10 Best Qwen GPU Hosting Providers (2026)

Alibaba’s Qwen family is now one of the most popular open-weight LLM lineups. You can download it, fine-tune it and use it commercially. The catch is hardware: even the mid-size Qwen3.8-27B needs a 24 GB GPU to run well, and the 122B and 397B MoE models need data-center cards.

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

We compared the GPU hosting providers that can run Qwen today, from $0.34/hour cloud GPUs to dedicated monthly servers. Below you will find each provider’s best GPU for Qwen, current pricing, pros and cons, and a quick deployment guide.

Quick Comparison: Best Qwen GPU Hosting (September 2026)

Provider	Best GPU for Qwen	Starting Price	Billing	Best For
GPU Mart	RTX 4090 24GB / A100 40GB	$409/month	Monthly	24/7 Qwen APIs on a budget
HOSTKEY	RTX 5090 32GB / RTX PRO 6000 96GB	€279/month	Hourly & monthly	EU hosting, big VRAM
RunPod	RTX 4090 / RTX 5090 / H100	$0.34/hour	Per second	Fast testing & serverless
Vast.ai	RTX 4090 / A100 80GB	~$0.30/hour	Per second	Lowest hourly prices
Lambda	H100 / GH200 / B200	$1.09/hour	Per minute	Large Qwen MoE models
OVHcloud	L40S 48GB / H100	$0.60/hour	Hourly & monthly	Sovereign EU cloud
Hyperstack	H100 PCIe / H100 NVLink	$2.50/hour (H100)	Per minute	Affordable H100s
TensorDock	RTX 4090 / A100 / H100	$0.12/hour	Per second	Cheap VMs with full root
Vultr	L40S / H100 / fractional A100	$1.67/hour (L40S)	Hourly	Global regions
Google Cloud	L4 / A100 / H100 / H200	~$0.70/hour (L4)	Per second	Enterprise & Vertex AI

Which Qwen Model Can You Run? (VRAM Requirements)

Pick the GPU for the model you actually plan to serve. The figures below are for weights only. Add 10 to 30% for the KV cache, and more if you use Qwen’s full 256K context window.

Qwen Model	Parameters	VRAM (4-bit)	VRAM (FP8 / BF16)	Recommended GPU
Qwen3.5-4B / 9B	4B / 9B dense	3 – 6 GB	9 – 18 GB	RTX 4060 Ti, RTX A4000, L4
Qwen3.8-27B / Qwen3.6-27B	27B dense	~17 GB	28 GB / 56 GB	RTX 4090, RTX 5090, L40S
Qwen3.6-35B-A3B (MoE)	35B total, 3B active	~22 GB	~36 GB / 70 GB	RTX 5090, A6000, L40S
Qwen3.5-122B-A10B (MoE)	122B total, 10B active	~70 GB	~125 GB / 245 GB	A100 80GB, H100, 2× H100
Qwen3.5-397B-A17B (MoE)	397B total, 17B active	~220 GB	~400 GB / 800 GB	4–8× H100 / H200
Qwen3.8-Max (2.4T-A95B)	2.4T total, 95B active	—	~2.5 TB (FP8)	Multi-node H200 / B200 cluster

Rule of thumb: For most teams, Qwen3.8-27B on a single RTX 4090 or RTX 5090 is the sweet spot for price and quality. Community benchmarks show 120–160 tokens/second for a single user on a 24 GB card with vLLM.

What Are the Best GPU Hosting Providers for Qwen?

1. GPU Mart

GPU Mart is our top pick for anyone who wants to run Qwen 24/7. Their control panel offers pre-configured apps, including Qwen3, Ollama and Gemma3, so you can have a working Qwen endpoint minutes after the server is delivered.

An RTX 4090 server at $409/month comfortably runs Qwen3.8-27B in 4-bit. The RTX A6000 (48 GB) plan at the same price gives you room for the 35B-A3B MoE model or longer context windows. A secure-cloud RTX 4090 running full-time costs about $540/month, so the dedicated server is the better deal for always-on use.

2. HOSTKEY

HOSTKEY offers one of the widest GPU catalogues for self-hosting Qwen, from RTX 4090 servers up to the RTX PRO 6000 with 96 GB of VRAM. That card can run the 122B-A10B MoE model in 4-bit on a single GPU, which usually needs two H100s elsewhere.

Their servers can come with Ollama and Open WebUI pre-installed, so you get a private ChatGPT-style interface for Qwen without any setup. With EU data centers, HOSTKEY is also a strong choice for GDPR-sensitive projects.

3. RunPod

RunPod is the easiest way to try Qwen by the hour. Start the vLLM template, set the model to Qwen/Qwen3.8-27B-FP8, and you will have an OpenAI-compatible endpoint in about a minute. An RTX 4090 costs $0.34/hour on Community Cloud ($0.74 on Secure Cloud).

For production, RunPod Serverless lets your Qwen endpoint scale to zero when idle, which suits apps with uneven traffic. The larger MoE models are available on H100 SXM ($2.69/hr) and H200 ($3.59/hr) pods.

4. Vast.ai

Vast.ai is a GPU marketplace where independent hosts rent out their hardware. It is often the cheapest place to run Qwen. RTX 4090s regularly go for around $0.30/hour, and A100 80GB cards, which can run Qwen3.5-122B at 4-bit, start from about $0.43/hour.

