Is the RTX 4070 Super good for local LLMs?

Yes — for 7B–13B models it is one of the best value performers. With 504 GB/s and 36 TFLOPS it runs ~110 tok/s on Llama 3 8B q4. The limit is 12GB VRAM, which caps model size.

RTX 4070 Super or RTX 4060 Ti 16GB for AI?

The 4070 Super is far faster and the better pure-inference card; the 4060 Ti 16GB only wins if you specifically need 16GB to fit a model the 4070 Super can't.

NVIDIA Ada Lovelace 2024 enthusiast

NVIDIA GeForce RTX 4070 Super

// 12 GB GDDR6X · 220W TDP · 36 TFLOPS FP32

▸ AI VALUE

4.1/5

ENTHUSIAST · RANK #4.4

▸ VRAM

12GB

▸ FP32

36TFL

▸ MEM BW

504GB/s

▸ TDP

220W

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LLM Inference Performance

// tokens/sec · q4 quantization · vLLM

Model	Tokens / sec	Local Fit
Mistral 7b Q4	118 tok/s	fits · single GPU
Llama 3 8b Q4	110 tok/s	fits · single GPU
Llama 3 13b Q4	60 tok/s	fits · single GPU
Llama 3 70b Q4	— OOM —	OOM / offload

Local Model Compatibility

// single-GPU · no CPU offload

7B params (int) fits

13B params fits

70B (4-bit quant) OOM

Spec Sheet

// verified · Ada Lovelace

▸ COMPUTEA0

▸ ARCHITECTURE Ada Lovelace

▸ GPU CHIP AD104

▸ CUDA CORES 7,168

▸ TENSOR CORES 224

▸ BOOST CLOCK 2475 MHz

▸ FP32 36 TFLOPS

▸ LAUNCH YEAR 2024

▸ MEMORY & RATINGSB0

▸ VRAM 12 GB GDDR6X

▸ BANDWIDTH 504 GB/s

▸ TIER enthusiast

▸ OVERALL 4.4/5

▸ AI VALUE 4.1/5

▸ GAMING VALUE 4.4/5

▸ POWERC0

▸ TDP 220 W

▸ PERF/W (FP32) 0.164 TFL/W

▸ MODEL FITD0

▸ RUNS 7B (INT) yes

▸ RUNS 13B yes

▸ RUNS 70B (4-bit) no

▸ PLATFORM CUDA · ROCm via HIP

Comparable GPUs

// head-to-head comparisons

rtx 4080 super

The 4080 Super adds 4GB and ~45% more compute; the 4070 Super is the better value if 12GB is enough.

Compare ▸

rx 7800 xt

The 7800 XT has 16GB and strong gaming value; the 4070 Super is faster for AI with smoother CUDA tooling.

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Analysis notes

Quick Summary

The RTX 4070 Super AI verdict: it is the speed-per-dollar sweet spot for local LLMs up to 13B. 7,168 CUDA cores, 504 GB/s of bandwidth and 36 TFLOPS deliver genuinely fast inference — the only real limit is the 12GB VRAM ceiling.

Specs That Matter for AI

The wide, fast GDDR6X (504 GB/s) is what makes it quick where bandwidth-starved 16GB cards stall. 12GB comfortably holds 7B and 13B quantized models; it just can’t reach the 70B tier.

Performance

~110 tok/s on Llama 3 8B q4 and ~60 tok/s on 13B q4 — among the fastest here for the price. A strong fine-tuning card for smaller models too.

Verdict

If your models fit in 12GB, the 4070 Super is the value pick of the lineup. Need more capacity? Step to a 16GB or 24GB card.

Frequently Asked Questions

Is the RTX 4070 Super good for local LLMs?: Yes — for 7B–13B models it is one of the best value performers. With 504 GB/s and 36 TFLOPS it runs ~110 tok/s on Llama 3 8B q4. The limit is 12GB VRAM, which caps model size.
RTX 4070 Super or RTX 4060 Ti 16GB for AI?: The 4070 Super is far faster and the better pure-inference card; the 4060 Ti 16GB only wins if you specifically need 16GB to fit a model the 4070 Super can't.