Llama Hosting

LLaMA Hosting: Deploy LLaMA 4/3/2 Models with Ollama, vLLM, TGI, TensorRT-LLM & GGML

 

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, DBM helps you choose the right stack for scalable APIs, chatbots, or private AI applications.

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 NameSize (4-bit Quantization)Recommended GPUsTokens/s
llama3.2:1b1.3GBP1000 < GTX1650 < GTX1660 < RTX2060 < T1000 < RTX3060 Ti < RTX4060 < RTX506028.09-100.10
llama3.2:3b2.0GBP1000 < GTX1650 < GTX1660 < RTX2060 < T1000 < RTX3060 Ti < RTX4060 < RTX506019.97-90.03
llama3:8b4.7GBT1000 < RTX3060 Ti < RTX4060 < RTX5060 < A4000 < V10021.51-84.07
llama3.1:8b4.9GBT1000 < RTX3060 Ti < RTX4060 < RTX5060 < A4000 < V10021.51-84.07
llama3.2-vision:11b7.8GBA4000 < A5000 < V100 < RTX409038.46-70.90
llama3:70b40GBA40 < A6000 < 2*A100-40gb < A100-80gb < H100 < 2*RTX509013.15-26.85
llama3.3:70b, llama3.1:70b43GBA40 < A6000 < 2*A100-40gb < A100-80gb < H100 < 2*RTX509013.15-26.85
llama3.2-vision:90b55GB2*A100-40gb < A100-80gb < H100 < 2*RTX5090~12-20
llama4:16x17b67GB2*A100-40gb < A100-80gb < H100~10-18
llama3.1:405b243GB8*A6000 < 4*A100-80gb < 4*H100--
llama4:128x17b245GB8*A6000 < 4*A100-80gb < 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 NameSize (16-bit Quantization)Recommended GPU(s)Concurrent RequestsTokens/s
meta-llama/Llama-3.2-1B2.1GBRTX3060 < RTX4060 < T1000 < A4000 < V10050-300~1000+
meta-llama/Llama-3.2-3B-Instruct6.2GBA4000 < A5000 < V100 < RTX409050-3001375-7214.10
deepseek-ai/DeepSeek-R1-Distill-Llama-8B
meta-llama/Llama-3.1-8B-Instruct16.1GBA5000 < A6000 < RTX409050-3001514.34-2699.72
deepseek-ai/DeepSeek-R1-Distill-Llama-70B132GB4*A100-40gb, 2*A100-80gb, 2*H10050-300~345.12-1030.51
meta-llama/Llama-3.3-70B-Instruct
meta-llama/Llama-3.1-70B
meta-llama/Meta-Llama-3-70B-Instruct132GB4*A100-40gb, 2*A100-80gb, 2*H10050~295.52-990.61

Express GPU Dedicated Server - P1000

Best For College Project

$74/mo
    • 32 GB RAM
    • GPU: Nvidia Quadro P1000
    • Eight-Core Xeon E5-2690
    • 120GB + 960GB SSD
    • 100Mbps-1Gbps
    • OS: Windows / Linux

Basic GPU Dedicated Server - T1000

For business

$109/mo
    • 64 GB RAM
    • GPU: Nvidia Quadro T1000
    • Eight-Core Xeon E5-2690
    • 120GB + 960GB SSD
    • 100Mbps-1Gbps
    • OS: Windows / Linux

Basic GPU Dedicated Server - GTX 1650

For business

$129/mo
  • 64GB RAM
  • GPU: Nvidia GeForce GTX 1650
  • Eight-Core Xeon E5-2667v3
  • 120GB + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Basic GPU Dedicated Server - GTX 1660

For business

$149/mo
  • 64GB RAM
  • GPU: Nvidia GeForce GTX 1660
  • Dual 10-Core Xeon E5-2660v2
  • 120GB + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Advanced GPU Dedicated Server - V100

Best For College Project

$239/mo
  • 128GB RAM
  • GPU: Nvidia V100
  • Dual 12-Core E5-2690v3
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Professional GPU Dedicated Server - RTX 2060

