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Order a server with pre-installed software and get a ready-to-use environment in minutes.
Open source LLM from China - the first-generation of reasoning models with performance comparable to OpenAI-o1.
Google Gemma 2 is a high-performing and efficient model available in three sizes: 2B, 9B, and 27B.
New state of the art 70B model. Llama 3.3 70B offers similar performance compared to the Llama 3.1 405B model.
Phi-4 is a 14B parameter, state-of-the-art open model from Microsoft.
PyTorch is a fully featured framework for building deep learning models.
TensorFlow is a free and open-source software library for machine learning and artificial intelligence.
Apache Spark is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters.
Open ecosystem for Data science and AI development.
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Smaller models of Ollama need a modern GPU with 16GB of VRAM or more. In the case of large-scale LLMs, including Llama 70B or DeepSeek, 48GB or more of VRAM and high bandwidth memory (HBM) is recommended. The best performance is achieved with GPUs that fulfill the ollama gpu requirements such as CUDA (in NVIDIA) or ROCm (in AMD).
Yes. Ollama is compatible with AMD Instinct GPUs including MI200 or MI300 using ROCm stack. HOSTKEY offers ollama amd gpu servers that are already configured to work with ROCm drivers and to be accelerated.
The best gpu for ollama depends on your workload:
Ollama gpu acceleration can be configured using images of Docker that are already installed or custom environments. HOSTKEY servers come with an optimized ollama gpu setup which means that you do not have to install the drivers manually.
Yes. Multi gpu support Ollama can be scaled to multiple GPUs and enables faster inference, higher throughput and large models can be handled easily.
It is compatible based on CUDA or ROCm. In case your GPU supports CUDA 12 or higher (NVIDIA) or ROCm 5 or higher (AMD), it is compatible with ollama gpu. HOSTKEY offers stability by offering pre-tested environments.
Sequential processing is much slower than running Ollama on a CPU. A GPU offers a massively parallel acceleration, which offers real-time inference and scale-efficiency to multi-billion parameter models.
Yes. HOSTKEY servers are all equipped with ready-to-use Ollama gpu environments, drivers, and popular LLMs (Gemma, Qwen, DeepSeek, Llama, Phi). This will enable you to install without any manual installation.
Get Top LLM models on high-performance GPU instances
The advanced LLMs, including Llama, Gemma, Phi, Qwen, and DeepSeek, are run with the help of strong GPU acceleration. The hardware and optimized environments are critical to the performance of Ollama. This is where ollama amd gpu hosting is introduced which has scalable performance and supports both AMD ROCm and NVIDIA CUDA. With the help of the GPU acceleration, it is possible to perform smooth inference, quick fine-tuning, and scale workloads on multiple GPUs. Ollama multi GPU hosting is the second step to enterprises and developers that would like to be fast and reliable.
Ollama gpu hosting With HOSTKEY, you receive access to state-of-the-art AMD and NVIDIA GPUs, optimized drivers, pre-configured frameworks, and elastic cloud computing. HOSTKEY offers the most suitable environment regardless of whether you are experimenting with a single model or are using many production workloads. It takes just a few minutes to spin up a server, choose the appropriate configuration and start serving or optimizing models.
GPUs are parallel calculators and thus they are suitable in the acceleration of transformer-based models. The performance of Ollama on CPUs alone can tend to be slow in response and GPUs can provide the throughput required to support real-time inference. With the help of ollama gpu acceleration, you can:
Both AMD ROCm and NVIDIA CUDA offer a full-fledged ecosystem to program their GPUs, and both should be compatible with ollama gpu across hardware types. With HOSTKEY, you will have the choice of the best gpu to use in ollama based on your budget, requirements of the VRAM and the latency goals you want to achieve.
HOSTKEY is compatible with both NVIDIA CUDA and AMD ROCm, which means that ollama gpus are smoothly compatible. CUDA is the most developed environment with a comprehensive set of developer tools, and AMD ROCm is a developing environment with a growing ecosystem of solutions that are cost-effective and have an excellent FP16 and INT8 performance.
Ease of manual configuration. HOSTKEY servers are pre-configured with drivers, CUDA/ROCm toolkits, and optimized ollama gpu configuration to deploy them instantly. This implies that your environment is set up and does not require them to trouble shoot kernel modules or version differences.
Switch on ollama multi gpu acceleration to accelerate inference and training. NCCL can be used to distribute workloads with NVIDIA or RCCL with AMD, which also guarantees efficient scaling. Such parallelism is crucial to real-time deployments of production.
