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-Instruct
16.1GBA5000 < 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-Instruct
132GB4*A100-40gb, 2*A100-80gb, 2*H10050~295.52-990.61
✅ Explanation:
  • Recommended GPUs: From left to right, performance from low to high
  • Tokens/s: from benchmark data.

Choose The Best GPU Plans for LLaMA 4/3/2 Hosting

  • GPU Card Classify :
  • GPU Server Price:
  • GPU Use Scenario:
  • GPU Memory:
  • GPU Card Model:

Express GPU Dedicated Server - P1000

64.00/mo
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  • 32GB RAM
  • Eight-Core Xeon E5-2690
  • 120GB + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: Nvidia Quadro P1000
  • Microarchitecture: Pascal
  • CUDA Cores: 640
  • GPU Memory: 4GB GDDR5
  • FP32 Performance: 1.894 TFLOPS
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Basic GPU Dedicated Server - T1000

69.00/mo
42% OFF Recurring (Was $119.00)
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  • 64GB RAM
  • Eight-Core Xeon E5-2690
  • 120GB + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: Nvidia Quadro T1000
  • Microarchitecture: Turing
  • CUDA Cores: 896
  • GPU Memory: 8GB GDDR6
  • FP32 Performance: 2.5 TFLOPS
Independence-6 Months Savings

Basic GPU Dedicated Server - GTX 1650

59.50/mo
50% OFF Recurring (Was $119.00)
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  • 64GB RAM
  • Eight-Core Xeon E5-2667v3
  • 120GB + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: Nvidia GeForce GTX 1650
  • Microarchitecture: Turing
  • CUDA Cores: 896
  • GPU Memory: 4GB GDDR5
  • FP32 Performance: 3.0 TFLOPS
Independence-6 Months Savings

Basic GPU Dedicated Server - GTX 1660

79.50/mo
50% OFF Recurring (Was $159.00)
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  • 64GB RAM
  • Dual 10-Core Xeon E5-2660v2
  • 120GB + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: Nvidia GeForce GTX 1660
  • Microarchitecture: Turing
  • CUDA Cores: 1408
  • GPU Memory: 6GB GDDR6
  • FP32 Performance: 5.0 TFLOPS

Advanced GPU Dedicated Server - V100

229.00/mo
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  • 128GB RAM
  • Dual 12-Core E5-2690v3
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: Nvidia V100
  • Microarchitecture: Volta
  • CUDA Cores: 5,120
  • Tensor Cores: 640
  • GPU Memory: 16GB HBM2
  • FP32 Performance: 14 TFLOPS

Professional GPU Dedicated Server - RTX 2060

199.00/mo
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  • 128GB RAM
  • Dual 10-Core E5-2660v2
  • 120GB + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: Nvidia GeForce RTX 2060
  • Microarchitecture: Ampere
  • CUDA Cores: 1920
  • Tensor Cores: 240
  • GPU Memory: 6GB GDDR6
  • FP32 Performance: 6.5 TFLOPS
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Advanced GPU Dedicated Server - RTX 2060

239.00/mo
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  • 128GB RAM
  • Dual 20-Core Gold 6148
  • 120GB + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: Nvidia GeForce RTX 2060
  • Microarchitecture: Ampere
  • CUDA Cores: 1920
  • Tensor Cores: 240
  • GPU Memory: 6GB GDDR6
  • FP32 Performance: 6.5 TFLOPS

Advanced GPU Dedicated Server - RTX 3060 Ti

239.00/mo
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  • 128GB RAM
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: GeForce RTX 3060 Ti
  • Microarchitecture: Ampere
  • CUDA Cores: 4864
  • Tensor Cores: 152
  • GPU Memory: 8GB GDDR6
  • FP32 Performance: 16.2 TFLOPS
Independence Offers

Professional GPU VPS - A4000

99.00/mo
44% OFF Recurring (Was $179.00)
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  • 32GB RAM
  • 24 CPU Cores
  • 320GB SSD
  • 300Mbps Unmetered Bandwidth
  • Once per 2 Weeks Backup
  • OS: Linux / Windows 10/ Windows 11
  • Dedicated GPU: Quadro RTX A4000
  • CUDA Cores: 6,144
  • Tensor Cores: 192
  • GPU Memory: 16GB GDDR6
  • FP32 Performance: 19.2 TFLOPS

Advanced GPU Dedicated Server - A4000

279.00/mo
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  • 128GB RAM
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: Nvidia Quadro RTX A4000
  • Microarchitecture: Ampere
  • CUDA Cores: 6144
  • Tensor Cores: 192
  • GPU Memory: 16GB GDDR6
  • FP32 Performance: 19.2 TFLOPS
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Advanced GPU Dedicated Server - A5000

174.50/mo
50% OFF Recurring (Was $349.00)
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  • 128GB RAM
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: Nvidia Quadro RTX A5000
  • Microarchitecture: Ampere
  • CUDA Cores: 8192
  • Tensor Cores: 256
  • GPU Memory: 24GB GDDR6
  • FP32 Performance: 27.8 TFLOPS
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Enterprise GPU Dedicated Server - A40

