Best DeepSeek GPU Hosting Providers (2026)

DeepSeek makes some of the strongest open-weight AI models you can download today. The DeepSeek V4 family, and the newer DeepSeek-V4.1-Flash released in September 2026, come close to closed frontier AI models on coding, math and agentic benchmarks. They are MIT-licensed, so you can self-host them, fine-tune them and use them commercially.

🧮 Not sure which GPU you need? Try our AI Model GPU Calculator: pick DeepSeek, your context length and usage, and see the VRAM needed, the cheapest hosting and the API cost side by side.

The catch is size. DeepSeek V4 models are huge Mixture-of-Experts (MoE) LLMs. Only a small part of the model is active for each token, but all weights still have to sit in GPU memory. In practice that means multi-GPU H100, H200 or B200 servers. The good news: the smaller DeepSeek-R1 distilled models run on a single consumer GPU.

We compared the GPU hosting providers that can run DeepSeek AI models in 2026, from $0.30/hour RTX 4090s for the distilled models to 8× H200 nodes for DeepSeek V4-Pro. Below you will find VRAM requirements, the right server for each model, current prices and a deployment guide.

Quick Comparison: Best DeepSeek GPU Hosting (September 2026)

Provider	Best GPU Setup for DeepSeek	Starting Price	Billing	Best For
Lambda	8× H100 SXM, GH200, B200	$3.99/GPU/hour (8× H100)	Hourly	DeepSeek V4 in production
RunPod	1–8× H100 / H200 pods	$2.69/hour (H100)	Per second	Testing V4-Flash by the hour
Hyperstack	H100 NVLink clusters	$2.50/hour (H100)	Hourly	Multi-GPU at a lower price
HOSTKEY	RTX PRO 6000 96GB, H100	€279/month	Hourly & monthly	EU hosting, big VRAM per GPU
GPU Mart	RTX 4090, A100, H100	$409/month	Monthly	24/7 DeepSeek-R1 distill APIs
Vast.ai	RTX 4090, A100 80GB, H100	~$0.30/hour	Per second	Cheapest AI experiments
OVHcloud	L40S, H100, H200	$0.60/hour	Hourly	EU data sovereignty
Google Cloud	A3 (H100/H200), A4 (B200)	~$0.70/hour (L4)	Hourly	Managed AI on Vertex AI

Which DeepSeek Model Can You Run? (VRAM Requirements)

DeepSeek V4 weights ship in FP8, so FP8 is the “native” size. The numbers below are for model weights only. Plan for another 10–30% of VRAM for the KV cache, and a lot more if you use the full 1-million-token context window.

DeepSeek Model	Parameters	VRAM (4-bit)	VRAM (FP8)	Recommended GPUs
DeepSeek-R1-Distill 7B / 8B / 14B	7–14B dense	5 – 9 GB	8 – 15 GB	RTX 4060 Ti, RTX A4000, L4
DeepSeek-R1-Distill 32B	32B dense	~20 GB	~34 GB	RTX 4090, RTX 5090, L40S
DeepSeek-R1-Distill 70B	70B dense	~42 GB	~72 GB	A6000 48GB, A100 80GB, H100
DeepSeek-V4-Flash	~304B total, 13B active (MoE)	~160 GB	~310 GB	2× H200 (4-bit) · 8× H100 (FP8)
DeepSeek-V4.1-Flash	552B total, 8–16B active (MoE)	~290 GB	~560 GB	4× H200 (4-bit) · 8× H200 (FP8)
DeepSeek-V4-Pro	~1.6T total, ~49B active (MoE)	~850 GB	~1.6 TB	8× H200 (4-bit) · 8× B300 or 2 nodes (FP8)

Rule of thumb: If you need a private DeepSeek AI assistant on a budget, run DeepSeek-R1-Distill 32B on a single RTX 4090 or RTX 5090. If you need the real DeepSeek V4 quality for coding agents or AI automation, start with V4-Flash on an 8× H100 node. It is the best price-to-quality ratio in the family.

What Are the Best GPU Hosting Providers for DeepSeek?