For business

$209/mo
  • 128GB RAM
  • GPU: Nvidia GeForce RTX 2060
  • Dual 10-Core E5-2660v2
  • 120GB + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Advanced GPU Dedicated Server - RTX 2060

For business

$249/mo
  • 128GB RAM
  • GPU: Nvidia GeForce RTX 2060
  • Dual 20-Core Gold 6148
  • 120GB + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Advanced GPU Dedicated Server - RTX 3060 Ti

For business

$249/mo
  • 128GB RAM
  • GPU: GeForce RTX 3060 Ti
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Professional GPU VPS - A4000

For Business

$139/mo
  • 32GB RAM
  • 24 CPU Cores
  • 320GB SSD
  • 300Mbps Unmetered Bandwidth
  • Once per 2 Weeks Backup
  • OS: Linux / Windows 10/ Windows 11

Advanced GPU Dedicated Server - A4000

For business

$289/mo
  • 128GB RAM
  • GPU: Nvidia Quadro RTX A4000
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Advanced GPU Dedicated Server - A5000

For business

$279/mo
  • 128GB RAM
  • GPU: Nvidia Quadro RTX A5000
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Enterprise GPU Dedicated Server - A40

For business

$449/mo
  • 256GB RAM
  • GPU: Nvidia A40
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Basic GPU Dedicated Server - RTX 5060

For Business

$199/mo
  • 64GB RAM
  • GPU: Nvidia GeForce RTX 5060
  • 24-Core Platinum 8160
  • 120GB SSD + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Enterprise GPU Dedicated Server - RTX 5090

For business

$489/mo
  • 256GB RAM
  • GPU: GeForce RTX 5090
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Enterprise GPU Dedicated Server - A100

For business

$809/mo
  • 256GB RAM
  • GPU: Nvidia A100
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Enterprise GPU Dedicated Server - A100(80GB)

For business

$1569/mo
  • 256GB RAM
  • GPU: Nvidia A100
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Enterprise GPU Dedicated Server - H100

For Business

$2109/mo
  • 256GB RAM
  • GPU: Nvidia H100
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server- 2xRTX 4090

For business

$739/mo
  • 256GB RAM
  • GPU: 2 x GeForce RTX 4090
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server- 2xRTX 5090

For business

$869/mo
  • 256GB RAM
  • GPU: 2 x GeForce RTX 5090
  • Dual Gold 6148
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 2xA100

For business

$1309/mo
  • 256GB RAM
  • GPU: Nvidia A100
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 2xRTX 3060 Ti

For Business

$329/mo
  • 128GB RAM
  • GPU: 2 x GeForce RTX 3060 Ti
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 2xRTX 4060

For business

$279/mo
  • 64GB RAM
  • GPU: 2 x Nvidia GeForce RTX 4060
  • Eight-Core E5-2690
  • 120GB SSD + 960GB SSD
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server- 2xRTX 5000

For business

$449/mo
  • 128GB RAM
  • GPU: 2 x Quadro RTX A5000
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 2xRTX A4000

For business

$369/mo
  • 128GB RAM
  • GPU: 2 x Quadro RTX A5000
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 3xRTX 3060 Ti

For Business

$379/mo
  • 256GB RAM
  • GPU: 3 x GeForce RTX 3060 Ti
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 3xV100

For business

$479/mo
  • 256GB RAM
  • GPU: 3 x Nvidia V100
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 3xRTX A5000

For business

$549/mo
  • 256GB RAM
  • GPU: 3 x Quadro RTX A5000
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 3xRTX A6000

For business

$909/mo
  • 256GB RAM
  • GPU: 3 x Quadro RTX A6000
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 4xA100

For Business

$1909/mo
  • 512GB RAM
  • GPU: 4 x Nvidia A100
  • Dual 22-Core E5-2699v4
  • 240GB SSD + 4TB NVMe + 16TB SATA
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 4xRTX A6000

For business

$1209/mo
  • 512GB RAM
  • GPU: 4 x Quadro RTX A6000
  • Dual 22-Core E5-2699v4
  • 240GB SSD + 4TB NVMe + 16TB SATA
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 8xV100

For business

$1509/mo
  • 512GB RAM
  • GPU: 8 x Nvidia Tesla V100
  • Dual 22-Core E5-2699v4
  • 240GB SSD + 4TB NVMe + 16TB SATA
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 8xRTX A6000

For business

$2109/mo
  • 512GB RAM
  • GPU: 8 x Quadro RTX A6000
  • Dual 22-Core E5-2699v4
  • 240GB SSD + 4TB NVMe + 16TB SATA
  • 1Gbps
  • OS: Windows / Linux
llama-1

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

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
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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
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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:
    llama-2

    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

    llama-3

    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.

