Can RTX 3090 Run LLaMA 3 8B in FP16?

Yes, the RTX 3090 runs LLaMA 3 8B in full FP16 with room to spare. With 24GB GDDR6X VRAM, the RTX 3090 loads the complete unquantised model and still has enough headroom for a generous context window. For LLaMA hosting at maximum quality, this is the go-to consumer GPU.

The Short Answer

YES. Full FP16 precision with 8K+ context and excellent throughput.

LLaMA 3 8B needs approximately 16.1GB of VRAM for its weights in FP16. The RTX 3090 with 24GB leaves roughly 8GB for KV cache and runtime overhead. That 8GB of headroom translates to a context window of approximately 16K-24K tokens depending on the serving framework, far beyond the model’s standard 8192-token context.

Running in FP16 means zero quality loss from quantisation. Every weight is at its original trained precision, which matters for tasks requiring nuanced reasoning, coding assistance, or instruction following. This is the configuration where LLaMA 3 8B performs at its published benchmark levels.

VRAM Analysis

At 8K context in FP16, you use about 18GB out of 24GB, leaving comfortable headroom. You can push to 16K context for longer document processing. At 32K, you are at the card’s absolute limit and may see OOM during generation spikes. See our LLaMA 3 VRAM requirements guide for all scenarios.

Performance Benchmarks

At 42 tok/s in FP16, the RTX 3090 delivers fast, responsive inference with zero quality compromise. The 3090’s 936 GB/s memory bandwidth feeds the model weights efficiently during generation. Interestingly, INT8 is slightly slower on this card because the Ampere architecture’s INT8 tensor cores have different throughput characteristics than Ada Lovelace. Full data on our tokens per second benchmark page.

Setup Guide

For FP16 inference on the RTX 3090, vLLM is the production-grade option:

# vLLM: Full FP16 serving
pip install vllm
vllm serve meta-llama/Meta-Llama-3-8B-Instruct \
  --dtype float16 \
  --max-model-len 8192 \
  --gpu-memory-utilization 0.90 \
  --host 0.0.0.0 --port 8000

This gives you an OpenAI-compatible API with continuous batching and PagedAttention. For quick testing with Ollama:

# Ollama: FP16 (Ollama uses GGUF F16 format)
ollama run llama3:8b-instruct-fp16

No quantisation flags, no memory hacks, no offloading. The model loads cleanly into 24GB and runs at full speed. This is the simplicity that 24GB VRAM buys you.

Recommended Alternative

The RTX 3090 is excellent for LLaMA 3 8B in FP16. The main reasons to upgrade would be for the 70B model or for faster throughput. The RTX 5090 with 32GB can run LLaMA 3 70B in INT4 if you need the larger model, and it delivers 75+ tok/s on the 8B in FP16.

For other workloads on the 3090, check whether it can run Mixtral 8x7B, run Whisper Large-v3, or run CodeLlama 34B. For combined workloads, see the SDXL plus LLM analysis. Browse all configurations on our dedicated GPU servers page or read the best GPU for LLM inference guide.