Can RTX 4060 Run LLaMA 3? (Benchmarks + Setup Guide)

Can RTX 4060 Run LLaMA 3? The Verdict

Yes, the RTX 4060 can run LLaMA 3 8B with 4-bit quantization at roughly 18-22 tokens per second. That is fast enough for interactive chat and development work. The RTX 4060 has 8 GB of GDDR6X VRAM with 272 GB/s bandwidth, making it a solid budget option for running the smallest LLaMA 3 variant on a dedicated GPU server.

However, LLaMA 3 70B and 405B are completely out of reach. The 4060’s 8 GB cannot fit these models even with extreme quantization. And FP16 inference of the 8B model requires 16 GB, so quantization is mandatory.

VRAM Breakdown: 8 GB vs LLaMA 3 Requirements

Here is how each LLaMA 3 variant’s VRAM requirements compare against the RTX 4060’s 8 GB:

The 4-bit quantized 8B model uses approximately 5.5 GB for weights, leaving 2.5 GB for KV cache and runtime overhead. This is enough for context lengths up to 4096 tokens comfortably. See our full LLaMA 3 VRAM requirements breakdown for all configurations.

Real Benchmarks: Tokens Per Second on RTX 4060

The RTX 4060 benefits from Ada Lovelace architecture improvements over Ampere GPUs at the same VRAM tier. Here are measured performance numbers:

At 18-22 tok/s, the RTX 4060 delivers a comfortable chat experience. Compare this against other GPUs using our tokens per second benchmark tool. For a direct comparison with the 3090, see our RTX 4060 vs 3090 for AI workloads analysis.

Best Quantization Options for 8 GB

Quantization quality matters when you are constrained to 8 GB. Here are the recommended options ranked by quality:

For most users, Q4_K_M via Ollama is the simplest path. For production APIs, AWQ with vLLM provides better throughput. Learn more in our GPTQ vs AWQ vs GGUF quantization guide.

What Can You Actually Run?

Realistic use cases for LLaMA 3 on an RTX 4060:

• LLaMA 3 8B Q4_K_M: Works well. 18-22 tok/s. Good for development, testing, personal chatbots, and light API serving.
• LLaMA 3 8B Q5_K_M: Works. 17-19 tok/s with 2048-3072 context. Better quality for tasks needing accuracy.
• LLaMA 3 8B FP16: Does not fit. Requires 16 GB.
• LLaMA 3 70B (any quant): Does not fit. Minimum 38 GB even at 4-bit.
• Concurrent users: Single user only. No batch inference headroom.

The RTX 4060 is roughly 30-40% faster than an RTX 3050 at the same VRAM tier due to higher bandwidth and Ada Lovelace efficiency. See our RTX 3050 LLaMA 3 analysis for comparison.

Setup Guide (Ollama + llama.cpp)

Get LLaMA 3 8B running on your RTX 4060 server in under two minutes:

Ollama (Fastest Setup)

# Install and run LLaMA 3 8B
curl -fsSL https://ollama.com/install.sh | sh
ollama run llama3:8b

# For API access
ollama serve &
curl http://localhost:11434/api/generate -d '{
  "model": "llama3:8b",
  "prompt": "Hello, how are you?"
}'

llama.cpp (More Control)

# Run server with GPU offloading
./llama-server -m llama-3-8b-instruct-Q4_K_M.gguf \
  -ngl 33 -c 4096 --host 0.0.0.0 --port 8080

For full deployment walkthroughs, see our self-host LLM guide and Ollama hosting documentation.

When to Upgrade: RTX 4060 vs Bigger GPUs

The RTX 4060 is a capable entry point, but here is when you should consider upgrading:

If you need to run LLaMA 3 70B, the minimum viable path is an RTX 3090 with 4-bit quantization, though performance will be limited. See our RTX 3090 LLaMA 3 70B analysis for details. For cost comparisons, use our cost per million tokens calculator.