    Self-hosted Llama Hosting vs. Llama as a Service

    In addition to GPU-based dedicated servers that host LLM models themselves, there are also many LLM API (Large Model as a Service) solutions on the market, which have become one of the mainstream ways to use models.
    Feature🖥️ Self-Hosted LLaMA☁️ LLaMA as a Service (API)
    Control & Customization✅ Full (infra, model version, tuning)❌ Limited (depends on provider/API features)
    Performance✅ Optimized for your use case⚠️ Shared resources, possible latency
    Initial Setup❌ Requires setup, infra, GPUs, etc.✅ Ready-to-use API
    Scalability⚠️ Needs manual scaling/K8s/devops✅ Auto-scaled by provider
    Cost ModelCapEx (hardware or GPU rental)OpEx (pay-per-token or per-call pricing)
    Latency✅ Low (especially for on-prem)⚠️ Varies (depends on network & provider)
    Security / Privacy✅ Full control over data⚠️ Depends on provider's data policy
    Model Fine-tuning / LoRA✅ Possible (custom models, LoRA)❌ Not supported or limited
    Toolchain OptionsvLLM, TGI, llama.cpp, GGUF, TensorRTOpenAI, Replicate, Together AI, Groq, etc.
    Updates / Maintenance❌ Your responsibility✅ Handled by provider
    Offline Use✅ Possible❌ Always online

    FAQs of Meta LLaMA 4/3/2 Models Hosting

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

    It depends on the model size and precision. For fp16 inference:

    • LLaMA 2/3/4 – 7B: RTX 4090 / A5000 (24 GB VRAM)
    • LLaMA 13B: RTX 5090 / A6000 / A100 40GB
    • LLaMA 70B: A100 80GB x2 or H100 x2 (multi-GPU)

    Which deployment platforms are supported?

    LLaMA models can be hosted using:

    • vLLM (best for high-throughput inference)
    • TGI (Text Generation Inference)
    • Ollama (easy local deployment)
    • llama.cpp / GGML / GGUF (CPU / GPU with quantization)
    • TensorRT-LLM (NVIDIA-optimized deployment)
    • LM Studio, Open WebUI (UI-based inference)

    Can I use LLaMA models for commercial purposes?

    LLaMA 2/3/4: Available under a custom Meta license. Commercial use is allowed with some limitations (e.g., >700M MAU companies must get special permission).

    How do I serve LLaMA models via API?

    You can use:

    • vLLM + FastAPI/Flask to expose REST endpoints
    • TGI with OpenAI-compatible APIs
    • Ollama’s local REST API
    • Custom wrappers around llama.cpp with web UI or LangChain integration

    What quantization formats are supported?

    LLaMA models support multiple formats:

    • fp16: High-quality GPU inference
    • int4: Low-memory, fast CPU/GPU inference (GGUF)
    • GPTQ: Compression + GPU compatibility
    • AWQ: NVIDIA optimized

    What are typical hosting costs?

    • Self-hosted: $1–3/hour (GPU rental, depending on model)
    • API (LaaS): $0.002–$0.01 per 1K tokens (e.g., Together AI, Replicate)
    • Quantized models can reduce costs by 60–80%

    Can I fine-tune or use LoRA adapters?

    Yes. LLaMA models support fine-tuning and parameter-efficient fine-tuning (LoRA, QLoRA, DPO, etc.), especially on:

    • PEFT + Hugging Face Transformers
    • Axolotl / OpenChatKit
    • Loading custom LoRA adapters in Ollama or llama.cpp

    Where can I download the models?

    You can download LLaMA Models on Hugging Face:

    • meta-llama/Llama-2-7b
    • meta-llama/Llama-3-8B-Instruct