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LLaMA Hosting Service: Auto-Deploy LLaMA 4/3/2 Models Efficiently

Name: LLaMA Hosting Service: Auto-Deploy LLaMA with Ollama
Brand: Cloud Clusters
Price: 409 USD
Availability: InStock
Rating: 4.8 (8964 reviews)

Host and serve Meta’s LLaMA 2, 3, and 4 models with flexible deployment options using leading inference engines like Ollama, vLLM, TGI, TensorRT-LLM, and GGML. Whether you need high-performance GPU hosting, quantized CPU deployment, or edge-friendly LLMs, Cloud Clusters helps you choose the right stack for scalable APIs, chatbots, or private AI applications.

Pre-installed Llama3.1-70B LLM Hosting

Llama 3.1 is a new state-of-the-art model from Meta. You'll get pre-installed Open WebUI + Ollama + Llama3.1-70B, it is a popluar way to self-hosted LLM models.

Enterprise GPU Dedicated Server - RTX A6000

$ 409.00/mo

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256GB RAM
GPU: Nvidia Quadro RTX A6000
Dual 18-Core E5-2697v4
240GB SSD + 2TB NVMe + 8TB SATA
100Mbps-1Gbps
OS: Windows / Linux

Single GPU Specifications:
Microarchitecture: Ampere
CUDA Cores: 10,752
Tensor Cores: 336
GPU Memory: 48GB GDDR6
FP32 Performance: 38.71 TFLOPS

New Arrival

Enterprise GPU Dedicated Server - RTX PRO 6000

$ 729.00/mo

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256GB RAM
GPU: Nvidia RTX PRO 6000
Dual 24-Core Platinum 8160
240GB SSD + 2TB NVMe + 8TB SATA
100Mbps-1Gbps
OS: Windows / Linux

Single GPU Specifications:
Microarchitecture: Blackwell
CUDA Cores: 24,064
Tensor Cores: 752
GPU Memory: 96GB GDDR7
FP32 Performance: 125.10 TFLOPS

Multi-GPU Dedicated Server - 2xA100

$ 1099.00/mo

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

Single GPU Specifications:
Microarchitecture: Ampere
CUDA Cores: 6912
Tensor Cores: 432
GPU Memory: 40GB HBM2
FP32 Performance: 19.5 TFLOPS
Free NVLink Included

Enterprise GPU Dedicated Server - A100(80GB)

$ 1559.00/mo

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256GB RAM
GPU: Nvidia A100
Dual 18-Core E5-2697v4
240GB SSD + 2TB NVMe + 8TB SATA
100Mbps-1Gbps
OS: Windows / Linux

Single GPU Specifications:
Microarchitecture: Ampere
CUDA Cores: 6912
Tensor Cores: 432
GPU Memory: 80GB HBM2e
FP32 Performance: 19.5 TFLOPS

Pre-installed Llama3.2-Vison-90B LLM Hosting

Llama 3.2 Vision is a collection of instruction-tuned image reasoning generative models. You'll get pre-installed Open WebUI + Ollama + Llama3.2-Vison-90B, it is a popluar way to self-hosted LLM models.

New Arrival

Enterprise GPU Dedicated Server - RTX PRO 6000

$ 729.00/mo

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256GB RAM
GPU: Nvidia RTX PRO 6000
Dual 24-Core Platinum 8160
240GB SSD + 2TB NVMe + 8TB SATA
100Mbps-1Gbps
OS: Windows / Linux

Single GPU Specifications:
Microarchitecture: Blackwell
CUDA Cores: 24,064
Tensor Cores: 752
GPU Memory: 96GB GDDR7
FP32 Performance: 125.10 TFLOPS

Multi-GPU Dedicated Server- 2xRTX 5090

$ 859.00/mo

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256GB RAM
GPU: 2 x GeForce RTX 5090
Dual E5-2699v4
240GB SSD + 2TB NVMe + 8TB SATA
1Gbps
OS: Windows / Linux

Single GPU Specifications:
Microarchitecture: Blackwell 2.0
CUDA Cores: 21,760
Tensor Cores: 680
GPU Memory: 32 GB GDDR7
FP32 Performance: 109.7 TFLOPS

Multi-GPU Dedicated Server - 2xA100

$ 1099.00/mo

1mo3mo12mo24mo

Order Now

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

Single GPU Specifications:
Microarchitecture: Ampere
CUDA Cores: 6912
Tensor Cores: 432
GPU Memory: 40GB HBM2
FP32 Performance: 19.5 TFLOPS
Free NVLink Included

