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.
| 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 < 2*A100-40gb < A100-80gb < H100 < 2*RTX5090 | 13.65-27.03 |
| deepseek-v2:236B | 133GB | 2*A100-80gb < 2*H100 | -- |
| deepSeek-r1:671B | 404GB | 6*A100-80gb < 6*H100 | -- |
| deepseek-v3:671B | 404GB | 6*A100-80gb < 6*H100 | -- |
| 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 < 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 | -- | -- | -- |
Express GPU Dedicated Server - P1000
Basic GPU Dedicated Server - T1000
Basic GPU Dedicated Server - GTX 1650
Basic GPU Dedicated Server - GTX 1660
Advanced GPU Dedicated Server - V100
Professional GPU Dedicated Server - RTX 2060
Advanced GPU Dedicated Server - RTX 2060
Advanced GPU Dedicated Server - RTX 3060 Ti
Professional GPU VPS - A4000
Advanced GPU Dedicated Server - A4000
Advanced GPU Dedicated Server - A5000
Enterprise GPU Dedicated Server - A40
Basic GPU Dedicated Server - RTX 5060
Enterprise GPU Dedicated Server - RTX 5090
Enterprise GPU Dedicated Server - A100
Enterprise GPU Dedicated Server - A100(80GB)
Enterprise GPU Dedicated Server - H100
Multi-GPU Dedicated Server- 2xRTX 4090
Multi-GPU Dedicated Server- 2xRTX 5090
Multi-GPU Dedicated Server - 2xA100
Multi-GPU Dedicated Server - 2xRTX 3060 Ti
Multi-GPU Dedicated Server - 2xRTX 4060
Multi-GPU Dedicated Server - 2xRTX A5000
Multi-GPU Dedicated Server - 2xRTX A4000
Multi-GPU Dedicated Server - 3xRTX 3060 Ti
Multi-GPU Dedicated Server - 3xV100
Multi-GPU Dedicated Server - 3xRTX A5000
Multi-GPU Dedicated Server - 3xRTX A6000
Multi-GPU Dedicated Server - 4xA100
Multi-GPU Dedicated Server - 4xRTX A6000
Multi-GPU Dedicated Server - 8xV100
Multi-GPU Dedicated Server - 8xRTX A6000
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.
| 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 |
