Can RTX 3090 Run LLaMA 3 70B? (VRAM Analysis)

Can RTX 3090 Run LLaMA 3 70B?

Technically yes, but barely. LLaMA 3 70B requires aggressive 4-bit quantization to fit on a single RTX 3090, and you will be severely constrained on context length. The 3090’s 24 GB of VRAM is not enough for FP16 or INT8 inference of a 70B model. If you need reliable 70B inference, a dedicated GPU server with more VRAM or a multi-GPU setup is the better path.

The RTX 3090 remains one of the best value GPUs for AI inference thanks to its 24 GB VRAM and 936 GB/s bandwidth. It excels at running LLaMA 3 8B in full precision. But 70B pushes it right to the edge. Let’s look at the numbers.

The VRAM Math: 24 GB vs 70B Parameters

A 70 billion parameter model requires significant memory. Here is the breakdown at each precision level:

Even at standard 4-bit quantization, 70B does not fit on 24 GB. You need extreme 2-3 bit quantization or partial CPU offloading to make it work. For the complete VRAM picture, see our LLaMA 3 VRAM requirements page.

Quantization Options to Make It Fit

There are two practical approaches to running LLaMA 3 70B on 24 GB:

Option 1: Extreme Quantization (2-bit)

Using GGUF IQ2_XXS or similar ultra-low-bit quantization, the model fits in ~20 GB. However, quality degrades significantly at this level. Expect roughly 80-85% of FP16 quality for simple tasks, with notable degradation on reasoning and complex instructions.

Option 2: GPU + CPU Offloading

Load the model in Q4_K_M and offload excess layers to system RAM. This keeps higher quality but tanks generation speed because CPU inference is much slower than GPU inference.

Neither option is ideal for production. For details on quantization trade-offs, see our GPTQ vs AWQ vs GGUF guide.

Performance: Is It Even Usable?

Realistically, running 70B on a single RTX 3090 gives you:

• 2-bit quantized (all GPU): 8-10 tok/s generation. Usable for experimentation but quality is noticeably worse.
• 4-bit with CPU offload: 2-3 tok/s generation. Painfully slow for interactive use. Viable for batch processing where latency doesn’t matter.
• For comparison: LLaMA 3 8B in FP16 on the same RTX 3090 runs at 40-45 tok/s. The 8B model is where this GPU shines.

Check real-time benchmarks on our tokens per second benchmark page. For a broader GPU comparison, read RTX 3090 vs RTX 5090 for AI.

What Actually Works on RTX 3090

The RTX 3090 is excellent for many LLaMA workloads. Here is where it fits in the lineup:

For production 70B inference, consider our multi-GPU cluster options. Two RTX 3090s with tensor parallelism can run 70B at 4-bit comfortably.

Multi-GPU Option: Two RTX 3090s

With two RTX 3090s (48 GB combined), LLaMA 3 70B becomes much more practical:

• Q4_K_M across 2x 3090: ~15-18 tok/s. Comfortable for interactive use and light API serving.
• INT8 across 2x 3090: ~12-14 tok/s. Better quality with acceptable speed.
• FP16: Still does not fit. Needs 140 GB minimum.

This is often the most cost-effective way to run 70B models. Learn more on our multi-GPU clusters page.

Setup Commands

If you want to attempt LLaMA 3 70B on a single RTX 3090:

Ollama with Partial Offload

# Pull the 70B model (will auto-quantize)
ollama run llama3:70b

llama.cpp with GPU + CPU Split

# Offload 40 of 80 layers to GPU, rest to CPU
./llama-server -m llama-3-70b-Q4_K_M.gguf \
  -ngl 40 -c 2048 --host 0.0.0.0 --port 8080

For the 8B model (recommended for this GPU), see our self-host LLM guide. Also check our vLLM hosting page for optimized serving of the 8B variant.

For cost analysis of running these models yourself versus API providers, see our cost per 1M tokens: GPU vs OpenAI comparison and the cost calculator tool.