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Llama 3.1 is a new state-of-the-art model from Meta. You'll get pre-installed Open WebUI + Ollama + Llama3.1-70B, it is a popluar way to self-hosted LLM models.
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Llama 3.2 Vision is a collection of instruction-tuned image reasoning generative models. You'll get pre-installed Open WebUI + Ollama + Llama3.2-Vison-90B, it is a popluar way to self-hosted LLM models.
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The Llama 4 collection of models are natively multimodal AI models that enable text and multimodal experiences. You'll get pre-installed Open WebUI + Ollama + Llama4-16x17B, it is a popluar way to self-hosted LLM models.
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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 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 | -- |
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 Name
|
Size (16-bit Quantization)
|
Recommended GPUs
|
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-Instructmeta-llama/Llama-3.1-70Bmeta-llama/Meta-Llama-3-70B-Instruct | 132GB | 4*A100-40gb, 2*A100-80gb, 2*H100 | 50 | ~295.52-990.61 |
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.
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).
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.
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.
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