Enterprise GPU Dedicated Server - A100(80GB)

$ 1559.00/mo

1mo3mo12mo24mo

Order Now

256GB RAM
GPU: Nvidia A100
Dual 18-Core E5-2697v4
240GB SSD + 2TB NVMe + 8TB SATA
100Mbps-1Gbps
OS: Windows / Linux

Single GPU Specifications:
Microarchitecture: Ampere
CUDA Cores: 6912
Tensor Cores: 432
GPU Memory: 80GB HBM2e
FP32 Performance: 19.5 TFLOPS

Pre-installed Llama4-16x17B LLM Hosting

The Llama 4 collection of models are natively multimodal AI models that enable text and multimodal experiences. You'll get pre-installed Open WebUI + Ollama + Llama4-16x17B, it is a popluar way to self-hosted LLM models.

New Arrival

Enterprise GPU Dedicated Server - RTX PRO 6000

$ 729.00/mo

1mo3mo12mo24mo

Order Now

256GB RAM
GPU: Nvidia RTX PRO 6000
Dual 24-Core Platinum 8160
240GB SSD + 2TB NVMe + 8TB SATA
100Mbps-1Gbps
OS: Windows / Linux

Single GPU Specifications:
Microarchitecture: Blackwell
CUDA Cores: 24,064
Tensor Cores: 752
GPU Memory: 96GB GDDR7
FP32 Performance: 125.10 TFLOPS

Multi-GPU Dedicated Server - 3xRTX A6000

$ 899.00/mo

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Order Now

256GB RAM
GPU: 3 x Quadro RTX A6000
Dual 18-Core E5-2697v4
240GB SSD + 2TB NVMe + 8TB SATA
1Gbps
OS: Windows / Linux

Single GPU Specifications:
Microarchitecture: Ampere
CUDA Cores: 10,752
Tensor Cores: 336
GPU Memory: 48GB GDDR6
FP32 Performance: 38.71 TFLOPS

Enterprise GPU Dedicated Server - A100(80GB)

$ 1559.00/mo

1mo3mo12mo24mo

Order Now

256GB RAM
GPU: Nvidia A100
Dual 18-Core E5-2697v4
240GB SSD + 2TB NVMe + 8TB SATA
100Mbps-1Gbps
OS: Windows / Linux

Single GPU Specifications:
Microarchitecture: Ampere
CUDA Cores: 6912
Tensor Cores: 432
GPU Memory: 80GB HBM2e
FP32 Performance: 19.5 TFLOPS

Enterprise GPU Dedicated Server - H100

$ 2099.00/mo

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Order Now

256GB RAM
GPU: Nvidia H100
Dual 18-Core E5-2697v4
240GB SSD + 2TB NVMe + 8TB SATA
100Mbps-1Gbps
OS: Windows / Linux

Single GPU Specifications:
Microarchitecture: Hopper
CUDA Cores: 14,592
Tensor Cores: 456
GPU Memory: 80GB HBM2e
FP32 Performance: 183TFLOPS

LLaMA Hosting with Ollama — GPU Recommendation

Deploy Meta’s LLaMA models locally with Ollama, a lightweight and developer-friendly LLM runtime. This guide offers GPU recommendations for hosting LLaMA 2 and LLaMA 3 models, ranging from 3B to 70B parameters. Learn which GPUs (e.g., RTX 4090, A100, H100) best support fast inference, low memory usage, and smooth multi-model workflows when using Ollama.

Model Name	Size (4-bit Quantization)	Recommended GPUs	Tokens/s
llama3.2:1b	1.3GB	P1000 < GTX1650 < GTX1660 < RTX2060 < T1000 < RTX3060 Ti < RTX4060 < RTX5060	28.09-100.10
llama3.2:3b	2.0GB	P1000 < GTX1650 < GTX1660 < RTX2060 < T1000 < RTX3060 Ti < RTX4060 < RTX5060	19.97-90.03
llama3:8b	4.7GB	T1000 < RTX3060 Ti < RTX4060 < RTX5060 < A4000 < V100	21.51-84.07
llama3.1:8b	4.9GB	T1000 < RTX3060 Ti < RTX4060 < RTX5060 < A4000 < V100	21.51-84.07
llama3.2-vision:11b	7.8GB	A4000 < A5000 < V100 < RTX4090	38.46-70.90
llama3:70b	40GB	A40 < A6000 < 2A100-40gb < A100-80gb < H100 < 2RTX5090	13.15-26.85
llama3.3:70b, llama3.1:70b	43GB	A40 < A6000 < 2A100-40gb < A100-80gb < H100 < 2RTX5090	13.15-26.85
llama3.2-vision:90b	55GB	2A100-40gb < A100-80gb < H100 < 2RTX5090	~12-20
llama4:16x17b	67GB	2*A100-40gb < A100-80gb < H100	~10-18
llama3.1:405b	243GB	8A6000 < 4A100-80gb < 4*H100	--
llama4:128x17b	245GB	8A6000 < 4A100-80gb < 4*H100	--

