LLaMA Hosting with Ollama — GPU Recommendation
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 < 2*A100-40gb < A100-80gb < H100 < 2*RTX5090 | 13.15-26.85 |
llama3.3:70b, llama3.1:70b | 43GB | A40 < A6000 < 2*A100-40gb < A100-80gb < H100 < 2*RTX5090 | 13.15-26.85 |
llama3.2-vision:90b | 55GB | 2*A100-40gb < A100-80gb < H100 < 2*RTX5090 | ~12-20 |
llama4:16x17b | 67GB | 2*A100-40gb < A100-80gb < H100 | ~10-18 |
llama3.1:405b | 243GB | 8*A6000 < 4*A100-80gb < 4*H100 | -- |
llama4:128x17b | 245GB | 8*A6000 < 4*A100-80gb < 4*H100 | -- |
LLaMA Hosting with vLLM + Hugging Face — GPU Recommendation
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 | 4*A100-40gb, 2*A100-80gb, 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 | 4*A100-40gb, 2*A100-80gb, 2*H100 | 50 | ~295.52-990.61 |
- 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
- 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
Basic GPU Dedicated Server - T1000
- 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
Basic GPU Dedicated Server - GTX 1650
- 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
Basic GPU Dedicated Server - GTX 1660
- 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
- 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
- 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
Advanced GPU Dedicated Server - RTX 2060
- 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
- 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
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 Dedicated Server - A4000
- 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
Advanced GPU Dedicated Server - A5000
- 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
Enterprise GPU Dedicated Server - A40
- 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
Basic GPU Dedicated Server - RTX 5060
- 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
Enterprise GPU Dedicated Server - RTX 5090
- 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
- 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
Enterprise GPU Dedicated Server - A100(80GB)
- 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
Enterprise GPU Dedicated Server - H100
- 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
- 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
Multi-GPU Dedicated Server- 2xRTX 5090
- 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
Multi-GPU Dedicated Server - 2xA100
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
Multi-GPU Dedicated Server - 4xA100
- 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
- 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
- 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
- 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?
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
vLLM Benchmark for LLaMA
How to Deploy Llama LLMs with Ollama/vLLM
Install and Run Meta LLaMA Locally with Ollama >
Install and Run Meta LLaMA Locally with vLLM v1 >
What Does Meta LLaMA Hosting Stack Include?
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
High memory bandwidth and VRAM are essential
Inference engines optimize GPU usage
Production LLaMA hosting needs orchestration and scalability
Self-hosted Llama Hosting vs. Llama as a Service
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 |