Samprix

What AI can the GeForce RTX 5060 Ti 8 GB run?

The GeForce RTX 5060 Ti 8 GB can give a local model 8 GB of memory. At a 4K context, 23 of the 42 models we track fit entirely on it; the largest is Qwen3 8B.

NVIDIA Specs last checked: how
Your computer
Context length
System RAM
Memory a model can use 8 GB 8 GB of VRAM · source

Models on this machine

  • Model file
  • Context
  • Allowance
  • Your GPU memory

Fits

23
  • Qwen3 8B
    Alibaba · Q4_K_M
    Needs 5.9 GB 2.1 GB spare
    Context up to
    19,348
  • DeepSeek-R1 0528 8B
    DeepSeek · Q4_K_M
    Needs 5.9 GB 2.1 GB spare
    Context up to
    19,348
  • DeepSeek-R1 Distill 8B
    DeepSeek · Q4_K_M
    Needs 5.6 GB 2.4 GB spare
    Context up to
    24,055
  • Dolphin 3.0 8B
    Cognitive Computations · Q4_K_M
    Needs 5.6 GB 2.4 GB spare
    Context up to
    24,055
  • Qwen2.5 7B
    Alibaba · Q4_K_M
    Needs 5.1 GB 2.9 GB spare
    Context up to
    32,768
  • Qwen2.5 Coder 7B
    Alibaba · Q4_K_M
    Needs 5.1 GB 2.9 GB spare
    Context up to
    32,768
  • DeepSeek-R1 Distill 7B
    DeepSeek · Q4_K_M
    Needs 5.1 GB 2.9 GB spare
    Context up to
    58,472
  • OLMo 2 7B
    Allen Institute for AI · Q4_K_M
    Needs 6.7 GB 1.3 GB spare
    Context up to
    4,096
  • Mistral 7B
    Mistral AI · Q4_K_M
    Needs 5.1 GB 2.9 GB spare
    Context up to
    27,870
  • Qwen3 4B
    Alibaba · Q4_K_M
    Needs 3.5 GB 4.5 GB spare
    Context up to
    36,980
  • Phi-4 mini 3.8B
    Microsoft · Q4_K_M
    Needs 3.3 GB 4.7 GB spare
    Context up to
    42,366
  • Phi-3 mini 3.8B
    Microsoft · Q4_K_M
    Needs 4.2 GB 3.8 GB spare
    Context up to
    4,096
  • Qwen2.5 3B
    Alibaba · Q4_K_M
    Needs 2.4 GB 5.6 GB spare
    Context up to
    32,768
  • Qwen2.5 Coder 3B
    Alibaba · Q4_K_M
    Needs 2.4 GB 5.6 GB spare
    Context up to
    32,768
  • Qwen3 1.7B
    Alibaba · Q4_K_M
    Needs 2.2 GB 5.8 GB spare
    Context up to
    40,960
  • DeepSeek-R1 Distill 1.5B
    DeepSeek · Q4_K_M
    Needs 1.6 GB 6.4 GB spare
    Context up to
    131,072
  • SmolLM2 1.7B
    Hugging Face · Q4_K_M
    Needs 2.3 GB 5.7 GB spare
    Context up to
    8,192
  • Qwen2.5 1.5B
    Alibaba · Q4_K_M
    Needs 1.5 GB 6.5 GB spare
    Context up to
    32,768
  • Qwen2.5 Coder 1.5B
    Alibaba · Q4_K_M
    Needs 1.5 GB 6.5 GB spare
    Context up to
    32,768
  • TinyLlama 1.1B
    TinyLlama · Q4_K_M
    Needs 1.2 GB 6.8 GB spare
    Context up to
    2,048
  • Qwen3 0.6B
    Alibaba · Q4_K_M
    Needs 1.4 GB 6.6 GB spare
    Context up to
    40,960
  • Qwen2.5 0.5B
    Alibaba · Q4_K_M
    Needs 0.9 GB 7.1 GB spare
    Context up to
    32,768
  • SmolLM2 360M
    Hugging Face · Q4_K_M
    Needs 0.9 GB 7.1 GB spare
    Context up to
    8,192

Won’t fit

19
Memory needed = model file + context + a 0.5 GB allowance (our estimate). See methodology.

Frequently asked questions

What’s the biggest AI model the GeForce RTX 5060 Ti 8 GB can run?

Qwen3 8B (Q4_K_M, a 5.2 GB download) is the largest of the 42 models we track that fits entirely in the 8 GB the GeForce RTX 5060 Ti 8 GB can give a model at a 4K context, with 2.1 GB to spare and room for up to 19,348 tokens of context.

Can the GeForce RTX 5060 Ti 8 GB run Mistral Nemo 12B?

Not entirely in its own memory. Mistral Nemo 12B (Q4_K_M) needs 8.1 GB at a 4K context, and the GeForce RTX 5060 Ti 8 GB can give a model 8 GB. With enough system RAM the rest can spill over into it, which works but is much slower; pick your RAM above to check.

How much memory can the GeForce RTX 5060 Ti 8 GB give an AI model?

All 8 GB of its VRAM, per NVIDIA GeForce graphics card comparison (checked Oct 2, 2026). The model file, its context and the program running it all have to fit in that.

Which other graphics cards or Macs run the same models?

GeForce RTX 5060, GeForce RTX 5050, GeForce RTX 4060 Ti 8 GB and GeForce RTX 4060 have the same 8 GB of VRAM, so the same models fit. How fast they run differs, but makers don’t publish speed figures we could cite.

How fast will local AI run on the GeForce RTX 5060 Ti 8 GB?

Hardware makers and model publishers don’t publish tokens-per-second figures, so there is no source we could cite. We only show whether a model fits and how much context it leaves room for.

Other graphics cards and Macs

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