GPU HEAD-TO-HEAD

NVIDIA RTX 3090 vs NVIDIA RTX 4070 Ti Super

Which graphics card is better for running local language models? Here is how the NVIDIA RTX 3090 (24 GB) compares against the NVIDIA RTX 4070 Ti Super (16 GB) in real memory headroom, supported model architectures, and generation speed.

NVIDIA · Ampere 24 GB VRAM

NVIDIA RTX 3090

Memory Bandwidth: 936 GB/s (GDDR6X 384-bit)
Fits fully in VRAM: 37 popular models
Power Draw (TDP): 350W
Largest recommended (Q4): 32B–34B

View all NVIDIA RTX 3090 models →

NVIDIA · Ada Lovelace 16 GB VRAM

NVIDIA RTX 4070 Ti Super

Memory Bandwidth: 672 GB/s (GDDR6X 256-bit)
Fits fully in VRAM: 35 popular models
Power Draw (TDP): 285W
Largest recommended (Q4): 20B–22B

View all NVIDIA RTX 4070 Ti Super models →

The AI Local Check Verdict: Which GPU should you buy for AI?

The NVIDIA RTX 3090 is the clear winner for local AI. It provides both 8 GB more VRAM and higher memory bandwidth (936 GB/s vs 672 GB/s).

This allows it to run larger model architectures entirely in video memory while also delivering faster token generation speeds on models of all sizes.

Models that fit on NVIDIA RTX 3090, but NOT on NVIDIA RTX 4070 Ti Super

These models run entirely in GPU VRAM on the NVIDIA RTX 3090, but require slow system RAM offload on the NVIDIA RTX 4070 Ti Super:

Model	Size	Downloads	NVIDIA RTX 3090	NVIDIA RTX 4070 Ti Super
ggml-org/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF	31.58B	180,114	Fits in VRAM	Offload (Slow)
unsloth/Qwen3-Coder-Next-GGUF	79.67B	138,575	Fits in VRAM	Offload (Slow)

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