Yes, the RTX 4060 Ti runs LLaMA 3 8B very well. With 16GB GDDR6 VRAM, the RTX 4060 Ti handles this model in INT8 with a full 8192-token context window and strong throughput. For LLaMA hosting, this is one of the best value cards for the 8B parameter class of models.
The Short Answer
YES. The RTX 4060 Ti runs LLaMA 3 8B comfortably in INT8 and INT4, with FP16 as a tight fit.
LLaMA 3 8B has 8.03 billion parameters. In FP16, the model weights consume approximately 16.1GB, which is right at the RTX 4060 Ti’s 16GB limit and leaves almost nothing for KV cache. In INT8, weights drop to roughly 8.5GB, leaving 7.5GB for context and overhead. In INT4 (GPTQ/AWQ), weights need only about 5GB, giving you the most headroom.
The sweet spot for this card is INT8 quantisation, which preserves nearly all of LLaMA 3 8B’s quality while fitting comfortably with generous context. This is genuinely a good pairing of GPU and model.
VRAM Analysis
| Quantisation | Model VRAM | KV Cache (8K ctx) | Total | RTX 4060 Ti (16GB) |
|---|---|---|---|---|
| FP16 | ~16.1GB | ~2.0GB | ~18.1GB | OOM with context |
| INT8 (GPTQ) | ~8.5GB | ~2.0GB | ~10.5GB | Fits well |
| INT4 (AWQ) | ~5.0GB | ~2.0GB | ~7.0GB | Fits easily |
| Q4_K_M (GGUF) | ~4.9GB | ~2.0GB | ~6.9GB | Fits easily |
| Q5_K_M (GGUF) | ~5.7GB | ~2.0GB | ~7.7GB | Fits easily |
For INT8 with 8K context, total VRAM usage sits around 10.5GB out of 16GB, leaving headroom for concurrent tasks or longer context. FP16 technically loads but the KV cache at longer contexts will push it into OOM. See our LLaMA 3 VRAM requirements guide for every quantisation level.
Performance Benchmarks
| GPU | Quantisation | Tokens/sec (output) | Context |
|---|---|---|---|
| RTX 4060 Ti (16GB) | INT8 | ~35 tok/s | 8192 |
| RTX 4060 Ti (16GB) | Q4_K_M | ~48 tok/s | 8192 |
| RTX 4060 (8GB) | Q4_K_M | ~28 tok/s | 4096 |
| RTX 3090 (24GB) | FP16 | ~42 tok/s | 8192 |
| RTX 5080 (16GB) | INT8 | ~55 tok/s | 8192 |
At 35 tok/s in INT8 and 48 tok/s in INT4, the RTX 4060 Ti delivers smooth, responsive inference for LLaMA 3 8B. This is well above the 20 tok/s threshold for comfortable interactive chat. The Ada Lovelace architecture handles quantised inference efficiently. Full comparisons are on our tokens per second benchmark page.
Setup Guide
The fastest path to running LLaMA 3 8B on the RTX 4060 Ti is with vLLM for production or Ollama for quick testing:
# Option 1: Ollama (simplest)
ollama run llama3:8b-instruct-q8_0
# Option 2: vLLM for production serving
pip install vllm
vllm serve meta-llama/Meta-Llama-3-8B-Instruct \
--quantization awq \
--max-model-len 8192 \
--gpu-memory-utilization 0.90 \
--host 0.0.0.0 --port 8000vLLM provides an OpenAI-compatible API endpoint, continuous batching, and PagedAttention for efficient memory use. For INT8 with vLLM, use --quantization gptq with a pre-quantised model. The --gpu-memory-utilization 0.90 flag tells vLLM to use up to 90% of available VRAM, which is safe on a dedicated server.
Recommended Alternative
The RTX 4060 Ti is genuinely well-suited for LLaMA 3 8B. If you want FP16 precision with full context or plan to run larger LLaMA variants (70B), step up to the RTX 3090 with 24GB which runs 8B in full FP16 with room for 32K context.
For the same card with different workloads, check whether the RTX 4060 Ti can run SDXL or the RTX 4060 Ti can run DeepSeek. If you are evaluating the base 4060, see our RTX 4060 DeepSeek analysis to understand the VRAM difference. Browse all dedicated GPU servers and compare in our best GPU for LLM inference guide.
Deploy This Model Now
Dedicated GPU servers with the VRAM you need. UK datacenter, full root access.
Browse GPU Servers