LLaMA Hosting with vLLM + Hugging Face — GPU Recommendation

Run LLaMA models efficiently using vLLM with Hugging Face integration for high-throughput, low-latency inference. This guide provides GPU recommendations for hosting LLaMA 4/3/2 models (3B to 70B), covering memory requirements, parallelism, and batching strategies. Ideal for self-hosted deployments on GPUs like A100, H100, or RTX 4090, whether you're building chatbots, APIs, or research pipelines.

Model Name	Size (16-bit Quantization)	Recommended GPU(s)	Concurrent Requests	Tokens/s
meta-llama/Llama-3.2-1B	2.1GB	RTX3060 < RTX4060 < T1000 < A4000 < V100	50-300	~1000+
meta-llama/Llama-3.2-3B-Instruct	6.2GB	A4000 < A5000 < V100 < RTX4090	50-300	1375-7214.10
deepseek-ai/DeepSeek-R1-Distill-Llama-8B meta-llama/Llama-3.1-8B-Instruct	16.1GB	A5000 < A6000 < RTX4090	50-300	1514.34-2699.72
deepseek-ai/DeepSeek-R1-Distill-Llama-70B	132GB	4A100-40gb, 2A100-80gb, 2*H100	50-300	~345.12-1030.51
meta-llama/Llama-3.3-70B-Instruct meta-llama/Llama-3.1-70B meta-llama/Meta-Llama-3-70B-Instruct	132GB	4A100-40gb, 2A100-80gb, 2*H100	50	~295.52-990.61

✅ Explanation:

Recommended GPUs: From left to right, performance from low to high
Tokens/s: from benchmark data.

What is Llama Hosting?

LLaMA Hosting is an infrastructure stack for running LLaMA models for inference or fine-tuning. It allows users to deploy Meta's LLaMA (Large Language Model Meta AI) models on infrastructure, run services or fine-tune them, typically through powerful GPU servers or cloud-based inference services.

✅ Self-hosting (local or dedicated GPU): Deployed on servers with GPUs such as A100, 4090, H100, etc., Supports inference engines: vLLM, TGI, Ollama, llama.cpp, full control of models, caching, scaling

✅ LLaMA as a service (API-based): No infrastructure setup required, suitable for quick experiments or low inference load applications

How Does Pre-Installed LLaMA Hosting Work?

1. High-End GPU Server

Prepare a powerful NVIDIA GPU, install Ubuntu 24.04, and pre-configure CUDA, cuDNN, and PyTorch.

2. Model Installation

The Ollama hosting platform is pre-downloaded, and LLaMA 7B, 13B, and 70B 4-bit quantized checkpoints are already placed on fast NVMe storage for memory-efficient inference.

3. Open WebUI Integration

Once the Open WebUI is installed and linked to the model, your dashboard displays the URL and port; open it in any browser to start chatting with LLaMA—no command line required. Built-in authentication allows multiple team members to log in securely.

4. Developer and CLI Access

SSH/Root Login: Full root privileges remain available for advanced tasks such as fine-tuning, installing additional frameworks, or automating deployments. Models can be called programmatically via REST or Python libraries (Transformers, vLLM, etc.).

Detail Display: Open WebUI Integration

The 'additional software' section in the client control panel provides a unique URL and port for Open WebUI. No additional setup is required; simply copy and paste the URL into any modern browser to access the chat interface.

Detail Display: Open WebUI Integration

You'll need to set a username and password to log in to protect your access. Llama3-70B is selected by default, allowing you to start a conversation directly in the user interface using natural language. View and save your results, download the conversation log, or share it using the built-in export option.

LLM Benchmark Results for LLaMA 1B/3B/8B/70B Hosting

Explore performance benchmarks for hosting LLaMA models across different sizes — 1B, 3B, 8B, and 70B. Compare latency, throughput, and GPU memory usage using inference engines like vLLM, TGI, TensorRT-LLM, and Ollama. Find the optimal GPU setup for self-hosted LLaMA deployments and scale your AI applications efficiently.