HOSTKEY servers have high capacity VRAM GPUs including A6000 (96GB) and AMD MI300X (192GB). These cards are also required to run large LLMs such as Llama 70B, Mixtral, or DeepSeek with little to no sharding.
Each server is based on enterprise-level infrastructure with inbuilt redundancy, DDoS protection and encryption. You are able to scale your resources on demand and integrate your Ollama implementation with a private network or VPN.
RTX 4090 and the upcoming RTX 5090 are also good when the developer requires the best consumer GPUs. They offer great FP16 performance, 24GB+ VRAM, and many believe that it is the best gpu to use ollama by cost-conscious professionals.
The A5000 and A6000 designed by NVIDIA are professional. Having 24GB and 48GB VRAM, they fit well in medium and large models, multi-user, and enterprise-level Ollama hosting.
AMD Instinct GPUs are deep learning optimized with ROCm. Ollama amd gpu hosting MI200/MI300 has good performance to dollar ratios. Single-gpu inference of gigantic models is made possible by MI300X with 192GB HBM.
The A100 and H100 are still the gold standard of ollama gpu acceleration. These GPUs have been optimized with Tensor Cores, huge bandwidth, and 80GB VRAM, making them suitable to high-intensity production workloads.
Although smaller models can be operated with 16GB consumer GPUs, inference with at least 24GB VRAM is needed to run on a large scale. Mid-size models can be run on a 24GB GPU, however, 65B+ parameter LLMs require 48GB or larger.
HOSTKEY has RTX cards to enterprise A/H series, and AMD Instinct, all ollama gpu requirements use cases.
We offer turnkey images which have Ollama, Docker and GPU libraries installed. This saves time taken to deploy by hours down to minutes.
HOSTKEY engineers keep an eye on your workloads, so that your uptime is guaranteed. In case of a driver mismatch or ollama gpu configuration problem, our team fixes it in a short time.
Select North American, European, or Asian data centers to ensure that the latency is minimal to your end users.
Pay per hour or month. Reservations with a long term cut the cost by up to 40 percent.
The GPUs are used to compute the attention mechanism in a matter of seconds because they multiply matrices. This enhances latency of applications such as chatbots, agents and API endpoints.
Use ollama gpu accelerated scalable chatbots to provide customer service.
Tune Llama 2, Gemma or Qwen easily on either ollama amd gpu or NVIDIA hardware.
Ollama multi gpu scaling is an extension of ollama used to integrate with internal search engines, CRMs, and ERPs.
The CUDA toolkit available at NVIDIA provides compatibility of ollama gpu with optimized libraries on AI..
AMD ROCm also supports Miospen and RCCL of distributed workloads using ollama amd gpu hosting.
Share loads using NVLink or PCIe 5.0. Ollama aids sharding across GPUs in models that are not single-GPU VRAM.
The A100, H100 and MI300 are the GPUs of the high-end category, and the RTX 4090 is relatively cheap. Costs scale with VRAM.
Single GPU: prototyping is cost-effective.
Multi-GPU: required when the size of 65B+ LLMs or when producing thousands of requests per second.
CUDA 11.8, 12.0, 12.2 available depending on your ollama gpu configuration.
ROCm 5.4, 5.6 fully supported for ollama amd gpu hosting.
Ubuntu 22.04 is the preferred OS. Windows Server available for enterprise compatibility.
Ollama is pre-loaded on Docker containers, which is quicker to deploy. The integration of Kubernetes provides effective ollama multi gpu scheduling.
Scheduling of fair-share GPU eliminates resource hogging. Supports NVIDIA A100/H100 Multi-instance GPU.
Mixed precision minimizes memory space as well as enhancing inference speed.
MLPerf benchmarks can be used to determine the best gpu with ollama workloads.
Track temperatures, VRAM usage, and process allocation. Optimize based on bottlenecks.
Use REST/gRPC with load balancers. Integrate with CI/CD pipelines for smooth updates.
Install in different locations to have redundancy on a global scale.
Integrate distributed computing systems with ollama multi gpu.
AES-256 encryption ensures data security.
Dedicated bare metal servers also ensure isolation as opposed to shared GPUs.
Backups and model checkpoints are automatically done to avoid downtime.
The following are sample dedicated and VPS plans that are optimized towards ollama amd gpu hosting. Each server has AMD EPYC 4thGen processors, 1Gbps port and immediate deployment.
All HOSTKEY servers are deployed within minutes. There are pre-installed software Ollama, Docker, and latest LLMs (DeepSeek, Gemma, Llama, Phi, Qwen, and others). Select either hourly or monthly billing and up to 40-year discounts. You can be ready to production faster than ever with ollama gpu acceleration.
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