LLaMA 3 vs DeepSeek: Which Is Better for Self-Hosting?

LLaMA 3 vs DeepSeek: Overview

Choosing between Meta’s LLaMA 3 and DeepSeek for a dedicated GPU server deployment is one of the most common decisions facing self-hosting teams in 2025. Both model families deliver strong general-purpose reasoning, but they differ significantly in architecture, licensing, and real-world throughput. This guide breaks down every factor that matters when you are deciding which LLM to run on your own hardware.

LLaMA 3, released by Meta, comes in 8B and 70B parameter variants and is widely regarded as the benchmark for open-weight models. DeepSeek, developed by the Chinese AI lab of the same name, offers competitive alternatives including DeepSeek-V2 and the reasoning-focused DeepSeek R1. Both are available under permissive licences suitable for commercial use.

Architecture and Model Sizes

DeepSeek uses a Mixture-of-Experts (MoE) architecture, activating only a fraction of total parameters per token. This makes it surprisingly efficient relative to its headline parameter count. LLaMA 3 uses a traditional dense transformer, meaning every parameter is active on every forward pass. For a deeper look at MoE trade-offs, see our GPU comparisons category.

Performance Benchmarks on GPU

We tested both model families on an NVIDIA RTX 3090 (24 GB VRAM) using vLLM with FP16 and AWQ-4bit quantisation where applicable. Check our tokens-per-second benchmark tool for live numbers.

LLaMA 3 8B leads on raw throughput at the 8B parameter tier, largely because the dense architecture maps efficiently to a single GPU. DeepSeek narrows the gap when you compare quality-adjusted output, especially on reasoning and code tasks. Visit our benchmarks hub for additional GPU-specific data.

VRAM Requirements Compared

VRAM is often the deciding constraint. LLaMA 3 8B fits comfortably on a single 24 GB card at FP16, while the 70B variant requires multi-GPU setups or aggressive quantisation. DeepSeek-V2’s MoE design means the full 236B model needs roughly 120 GB at FP16 but only about 50 GB at 4-bit, making dual RTX 3090 deployments feasible. Consult our guides on LLaMA 3 VRAM requirements and DeepSeek VRAM requirements for full breakdowns.

Self-Hosting Setup and Tooling

Both models are first-class citizens in the two most popular serving frameworks. Our vLLM vs Ollama comparison covers the trade-offs in detail.

# LLaMA 3 8B with vLLM
python -m vllm.entrypoints.openai.api_server \
  --model meta-llama/Meta-Llama-3-8B-Instruct \
  --dtype float16 --max-model-len 8192

# DeepSeek R1 Distill 8B with Ollama
ollama run deepseek-r1:8b

Both frameworks expose an OpenAI-compatible API, so switching between models requires only a config change. Use our cost-per-million-tokens calculator to estimate operating expenses for each option.

Verdict: Which Should You Host?

Choose LLaMA 3 if you want maximum tokens-per-second on a single GPU, broad ecosystem support, and the simplest deployment path. The 8B model is ideal for latency-sensitive applications on a budget card.

Choose DeepSeek if you prioritise reasoning depth, longer context windows, or multilingual coverage. The MoE architecture gives you a larger effective model for the same VRAM budget once you move beyond single-GPU setups.

For a broader view of the landscape, read our complete self-host LLM guide. You can also compare these models against Mistral in our DeepSeek vs Mistral breakdown.