12 GB at 504 GB/s → 12 GB at 672 GB/s

Same 12 GB either way. GeForce RTX 4070 to GeForce RTX 5070 does not unlock a larger catalog model at 8K, it raises bandwidth. Mistral Nemo 12B Instruct moves from 31–54 tok/s to 42–72 tok/s.

Hands installing a graphics card into a PC motherboard slot

GeForce RTX 4070 and GeForce RTX 5070, same 8K context and recommended quant, 32 GB system RAM.

ModelGeForce RTX 4070GeForce RTX 5070Speed beforeSpeed after
Llama 3.2 1B InstructFitsFits258–499 tok/s343–666 tok/s
Llama 3.2 3B InstructFitsFits94–200 tok/s125–266 tok/s
Llama 3.1 8B InstructFitsFits46–82 tok/s62–109 tok/s
Llama 3.3 70B InstructNoNo——
Qwen2.5 7B InstructFitsFits54–86 tok/s72–115 tok/s
Qwen2.5 14B InstructTightTight26–45 tok/s35–60 tok/s
Qwen2.5 32B InstructOffloadOffload1–2 tok/s2–4 tok/s
Qwen2.5 72B InstructNoNo——
Qwen3 8BFitsFits44–80 tok/s59–107 tok/s
Qwen3 14BTightTight27–45 tok/s36–60 tok/s
Qwen3 32BOffloadOffload1–2 tok/s2–4 tok/s
Qwen3 30B-A3BOffloadOffload15–22 tok/s29–42 tok/s
Qwen3.5 9BFitsFits46–71 tok/s62–95 tok/s
Qwen3.8 27BOffloadOffload2–4 tok/s5–7 tok/s
Qwen3.6 35B-A3BOffloadOffload13–18 tok/s25–36 tok/s
Gemma 3 4B InstructFitsFits100–162 tok/s133–216 tok/s
Gemma 3 12B InstructFitsFits34–55 tok/s45–74 tok/s
Gemma 3 27B InstructOffloadOffload2–3 tok/s4–6 tok/s
Gemma 4 E2BFitsFits86–130 tok/s114–173 tok/s
Gemma 4 E4BFitsFits53–81 tok/s70–108 tok/s
Gemma 4 12BFitsFits37–57 tok/s49–75 tok/s
Gemma 4 26B-A4BOffloadOffload17–25 tok/s33–47 tok/s
Gemma 4 31BOffloadOffload2 tok/s3–5 tok/s
gpt-oss-20bOffloadOffload105–153 tok/s140–204 tok/s
gpt-oss-120bNoNo——
Phi-4TightTight26–45 tok/s34–59 tok/s
Phi-4-mini InstructFitsFits78–162 tok/s104–216 tok/s
Mistral Nemo 12B InstructFitsFits31–54 tok/s42–72 tok/s
Mistral Small 3.2 24B InstructOffloadOffload3–5 tok/s6–9 tok/s
Devstral Small 2 24B InstructOffloadOffload3–5 tok/s6–9 tok/s
DeepSeek-R1 Distill Qwen 14BTightTight26–45 tok/s35–60 tok/s
DeepSeek-R1 Distill Qwen 32BOffloadOffload1–2 tok/s2–4 tok/s
SmolLM3 3BFitsFits110–211 tok/s147–281 tok/s

FAQ

What counts as an upgrade here?

A model newly reaching Fits, or a higher tokens-per-second range when VRAM does not change. Gaming FPS is a different question.

Are the two cards measured the same way?

Both use 8,192 tokens, the recommended quant, and 32 GB of assumed system RAM. Bandwidth is 504 GB/s versus 672 GB/s.

Should I buy this for local AI?

Buy VRAM first when a model you want is in Offload or No. Buy bandwidth when the model already fits and you want more tokens per second.