.