299.00/mo
45% OFF Recurring (Was $549.00)
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  • 256GB RAM
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: Nvidia A40
  • Microarchitecture: Ampere
  • CUDA Cores: 10,752
  • Tensor Cores: 336
  • GPU Memory: 48GB GDDR6
  • FP32 Performance: 37.48 TFLOPS
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Basic GPU Dedicated Server - RTX 5060

159.00/mo
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  • 64GB RAM
  • 24-Core Platinum 8160
  • 120GB SSD + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: Nvidia GeForce RTX 5060
  • Microarchitecture: Blackwell 2.0
  • CUDA Cores: 4608
  • Tensor Cores: 144
  • GPU Memory: 8GB GDDR7
  • FP32 Performance: 23.22 TFLOPS
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Enterprise GPU Dedicated Server - RTX 5090

479.00/mo
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  • 256GB RAM
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: GeForce RTX 5090
  • Microarchitecture: Ada Lovelace
  • CUDA Cores: 20,480
  • Tensor Cores: 680
  • GPU Memory: 32 GB GDDR7
  • FP32 Performance: 109.7 TFLOPS

Enterprise GPU Dedicated Server - A100

639.00/mo
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  • 256GB RAM
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: Nvidia A100
  • Microarchitecture: Ampere
  • CUDA Cores: 6912
  • Tensor Cores: 432
  • GPU Memory: 40GB HBM2
  • FP32 Performance: 19.5 TFLOPS
Independence Offers

Enterprise GPU Dedicated Server - A100(80GB)

1019.00/mo
40% OFF Recurring (Was $1699.00)
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  • 256GB RAM
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: Nvidia A100
  • Microarchitecture: Ampere
  • CUDA Cores: 6912
  • Tensor Cores: 432
  • GPU Memory: 80GB HBM2e
  • FP32 Performance: 19.5 TFLOPS
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Enterprise GPU Dedicated Server - H100

1767.00/mo
32% OFF Recurring (Was $2599.00)
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  • 256GB RAM
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: Nvidia H100
  • Microarchitecture: Hopper
  • CUDA Cores: 14,592
  • Tensor Cores: 456
  • GPU Memory: 80GB HBM2e
  • FP32 Performance: 183TFLOPS

Multi-GPU Dedicated Server- 2xRTX 4090

729.00/mo
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  • 256GB RAM
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux
  • GPU: 2 x GeForce RTX 4090
  • Microarchitecture: Ada Lovelace
  • CUDA Cores: 16,384
  • Tensor Cores: 512
  • GPU Memory: 24 GB GDDR6X
  • FP32 Performance: 82.6 TFLOPS
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Multi-GPU Dedicated Server- 2xRTX 5090

859.00/mo
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  • 256GB RAM
  • Dual Gold 6148
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux
  • GPU: 2 x GeForce RTX 5090
  • Microarchitecture: Ada Lovelace
  • CUDA Cores: 20,480
  • Tensor Cores: 680
  • GPU Memory: 32 GB GDDR7
  • FP32 Performance: 109.7 TFLOPS
Independence Offers

Multi-GPU Dedicated Server - 2xA100

769.00/mo
45% OFF Recurring (Was $1399.00)
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  • 256GB RAM
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux
  • GPU: Nvidia A100
  • Microarchitecture: Ampere
  • CUDA Cores: 6912
  • Tensor Cores: 432
  • GPU Memory: 40GB HBM2
  • FP32 Performance: 19.5 TFLOPS
  • Free NVLink Included

Multi-GPU Dedicated Server - 2xRTX 3060 Ti

319.00/mo
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  • 128GB RAM
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 1Gbps
  • OS: Windows / Linux
  • GPU: 2 x GeForce RTX 3060 Ti
  • Microarchitecture: Ampere
  • CUDA Cores: 4864
  • Tensor Cores: 152
  • GPU Memory: 8GB GDDR6
  • FP32 Performance: 16.2 TFLOPS

Multi-GPU Dedicated Server - 2xRTX 4060

269.00/mo
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  • 64GB RAM
  • Eight-Core E5-2690
  • 120GB SSD + 960GB SSD
  • 1Gbps
  • OS: Windows / Linux
  • GPU: 2 x Nvidia GeForce RTX 4060
  • Microarchitecture: Ada Lovelace
  • CUDA Cores: 3072
  • Tensor Cores: 96
  • GPU Memory: 8GB GDDR6
  • FP32 Performance: 15.11 TFLOPS

Multi-GPU Dedicated Server - 2xRTX A5000

439.00/mo
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  • 128GB RAM
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 1Gbps
  • OS: Windows / Linux
  • GPU: 2 x Quadro RTX A5000
  • Microarchitecture: Ampere
  • CUDA Cores: 8192
  • Tensor Cores: 256
  • GPU Memory: 24GB GDDR6
  • FP32 Performance: 27.8 TFLOPS