Ollama Benchmark for LLaMA

Evaluate the performance of Meta’s LLaMA models using the Ollama inference engine. This benchmark covers LLaMA 2/3/4 across various sizes (3B, 8B, 13B, 70B), highlighting startup time, tokens per second, and GPU memory usage. Ideal for users seeking fast, local LLM deployment on consumer or enterprise GPUs.

vLLM Benchmark for LLaMA

Discover high-performance benchmark results for running LLaMA models with vLLM — a fast, memory-efficient inference engine optimized for large-scale LLM serving. This benchmark evaluates LLaMA 2 and LLaMA 3 across multiple model sizes (3B, 8B, 13B, 70B), measuring throughput (tokens/sec), latency, memory footprint, and GPU utilization. Ideal for deploying scalable, production-grade LLaMA APIs on A100, H100, or 4090 GPUs.

How to Deploy Llama LLMs with Ollama/vLLM

Install and Run Meta LLaMA 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 Meta LLaMA 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 Meta LLaMA Hosting Stack Include?

Hosting Meta’s LLaMA (Large Language Model Meta AI) models—such as LLaMA 2, 3, and 4—requires a carefully designed software and hardware stack to ensure efficient, scalable, and performant inference. Here's what a typical LLaMA hosting stack includes:

Hardware Stack

✅ GPU(s): High-memory GPUs (e.g. A100 80GB, H100, RTX 4090, 5090) for fast inference

✅ CPU & RAM: Sufficient CPU cores and RAM to support preprocessing, batching, and runtime

✅ Storage (SSD): Fast NVMe SSDs for loading large model weights (10–200GB+)

✅ Networking: High bandwidth and low-latency for serving APIs or inference endpoints

Software Stack

✅ Model Weights: Meta LLaMA 2/3/4 models from Hugging Face or Meta

✅ Inference Engine: vLLM, TGI (Text Generation Inference), TensorRT-LLM, Ollama, llama.cpp

✅ Quantization Support: GGML / GPTQ / AWQ for int4 or int8 model compression

✅ Serving Framework: FastAPI, Triton Inference Server, REST/gRPC API wrappers

✅ Environment Tools: Docker, Conda/venv, CUDA/cuDNN, PyTorch (or TensorRT runtime)

✅ Monitoring / Scaling: Prometheus, Grafana, Kubernetes, autoscaling (for cloud-based hosting)

Why LLaMA Hosting Needs a GPU Hardware + Software Stack

LLaMA models are computationally intensive

Meta’s LLaMA models — especially LLaMA 3 and LLaMA 2 at 7B, 13B, or 70B parameters — require billions of matrix operations to perform text generation. These operations are highly parallelizable, which is why modern GPUs (like the A100, H100, or even 4090) are essential. CPUs are typically too slow or memory-limited to handle full-size models in real-time without quantization or batching delays.

High memory bandwidth and VRAM are essential

Full-precision (fp16 or bf16) LLaMA models require significant VRAM — for example, LLaMA 7B needs ~14–16GB, while 70B models may require 140GB+ VRAM or multiple GPUs. GPUs offer the high memory bandwidth necessary for fast inference, especially when serving multiple users or handling long contexts (e.g., 8K or 32K tokens).

Inference engines optimize GPU usage

To maximize GPU performance, specialized software stacks like vLLM, TensorRT-LLM, TGI, and llama.cpp are used. These tools handle quantization, token streaming, KV caching, and batching, drastically improving latency and throughput. Without these optimized software frameworks, even powerful GPUs may underperform.

Production LLaMA hosting needs orchestration and scalability

Hosting LLaMA for APIs, chatbots, or internal tools requires more than just loading a model. You need a full stack: GPU-accelerated backend, a serving engine, auto-scaling, memory management, and sometimes distributed inference. Together, this ensures high availability, fast responses, and cost-efficient usage at scale.

FAQs of Meta LLaMA 4/3/2 Models Hosting

What are the hardware requirements for hosting LLaMA models on Hugging Face?

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Which deployment platforms are supported?

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Can I use LLaMA models for commercial purposes?

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How do I serve LLaMA models via API?

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What quantization formats are supported?

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What are typical hosting costs?

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Can I fine-tune or use LoRA adapters?

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Where can I download the models?

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