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.
| 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 | -- |
| 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 |
Express GPU Dedicated Server - P1000
Basic GPU Dedicated Server - T1000
Basic GPU Dedicated Server - GTX 1650
Basic GPU Dedicated Server - GTX 1660
Advanced GPU Dedicated Server - V100
Professional GPU Dedicated Server - RTX 2060
Advanced GPU Dedicated Server - RTX 2060
Advanced GPU Dedicated Server - RTX 3060 Ti
Professional GPU VPS - A4000
Advanced GPU Dedicated Server - A4000
Advanced GPU Dedicated Server - A5000
Enterprise GPU Dedicated Server - A40
Basic GPU Dedicated Server - RTX 5060
Enterprise GPU Dedicated Server - RTX 5090
Enterprise GPU Dedicated Server - A100
Enterprise GPU Dedicated Server - A100(80GB)
Enterprise GPU Dedicated Server - H100
Multi-GPU Dedicated Server- 2xRTX 4090
Multi-GPU Dedicated Server- 2xRTX 5090
Multi-GPU Dedicated Server - 2xA100
Multi-GPU Dedicated Server - 2xRTX 3060 Ti
Multi-GPU Dedicated Server - 2xRTX 4060
Multi-GPU Dedicated Server - 2xRTX A5000
Multi-GPU Dedicated Server - 2xRTX A4000
Multi-GPU Dedicated Server - 3xRTX 3060 Ti
Multi-GPU Dedicated Server - 3xV100
Multi-GPU Dedicated Server - 3xRTX A5000
Multi-GPU Dedicated Server - 3xRTX A6000
Multi-GPU Dedicated Server - 4xA100
Multi-GPU Dedicated Server - 4xRTX A6000
Multi-GPU Dedicated Server - 8xV100
Multi-GPU Dedicated Server - 8xRTX A6000
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
✅ 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
✅ 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)
| 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 |