Multi-GPU Dedicated Server - 2xRTX A4000

359.00/mo
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  • 128GB RAM
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 1Gbps
  • OS: Windows / Linux
  • GPU: 2 x Nvidia RTX A4000
  • Microarchitecture: Ampere
  • CUDA Cores: 6144
  • Tensor Cores: 192
  • GPU Memory: 16GB GDDR6
  • FP32 Performance: 19.2 TFLOPS

Multi-GPU Dedicated Server - 3xRTX 3060 Ti

369.00/mo
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  • 256GB RAM
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux
  • GPU: 3 x GeForce RTX 3060 Ti
  • Microarchitecture: Ampere
  • CUDA Cores: 4864
  • Tensor Cores: 152
  • GPU Memory: 8GB GDDR6
  • FP32 Performance: 16.2 TFLOPS

Multi-GPU Dedicated Server - 3xV100

469.00/mo
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  • 256GB RAM
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux
  • GPU: 3 x Nvidia V100
  • Microarchitecture: Volta
  • CUDA Cores: 5,120
  • Tensor Cores: 640
  • GPU Memory: 16GB HBM2
  • FP32 Performance: 14 TFLOPS

Multi-GPU Dedicated Server - 3xRTX A5000

539.00/mo
1mo3mo12mo24mo
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  • 256GB RAM
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux
  • GPU: 3 x Quadro RTX A5000
  • Microarchitecture: Ampere
  • CUDA Cores: 8192
  • Tensor Cores: 256
  • GPU Memory: 24GB GDDR6
  • FP32 Performance: 27.8 TFLOPS

Multi-GPU Dedicated Server - 3xRTX A6000

899.00/mo
1mo3mo12mo24mo
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  • 256GB RAM
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux
  • GPU: 3 x Quadro RTX A6000
  • Microarchitecture: Ampere
  • CUDA Cores: 10,752
  • Tensor Cores: 336
  • GPU Memory: 48GB GDDR6
  • FP32 Performance: 38.71 TFLOPS
Independence Offers

Multi-GPU Dedicated Server - 4xA100

1374.00/mo
45% OFF Recurring (Was $2499.00)
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  • 512GB RAM
  • Dual 22-Core E5-2699v4
  • 240GB SSD + 4TB NVMe + 16TB SATA
  • 1Gbps
  • OS: Windows / Linux
  • GPU: 4 x Nvidia A100
  • Microarchitecture: Ampere
  • CUDA Cores: 6912
  • Tensor Cores: 432
  • GPU Memory: 40GB HBM2
  • FP32 Performance: 19.5 TFLOPS

Multi-GPU Dedicated Server - 4xRTX A6000

1199.00/mo
1mo3mo12mo24mo
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  • 512GB RAM
  • Dual 22-Core E5-2699v4
  • 240GB SSD + 4TB NVMe + 16TB SATA
  • 1Gbps
  • OS: Windows / Linux
  • GPU: 4 x Quadro RTX A6000
  • Microarchitecture: Ampere
  • CUDA Cores: 10,752
  • Tensor Cores: 336
  • GPU Memory: 48GB GDDR6
  • FP32 Performance: 38.71 TFLOPS

Multi-GPU Dedicated Server - 8xV100

1499.00/mo
1mo3mo12mo24mo
  • 512GB RAM
  • Dual 22-Core E5-2699v4
  • 240GB SSD + 4TB NVMe + 16TB SATA
  • 1Gbps
  • OS: Windows / Linux
  • GPU: 8 x Nvidia Tesla V100
  • Microarchitecture: Volta
  • CUDA Cores: 5,120
  • Tensor Cores: 640
  • GPU Memory: 16GB HBM2
  • FP32 Performance: 14 TFLOPS

Multi-GPU Dedicated Server - 8xRTX A6000

2099.00/mo
1mo3mo12mo24mo
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  • 512GB RAM
  • Dual 22-Core E5-2699v4
  • 240GB SSD + 4TB NVMe + 16TB SATA
  • 1Gbps
  • OS: Windows / Linux
  • GPU: 8 x Quadro RTX A6000
  • Microarchitecture: Ampere
  • CUDA Cores: 10,752
  • Tensor Cores: 336
  • GPU Memory: 48GB GDDR6
  • FP32 Performance: 38.71 TFLOPS
What is Llama Hosting?

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 Hosting

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 Hosting

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

Ollama Hosting

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.
vLLM Hosting

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:
gpu server

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

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

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

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

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

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 Model CapEx (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 Options vLLM, TGI, llama.cpp, GGUF, TensorRT OpenAI, 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
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    DBM has a variety of high-performance Nvidia GPU servers equipped with one or more RTX 4090 24GB, RTX A6000 48GB, A100 40/80GB, which are very suitable for LLMs inference. Choosing the Right GPU for Popular LLMs on Ollama
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