GPU HEAD-TO-HEAD

NVIDIA RTX 4080 Super vs NVIDIA RTX 5060

Which graphics card is better for running local language models? Here is how the NVIDIA RTX 4080 Super (16 GB) compares against the NVIDIA RTX 5060 (8 GB) in real memory headroom, supported model architectures, and generation speed.

NVIDIA · Ada Lovelace 16 GB VRAM

NVIDIA RTX 4080 Super

  • Memory Bandwidth: 736 GB/s (GDDR6X 256-bit)
  • Fits fully in VRAM: 34 popular models
  • Power Draw (TDP): 320W
  • Largest recommended (Q4): 20B–22B
View all NVIDIA RTX 4080 Super models →
NVIDIA · Desktop 8 GB VRAM

NVIDIA RTX 5060

  • Memory Bandwidth: 256 GB/s (GDDR6)
  • Fits fully in VRAM: 27 popular models
  • Power Draw (TDP): 150W
  • Largest recommended (Q4): 8B
View all NVIDIA RTX 5060 models →

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

The NVIDIA RTX 4080 Super is the clear winner for local AI. It provides both 8 GB more VRAM and higher memory bandwidth (736 GB/s vs 256 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 4080 Super, but NOT on NVIDIA RTX 5060

These models run entirely in GPU VRAM on the NVIDIA RTX 4080 Super, but require slow system RAM offload on the NVIDIA RTX 5060:

Model Size Downloads NVIDIA RTX 4080 Super NVIDIA RTX 5060
unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF 30.53B 10,762,547 Fits in VRAM Offload (Slow)
unsloth/gpt-oss-20b-GGUF 20.91B 501,909 Fits in VRAM Offload (Slow)
unsloth/Qwen-AgentWorld-35B-A3B-GGUF 34.66B 373,970 Fits in VRAM Offload (Slow)
bartowski/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF 34.66B 360,510 Fits in VRAM Offload (Slow)
MaziyarPanahi/Qwen3-30B-A3B-GGUF 30.53B 260,445 Fits in VRAM Offload (Slow)
MaziyarPanahi/Qwen3-32B-GGUF 32.76B 259,844 Fits in VRAM Offload (Slow)
bartowski/Qwen2.5-32B-Instruct-GGUF 32.76B 216,296 Fits in VRAM Offload (Slow)

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