Ollama on RTX 4060: Budget LLM Serving Guide

The Budget Case for the RTX 4060

The RTX 4060 is the most affordable entry point for self-hosted AI inference. With 8GB GDDR6 VRAM on a dedicated GPU server, it runs quantised 7B-8B models at interactive speeds — enough for personal chatbots, development environments, and low-traffic production endpoints. The monthly cost is significantly lower than API pricing for consistent workloads, as our GPU vs API cost comparison demonstrates.

Ollama is the ideal serving framework for the 4060. Its automatic GGUF quantisation support and simple CLI mean you can be running an LLM in under five minutes without worrying about precision formats or memory management.

What Fits in 8GB VRAM

The RTX 4060 is a solid 7B-class GPU. Anything at 7B-8B in Q4 quantisation fits with room for KV cache. Models above 8B generally need more VRAM. For a direct comparison, see our best GPU for LLM inference roundup.

Setup Guide

# Install Ollama on your dedicated server
curl -fsSL https://ollama.com/install.sh | sh

# Run Llama 3 8B — downloads and starts automatically
ollama run llama3:8b

# For a smaller, faster model
ollama run phi3:mini

# Expose the API for remote access
OLLAMA_HOST=0.0.0.0 ollama serve

By default, Ollama binds to localhost. Set OLLAMA_HOST=0.0.0.0 to accept remote connections, then secure it behind an nginx reverse proxy with TLS. See the secure AI inference API guide for the full setup.

Performance Expectations

At 40-45 tokens/s, the RTX 4060 delivers a fast typing speed for single-user chat. Context length is limited because 8GB leaves less room for KV cache after the model loads. Keep prompts concise for best results.

Optimisation Tips for 8GB

Maximise the 4060’s limited VRAM with these Ollama settings:

# Create a Modelfile to limit context and save VRAM
cat > Modelfile << 'EOF'
FROM llama3:8b
PARAMETER num_ctx 2048
PARAMETER num_gpu 99
EOF

ollama create llama3-compact -f Modelfile
ollama run llama3-compact

Reducing num_ctx from the default 4096 to 2048 frees approximately 500MB of VRAM, which can prevent out-of-memory errors on tight models. Use num_gpu 99 to ensure full GPU offloading rather than falling back to CPU layers.

When to Upgrade

Upgrade from the RTX 4060 when you need 13B+ models, FP16 quality without quantisation, longer context windows, or multi-user serving. The RTX 4060 to RTX 3090 upgrade triples your VRAM to 24GB at a modest price increase. For the latest generation, the RTX 5080 upgrade path brings Blackwell architecture with 16GB GDDR7.

Browse more model-specific guides in the tutorials section and estimate your monthly costs with the LLM cost calculator. For production deployments beyond Ollama, see our self-hosting LLM guide.