1. Lambda

Lambda is our top pick for running the full DeepSeek V4 models. An 8× H100 SXM instance ($3.99 per GPU-hour, about $32/hour in total) has 640 GB of VRAM, enough for DeepSeek-V4-Flash in its native FP8 weights with plenty of room for long context. The GPUs are linked with NVLink, which matters a lot for tensor-parallel AI inference.

Because every instance ships with Lambda Stack, you can install vLLM or SGLang and serve DeepSeek in a few minutes. For V4-Pro, Lambda’s B200 clusters are one of the few places that can hold the whole 1.6T-parameter model.

2. RunPod

RunPod is the easiest way to test DeepSeek before you commit. Spin up a 4× H200 pod ($3.59 per GPU-hour), pick the SGLang or vLLM template, point it at deepseek-ai/DeepSeek-V4.1-Flash, and you have an OpenAI-compatible AI endpoint in minutes. Per-second billing means a two-hour benchmark costs you about $29.

For the distilled DeepSeek-R1 models, a single RTX 4090 at $0.34/hour on Community Cloud is enough. RunPod Serverless is a good way to run a small DeepSeek AI chatbot that only costs money when people use it.

3. Hyperstack

Hyperstack offers H100s from $2.50/hour, which makes an 8-GPU node for DeepSeek-V4-Flash roughly 35% cheaper than on-demand prices at big clouds. Choose the SXM/NVLink VMs for DeepSeek: MoE models move a lot of data between GPUs, and PCIe-only setups are noticeably slower.

Hibernation is handy for dev and staging AI endpoints: you can stop the GPUs at night and keep the 300+ GB of DeepSeek weights on disk, so you do not have to download them again.

4. HOSTKEY

HOSTKEY stands out for its RTX PRO 6000 servers with 96 GB of VRAM per card. Two of them (192 GB) hold a 4-bit DeepSeek-V4-Flash build, and four of them give you enough memory for FP8. Doing the same with 24–32 GB consumer cards is not realistic.

For smaller budgets, a single RTX 4090 or RTX 5090 server runs DeepSeek-R1-Distill 32B at full speed. Servers can come with Ollama and Open WebUI pre-installed, so you get a private ChatGPT-style AI assistant without any setup, hosted in the EU if you need GDPR compliance.

5. GPU Mart

GPU Mart is the budget choice if you want to run a DeepSeek AI model all day, every day. An RTX 4090 server at $409/month runs DeepSeek-R1-Distill 32B in 4-bit. An A100 or RTX A6000 plan gives you room for the 70B distill, which is noticeably smarter for reasoning tasks.

GPU Mart is not the right place for the full DeepSeek V4 models, which need 8-GPU NVLink nodes. For a private AI assistant, an internal coding helper or a document Q&A bot, though, a single-GPU monthly server gives you a fixed, predictable cost.

6. Vast.ai

Vast.ai is usually the cheapest place to try DeepSeek. RTX 4090s go for around $0.30/hour and A100 80GB cards start from about $0.43/hour, which is enough for the 70B distilled model at 4-bit.

You will also find 8× H100 and H200 offers from data-center hosts. Filter for high reliability and at least 1 Gbps download speed, because DeepSeek V4 weights are hundreds of gigabytes. Vast.ai is best for AI experiments, evaluation runs and fine-tuning rather than customer-facing production.

7. OVHcloud

Some companies cannot send data to a Chinese-developed AI service, but can self-host the open DeepSeek weights on European infrastructure. OVHcloud is a safe choice for this. Its H100 and H200 instances run DeepSeek-V4-Flash, and the data never leaves the EU.

OVHcloud does not charge for traffic, so a busy DeepSeek API that streams millions of tokens a day will not surprise you with bandwidth bills.

8. Google Cloud

If your company already runs on Google Cloud, Vertex AI Model Garden lets you deploy DeepSeek models to a managed endpoint without managing servers. For full control, an A3 Ultra VM with 8× H200 runs DeepSeek-V4.1-Flash in FP8.

Keep an eye on costs: on-demand GPU prices on GCP are among the highest in this list. Use Spot VMs for batch AI jobs, or committed-use discounts for long-running DeepSeek deployments.

