Self-Hosted LLM & Serverless LLM Hosting Solutions
Deploying Large Language Models (LLMs) like DeepSeek, LLaMA, or Mistral requires robust infrastructure optimized for AI inference and fine-tuning. Whether you need the agility of serverless LLM hosting via cloud APIs, or the ultimate data privacy of a self hosted LLM deployed on an on premise LLM architecture, we have you covered. Choose from our dedicated GPU servers or a private LLM VPS based on your workload size, latency needs, and security compliance.
LLM Hosting on NVIDIA Data Center GPU Servers
Advanced GPU Dedicated Server - V100
- 128GB RAM
- GPU: Nvidia V100
- Dual 12-Core E5-2690v3
- 240GB SSD + 2TB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Volta
- CUDA Cores: 5,120
- Tensor Cores: 640
- GPU Memory: 16GB HBM2
- FP32 Performance: 14 TFLOPS
Multi-GPU Dedicated Server - 3xV100
- 256GB RAM
- GPU: 3 x Nvidia V100
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Volta
- CUDA Cores: 5,120
- Tensor Cores: 640
- GPU Memory: 16GB HBM2
- FP32 Performance: 14 TFLOPS
Enterprise GPU Dedicated Server - A100
- 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: 40GB HBM2
- FP32 Performance: 19.5 TFLOPS
Multi-GPU Dedicated Server - 4xA100
- 512GB RAM
- GPU: 4 x Nvidia A100
- Dual 22-Core E5-2699v4
- 240GB SSD + 4TB NVMe + 16TB 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
Enterprise GPU Dedicated Server - A100(80GB)
- 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
- 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
General Recommendations
NVIDIA H100: Optimized for high-performance LLM hosting, offering over 1 petaFLOP in FP8 precision and around 15,000 CUDA cores. It excels in low-latency scenarios and is ideal for strict service-level objectives (SLOs). The H100's 80 GB VRAM (HBM3) supports larger context windows and batching, making it suitable for massive models 1.
NVIDIA A100: A balanced workhorse with 40 GB or 80 GB VRAM options. It handles most LLM hosting scenarios efficiently and is widely used for its reliability and software compatibility.
NVIDIA Tesla V100: Remains a powerful and capable GPU for various Large Language Model (LLM) tasks, despite being based on the older Volta architecture. Compared to the newer Ampere (A100) or Hopper (H100) architectures, the V100 lacks support for FP8 precision and has lower raw performance and memory bandwidth.
LLM Hosting on GeForce RTX GPU Bare Metal Servers
Basic GPU Dedicated Server - RTX 4060
- 64GB RAM
- GPU: Nvidia GeForce RTX 4060
- Eight-Core E5-2690
- 120GB SSD + 960GB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ada Lovelace
- CUDA Cores: 3072
- Tensor Cores: 96
- GPU Memory: 8GB GDDR6
- FP32 Performance: 15.11 TFLOPS
Basic GPU Dedicated Server - RTX 5060
- 64GB RAM
- GPU: Nvidia GeForce RTX 5060
- 24-Core Platinum 8160
- 120GB SSD + 960GB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Blackwell 2.0
- CUDA Cores: 4608
- Tensor Cores: 144
- GPU Memory: 8GB GDDR7
- FP32 Performance: 23.22 TFLOPS
Enterprise GPU Dedicated Server - RTX 4090
- 256GB RAM
- GPU: GeForce RTX 4090
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ada Lovelace
- CUDA Cores: 16,384
- Tensor Cores: 512
- GPU Memory: 24 GB GDDR6X
- FP32 Performance: 82.6 TFLOPS
Enterprise GPU Dedicated Server - RTX 5090
- 256GB RAM
- GPU: GeForce RTX 5090
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-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
General Recommendations
Nvidia GeForce RTX 4060 & RTX 5060 (8GB VRAM): With 8GB of VRAM, these cards are at the entry-level for serious LLM hosting. Many popular models like Mistral 7B, Gemma 7B, and Llama 3.1 8B can run well with 4-bit quantization. This allows for a good balance of performance and quality.
Nvidia GeForce RTX 4090 (24GB VRAM) & RTX 5090 (32GB VRAM): These are the flagship consumer GPUs and are powerhouses for LLM inference. The substantial VRAM capacity makes them capable of running medium-sized models. Both are the go-to consumer GPUs for serious local LLM inference.
