LLM Hosting，Serverless LLM and Self-hosted LLMs
LLM hosting refers to deploying and running Large Language Models (LLMs) such as DeepSeek, LLaMA, GPT, Mistral, or Gemma on infrastructure optimized for inference or fine-tuning. It can be done through cloud APIs (serverless LLM hosting), dedicated GPU servers, or self-hosted solutions. The right choice depends on workload size, latency needs, compliance requirements, and budget.
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
- 50% off for the first month, 25% off for every renewals.
Multi-GPU Dedicated Server - 2xA100
- 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
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
Multi-GPU Dedicated Server - 2xRTX A5000
- 128GB RAM
- GPU: 2 x Quadro RTX A5000
- Dual 12-Core E5-2697v2
- 240GB SSD + 2TB SSD
- 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
Multi-GPU Dedicated Server - 2xRTX A4000
- 128GB RAM
- GPU: 2 x Nvidia RTX A4000
- Dual 12-Core E5-2697v2
- 240GB SSD + 2TB SSD
- 1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ampere
- CUDA Cores: 6144
- Tensor Cores: 192
- GPU Memory: 16GB GDDR6
- FP32 Performance: 19.2 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)
Express GPU VPS - GT730
- 8GB RAM
- Dedicated GPU: GeForce GT730
- 6 CPU Cores
- 120GB SSD
- 100Mbps Unmetered Bandwidth
- OS: Linux / Windows 10/ Windows 11
- Once per 4 Weeks Backup
- Single GPU Specifications:
- CUDA Cores: 384
- GPU Memory: 2GB DDR3
- FP32 Performance: 0.692 TFLOPS
Express GPU VPS - K620
- 12GB RAM
- Dedicated GPU: Quadro K620
- 9 CPU Cores
- 160GB SSD
- 100Mbps Unmetered Bandwidth
- OS: Linux / Windows 10/ Windows 11
- Once per 4 Weeks Backup
- Single GPU Specifications:
- CUDA Cores: 384
- GPU Memory: 2GB DDR3
- FP32 Performance: 0.863 TFLOPS
Professional GPU VPS - A4000
- 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 VPS - RTX 5090
- 96GB RAM
- 32 CPU Cores
- 400GB SSD
- 500Mbps Unmetered Bandwidth
- Once per 2 Weeks Backup
- OS: Linux / Windows 10/ Windows 11
- Dedicated GPU: GeForce RTX 5090
- CUDA Cores: 21,760
- Tensor Cores: 680
- GPU Memory: 32GB GDDR7
- FP32 Performance: 109.7 TFLOPS
Serverless LLM Hosting (API-Based)
Serverless-V100*3
- OS: Linux
- GPU: Nvidia V100
- Architecture: Volta
- CUDA Cores: 5,120
- 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: Nvidia A40
- Architecture: Ampere
- CUDA Cores: 10,752
- 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: Nvidia A100
- Architecture: Ampere
- CUDA Cores: 6,912
- 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: Nvidia A100
- Architecture: Ampere
- CUDA Cores: 6,912
- 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
Q1: What is the cheapest way to host an LLM?
Small LLMs (e.g., Mistral-7B, LLaMA-13B) can run on a single GPU VPS with 16–32GB VRAM, or even locally with quantization. For enterprise-scale models, renting GPUs (A100/H100) by the hour is often cheaper than cloud APIs.
Q2: Is serverless LLM suitable for production?
Yes, but mainly for light workloads or prototypes. For enterprise apps with strict latency and compliance needs, dedicated GPU servers are better.
Q3: Which GPUs are best for LLM hosting?
- NVIDIA H100 – best for large-scale inference and training
- NVIDIA A100 – widely available, balanced price-performance
- RTX 4090/5090 & A6000 – cost-efficient for mid-sized LLMs and generative tasks
Q4: Can I host GPT-4 or Claude on my own server?
No – proprietary models like GPT-4 and Claude are only available via API. For self-hosting, you can run open-source LLMs such as LLaMA-3, Qwen-2.5, or Mistral.
Q5: How do I scale LLM inference?
Use multi-GPU parallelism, vLLM for efficient serving, and orchestration frameworks like Kubernetes or Ray to handle multiple requests concurrently.
Q6. What hardware is best for LLM hosting?
- NVIDIA A100, H100, RTX 5090，and A6000 GPUs with 40–80 GB VRAM are optimal for large models.
Q7. How much does LLM hosting cost?
- Serverless APIs: $0.69–$1.69 per hour
- GPU Servers: $129–$3000 per month depending on GPU type
Q8. Can I self-host LLMs without GPUs?
- Small models (7B–13B) can run on high-end CPUs or Mac M-series, but GPUs are strongly recommended.
Q9. What frameworks are best for LLM inference?
- Ollama your personal AI model tool
- vLLM for high-throughput, low-latency serving for single-turn requests
- SGLang for complex, multi-turn conversations and structured output generation
- TensorRT-LLM for NVIDIA GPU acceleration
- TGI (Text Generation Inference) for Hugging Face ecosystem