How to Deploy DeepSeek on a GPU Server

Once your server is running (Ubuntu + NVIDIA drivers), you can serve DeepSeek in a few commands.

Option 1: Ollama for DeepSeek-R1 distilled models (easiest)

curl -fsSL https://ollama.com/install.sh | sh
ollama run deepseek-r1:32b

Use deepseek-r1:14b on 16 GB cards or deepseek-r1:70b on 48–80 GB cards. This is the fastest way to get a private DeepSeek AI assistant running.

Option 2: SGLang or vLLM for DeepSeek V4 (8-GPU node)

pip install "sglang[all]"
python3 -m sglang.launch_server \
  --model-path deepseek-ai/DeepSeek-V4.1-Flash \
  --tp 8 --host 0.0.0.0 --port 30000

Or with vLLM:

pip install vllm
vllm serve deepseek-ai/DeepSeek-V4-Flash-0731 \
  --tensor-parallel-size 8 \
  --max-model-len 131072

Both expose an OpenAI-compatible API, so Open WebUI, LangChain, coding agents or your own app can use DeepSeek as a drop-in replacement for the OpenAI API. Start with a 128K context limit and only raise it if you need to, because very long contexts use a lot of VRAM for the KV cache.

How to Choose the Right DeepSeek Hosting

• Start with the model, not the provider. The distilled R1 models run on one GPU. DeepSeek V4 needs a multi-GPU node. Check the VRAM table first.
• NVLink matters for V4. MoE AI models send a lot of traffic between GPUs. Pick H100/H200 SXM nodes with NVLink (Lambda, Hyperstack, RunPod, Google Cloud) over PCIe-only servers.
• Hourly vs monthly. Use hourly clouds for testing and batch AI jobs. For a 24/7 chatbot on a distilled model, a monthly dedicated server (GPU Mart, HOSTKEY) is usually much cheaper.
• Data location. Self-hosting DeepSeek means your prompts never go to DeepSeek’s own API. Pick an EU provider (OVHcloud, HOSTKEY, Hyperstack) if you handle EU personal data.
• Storage and bandwidth. V4 weights are 300 GB to 1.6 TB. Make sure the server has enough fast NVMe disk and a fast connection to Hugging Face.

FAQ

What is the cheapest way to host DeepSeek?

For short AI experiments, rent an RTX 4090 on Vast.ai or RunPod (about $0.30–$0.35/hour) and run DeepSeek-R1-Distill 32B. For 24/7 use, a monthly RTX 4090 server from GPU Mart (about $409/month) is usually cheaper than paying by the hour.

Can I run DeepSeek V4 on a single GPU?

Not realistically. Even the smallest V4 model, V4-Flash, needs about 160 GB of VRAM at 4-bit, which means at least two H200s or two RTX PRO 6000 cards. If you only have one GPU, use a distilled DeepSeek-R1 model or a smaller open AI model such as Qwen.

Is DeepSeek free for commercial use?

Yes. DeepSeek V4 and V4.1 weights are released under the MIT license, which allows commercial use, fine-tuning and redistribution. Always check the license on the model’s Hugging Face page before you ship an AI product.

Is self-hosted DeepSeek safe for private data?

When you run the open weights on your own GPU server, your prompts and outputs stay on that server and are never sent to DeepSeek. That is the main reason companies self-host DeepSeek instead of using the official API. Choose a provider and region that match your compliance needs.

Should I use vLLM or SGLang for DeepSeek?

Both are supported by DeepSeek and both give an OpenAI-compatible API. SGLang is often a little faster for DeepSeek’s MoE architecture, while vLLM has a larger ecosystem. For a single-GPU distilled model, Ollama is the easiest option.

Conclusion

For most teams, the best DeepSeek GPU hosting depends on which DeepSeek you need. For a private AI assistant on a budget, run DeepSeek-R1-Distill 32B on a single RTX 4090 from GPU Mart, HOSTKEY or RunPod. For real DeepSeek V4 performance, rent an 8× H100 or H200 NVLink node from Lambda, Hyperstack or RunPod.

Prices were checked in September 2026 and can change often. Confirm current pricing on each provider’s website before you order.