LLM Hosting on Quadro RTX GPU Bare Metal Servers
Multi-GPU Dedicated Server - 4xRTX A6000
- 512GB RAM
- GPU: 4 x Quadro RTX A6000
- Dual 22-Core E5-2699v4
- 240GB SSD + 4TB NVMe + 16TB 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
Multi-GPU Dedicated Server - 3xRTX A6000
- 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 - RTX A6000
- 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
Multi-GPU Dedicated Server - 3xRTX A5000
- 256GB RAM
- GPU: 3 x Quadro RTX A5000
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ampere
- CUDA Cores: 8192
- Tensor Cores: 256
- GPU Memory: 24GB GDDR6
- FP32 Performance: 27.8 TFLOPS
Advanced GPU Dedicated Server - A5000
- 128GB RAM
- GPU: Nvidia Quadro RTX A5000
- Dual 12-Core E5-2697v2
- 240GB SSD + 2TB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ampere
- CUDA Cores: 8192
- Tensor Cores: 256
- GPU Memory: 24GB GDDR6
- FP32 Performance: 27.8 TFLOPS
Advanced GPU Dedicated Server - A4000
- 128GB RAM
- GPU: Nvidia Quadro RTX A4000
- Dual 12-Core E5-2697v2
- 240GB SSD + 2TB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ampere
- CUDA Cores: 6144
- Tensor Cores: 192
- GPU Memory: 16GB GDDR6
- FP32 Performance: 19.2 TFLOPS
General Recommendations
RTX A4000 (16GB VRAM): This is a great entry point for professional LLM hosting. Its 16GB of VRAM is enough to run most 7B to 13B parameter models without any or with light quantization.
RTX A5000 (24GB VRAM): The A5000 is a powerful, well-balanced option. Its 24GB of VRAM is comparable to a GeForce RTX 4090, allowing it to run large models up to 30B parameters with quantization. It also supports NVLink, giving you the option to scale to 48GB of VRAM with a second card for a mid-tier multi-GPU setup.
RTX A6000 (48GB VRAM): This is the flagship workstation GPU and one of the best for LLM hosting outside of dedicated data center cards like the A100 or H100. With its massive 48GB of VRAM, the A6000 can run 70B parameter models with minimal or no quantization, providing maximum inference speed and accuracy. With NVLink, two A6000s can combine to a staggering 192GB of VRAM, which allows for hosting very large models or using very large context windows.
Private LLM Hosting on GPU VPS (LLM VPS)
Professional GPU VPS - A4000
- 30GB RAM
- 24 CPU Cores
- 320GB SSD
- 300Mbps Unmetered Bandwidth
- Once per 2 Weeks Backup
- OS: Windows / Linux
- Dedicated GPU: Quadro RTX A4000
- CUDA Cores: 6,144
- Tensor Cores: 192
- GPU Memory: 16GB GDDR6
- FP32 Performance: 19.2 TFLOPS
Advanced GPU VPS - RTX 5090
- 90GB RAM
- 32 CPU Cores
- 400GB SSD
- 500Mbps Unmetered Bandwidth
- Once per 2 Weeks Backup
- OS: Windows / Linux
- Dedicated GPU: GeForce RTX 5090
- CUDA Cores: 21,760
- Tensor Cores: 680
- GPU Memory: 32GB GDDR7
- FP32 Performance: 109.7 TFLOPS
Professional GPU VPS- RTX Pro 2000
- 28GB RAM
- 16 CPU Cores
- 240GB SSD
- 300Mbps Unmetered Bandwidth
- Once per 2 Weeks Backup
- OS: Windows / Linux
- Dedicated GPU: Nvidia RTX Pro 2000
- CUDA Cores: 4,352
- Tensor Cores: 5th Gen
- GPU Memory: 16GB GDDR7
- FP32 Performance: 17 TFLOPS
Advanced GPU VPS- RTX Pro 4000
- 60GB RAM
- 24 CPU Cores
- 320GB SSD
- 500Mbps Unmetered Bandwidth
- Once per 2 Weeks Backup
- OS: Windows / Linux
- Dedicated GPU: Nvidia RTX Pro 4000
- CUDA Cores: 8,960
- Tensor Cores: 280
- GPU Memory: 24GB GDDR7
- FP32 Performance: 34 TFLOPS
Advanced GPU VPS- RTX Pro 5000
- 60GB RAM
- 24 CPU Cores
- 320GB SSD
- 500Mbps Unmetered Bandwidth
- Once per 2 Weeks Backup
- OS: Windows / Linux
- Dedicated GPU: Nvidia RTX Pro 5000
- CUDA Cores: 14,080
- Tensor Cores: 440
- GPU Memory: 48GB GDDR7
- FP32 Performance: 66.94 TFLOPS
Enterprise GPU VPS- RTX Pro 6000
- 90GB RAM
- 32 CPU Cores
- 400GB SSD
- 1000Mbps Unmetered Bandwidth
- Once per 2 Weeks Backup
- OS: Windows / Linux
- Dedicated GPU: Nvidia RTX Pro 6000
- CUDA Cores: 24,064
- Tensor Cores: 852
- GPU Memory: 96GB GDDR7
- FP32 Performance: 126 TFLOPS
Serverless LLM Hosting (API-Based)
Serverless-V100*3
- OS: Linux
- GPU: GPU: Nvidia V100
- Architecture: Volta
- CUDA Cores: CUDA Cores: 5,120
- Memory: GPU Memory: 3 x 16GB HBM2
- GPU Count: 3
- Best for LLMs under 14B:
- DeepSeek-R1-Distill-Qwen-14B
- DeepSeek-R1-Distill-Llama-8B
- DeepSeek-R1-Distill-Qwen-7B
- Llama-3.1-8B-Instruct
- Qwen3-14B
- ...