Filter for hosts with a high reliability score and a fast internet connection, because Qwen weights are tens of gigabytes. Vast.ai works best for benchmarking, fine-tuning and batch jobs.

5. Lambda

Lambda is built for AI workloads and is a great fit for the big Qwen models. An 8× H100 instance ($3.99 per GPU-hour) can serve Qwen3.5-397B-A17B in FP8. The single GH200 with 96 GB of memory ($2.29/hr) is one of the best-value options for the 122B MoE model.

Every instance ships with Lambda Stack, so CUDA, drivers and PyTorch are ready and vLLM installs in one command.

6. OVHcloud

OVHcloud is Europe’s largest cloud provider and a safe choice if your Qwen deployment has to stay under EU jurisdiction. The L40S (48 GB) instance is well suited to Qwen3.8-27B in FP8 or Qwen3.6-35B-A3B, with plenty of room left for long context.

OVHcloud does not charge for traffic, so you can serve a high-volume Qwen API without surprise egress bills.

7. Hyperstack

Hyperstack offers H100s from $2.50/hour, with NVLink configurations that speed up tensor-parallel inference for large Qwen MoE models. A 4× H100 NVLink VM is a cost-effective way to serve Qwen3.5-397B at 4-bit.

Its hibernation feature lets you pause the VM while keeping the disk, which is handy for dev and staging Qwen endpoints.

8. TensorDock

TensorDock gives you full virtual machines instead of containers, which helps if you want to run Qwen alongside your own services, databases or a custom inference stack. Consumer GPUs start from $0.12/hour and H100s from $2.25/hour.

It sits between Vast.ai’s marketplace prices and the reliability of a traditional cloud.

9. Vultr

Vultr is a good fit if you need to serve Qwen from many regions for low latency. Its fractional GPUs are a cheap way to host the small Qwen3.5-4B or 9B models, and an L40S at $1.67/hour handles the 27B model comfortably.

Vultr also offers a managed Kubernetes engine, so you can scale Qwen replicas behind a load balancer.

10. Google Cloud

If your company already runs on Google Cloud, Vertex AI Model Garden lets you deploy Qwen models to a managed endpoint without touching a server. A single L4 (24 GB) G2 instance is enough for Qwen3.8-27B in 4-bit.

Keep an eye on costs, though: on-demand H100 pricing on GCP is among the highest in this list, so use Spot VMs or committed-use discounts for big Qwen deployments.

How to Deploy Qwen on a GPU Server

Once your server is running (Ubuntu + NVIDIA drivers), you can serve Qwen in minutes. There are two common options.

Option 1: Ollama (easiest)

curl -fsSL https://ollama.com/install.sh | sh
ollama run qwen3.8:27b

Option 2: vLLM (production, OpenAI-compatible API)

pip install vllm
vllm serve Qwen/Qwen3.8-27B-FP8 \
  --max-model-len 32768 \
  --gpu-memory-utilization 0.92

vLLM exposes an OpenAI-compatible endpoint on port 8000, so tools like Open WebUI, LangChain or your own app can use Qwen as a drop-in replacement for the OpenAI API. For MoE models across several GPUs, add --tensor-parallel-size N.

How to Choose the Right Qwen Hosting

• Model size decides the GPU. Check the VRAM table first. Buying too little VRAM is the most common mistake.
• Hourly vs monthly. For experiments and fine-tuning, use hourly clouds (RunPod, Vast.ai, Lambda). For a 24/7 chatbot or API, a monthly dedicated server (GPU Mart, HOSTKEY) gives you a fixed price and a whole machine, and is often cheaper than reliable (secure-cloud) hourly GPUs.
• Data location. If you handle EU customer data, pick an EU region (OVHcloud, HOSTKEY, Hyperstack).
• Multi-GPU interconnect. The 122B and 397B MoE models need NVLink or InfiniBand H100/H200 nodes. Consumer-GPU marketplaces are not a good fit here.

FAQ

What is the cheapest way to host Qwen?

For short jobs, a Vast.ai or RunPod Community Cloud RTX 4090 costs about $0.30–$0.35 per hour and runs Qwen3.8-27B in 4-bit. For 24/7 use, a monthly RTX 4090 dedicated server from GPU Mart (around $409/month) usually costs less than paying by the hour.

Can I run Qwen on a single GPU?

Yes. Every Qwen model up to 27B dense, and the 35B-A3B MoE, fits on one 24–32 GB GPU with 4-bit or FP8 quantization. The 122B MoE needs one 80 GB card at 4-bit, and larger models need multi-GPU servers.

Is Qwen free for commercial use?

Qwen 3.5, 3.6 and Qwen3.8-27B are released under Apache 2.0, so commercial use is allowed. Qwen3.8-Max uses a custom license with extra terms for very large products. Always check the license on the model’s Hugging Face page.

Which is better for Qwen: vLLM or Ollama?

Ollama is the fastest way to get started and works well for personal use. vLLM (or SGLang) gives much higher throughput with many concurrent users, so it is the better choice for production APIs.

Conclusion

For most people, the best Qwen GPU hosting is a single RTX 4090 or RTX 5090 running Qwen3.8-27B. Choose GPU Mart or HOSTKEY if you want a fixed monthly price, or RunPod or Vast.ai if you want to pay by the hour. For the large MoE models, go with Lambda, Hyperstack or OVHcloud H100/H200 instances.

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