Serverless-A40
- OS: Linux
- GPU: GPU: Nvidia A40
- Architecture: Ampere
- CUDA Cores: CUDA Cores: 10,752
- Memory: GPU Memory: 48GB GDDR6
- GPU Count: 1
- Best for LLMs under 14B:
- DeepSeek-R1-Distill-Qwen-14B
- DeepSeek-R1-Distill-Llama-8B
- DeepSeek-R1-Distill-Qwen-7B
- Llama-3.1-8B-Instruct
- Qwen3-14B
- ...
Serverless-A100-40GB
- OS: Linux
- GPU: GPU: Nvidia A100
- Architecture: Ampere
- CUDA Cores: CUDA Cores: 6,912
- Memory: GPU Memory: 40GB HBM2
- GPU Count: 1
- Best for LLMs under 14B:
- DeepSeek-R1-Distill-Qwen-14B
- DeepSeek-R1-Distill-Llama-8B
- DeepSeek-R1-Distill-Qwen-7B
- Llama-3.1-8B-Instruct
- Qwen3-14B
- Gemma-3-12B
- ...
Serverless-A100-80GB
- OS: Linux
- GPU: GPU: Nvidia A100
- Architecture: Ampere
- CUDA Cores: CUDA Cores: 6,912
- Memory: GPU Memory: 80GB HBM2e
- GPU Count: 1
- Best for LLMs under 32B:
- DeepSeek-R1-Distill-Qwen-32B
- Qwen2.5-32B-Instruct
- Qwen3-32B
- Qwen3-14B
- Gemma-3-12B
- ...
Custom your LLMs Server Hosting
FAQs about LLM Hosting
Frequently Asked Questions
Q1: What is the cheapest way to host an LLM?
Small LLMs (e.g., Mistral-7B, LLaMA-13B) can run efficiently on a single private LLM VPS with 16–32GB VRAM. For enterprise-scale models requiring a self hosted LLM setup, renting dedicated GPUs (A100/H100) by the hour or month is often cheaper and offers better data control than public cloud APIs.
Q2: Is serverless LLM hosting suitable for production?
Yes, but mainly for light workloads or rapid prototyping. For enterprise applications with strict data privacy, low latency, and compliance needs, dedicated GPU servers or an on premise LLM deployment are highly recommended.
Q3: Which GPUs are best for a dedicated LLM hosting server?
- NVIDIA H100 – Best for large-scale AI training and heavy inference workloads.
- NVIDIA A100 – Widely available, offering balanced price-performance for a self hosted LLM.
- RTX 4090 / 5090 & A6000 – Extremely cost-efficient for mid-sized LLMs and generative AI tasks.
Q4: Can I deploy GPT-4 or Claude on an on-premise LLM server?
No – proprietary models like GPT-4 and Claude are locked behind APIs. For true data privacy and a self hosted LLM environment, you should run open-source models like LLaMA-3, Qwen-2.5, or Mistral on a dedicated GPU server.
Q5: How do I scale an AI inference server for LLMs?
To handle multiple concurrent requests smoothly, use a multi GPU server configuration, vLLM for efficient serving, and orchestration frameworks like Kubernetes or Ray across your LLM hosting cluster.
Q6. What hardware is best for on-premise LLMs?
High VRAM GPUs like NVIDIA A100, H100, RTX 5090, and RTX A6000 (with 40–80 GB VRAM) are optimal for large parameter models and deep learning tasks.
Q7. How much does LLM hosting cost?
- Serverless LLM APIs: $0.69–$1.69 per hour.
- Dedicated GPU Servers: $129–$3000+ per month depending on the GPU model and whether it's a single or multi GPU server.
Q8. Can I build a self hosted LLM without GPUs?
Small models (7B–13B) can technically run on high-end CPUs or Mac M-series, but deploying an AI inference server with dedicated GPUs is strongly recommended for acceptable speed and throughput.
Q9. What frameworks are best for LLM inference?
- Ollama: Great for personal AI model deployment.
- vLLM: For high-throughput, low-latency serving on your self hosted LLM.
- SGLang: For complex, multi-turn conversations and structured output.
- TensorRT-LLM: For maximizing NVIDIA GPU acceleration.
- TGI (Text Generation Inference): For the Hugging Face ecosystem.