Can the RTX 4070 Ti run Llama 3 70B?

Not at usable quality. 12GB VRAM only fits Llama 3 70B at extreme quantization (q2_K, ~26GB needed) with CPU offload, dropping speeds below 2 tok/s. Stick to 7B–13B quantized models for this card.

Is 12GB VRAM enough for AI in 2026?

For inference of 7B and quantized 13B models, yes. For training, fine-tuning beyond LoRA, or 30B+ models, no — go 24GB+.

RTX 4070 Ti vs RX 7900 XTX for AI?

RX 7900 XTX wins on VRAM (24GB) and raw price/GB, but CUDA ecosystem still dominates. 4070 Ti faster end-to-end for most LLM stacks today.

NVIDIA Ada Lovelace 2023 enthusiast

NVIDIA GeForce RTX 4070 Ti

// 12 GB GDDR6X · 285W TDP · 40.09 TFLOPS FP32

▸ AI VALUE

3.8/5

ENTHUSIAST · RANK #4.1

▸ VRAM

12GB

▸ FP32

40.09TFL

▸ FP16

80.18TFL

▸ TDP

285W

Buy · Check price ↗ + Compare ← All GPUs

LLM Inference Performance

// tokens/sec · q4 quantization · vLLM

Model	Tokens / sec	Local Fit
Llama 3 8b Q4	95 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 OOM

70B (4-bit quant) OOM

Spec Sheet

// verified · Ada Lovelace

▸ COMPUTEA0

▸ ARCHITECTURE Ada Lovelace

▸ CUDA CORES 7,680

▸ FP32 40.09 TFLOPS

▸ FP16 / BF16 80.18 TFLOPS

▸ LAUNCH YEAR 2023

▸ MEMORY & RATINGSB0

▸ VRAM 12 GB GDDR6X

▸ TIER enthusiast

▸ OVERALL 4.1/5

▸ AI VALUE 3.8/5

▸ POWERC0

▸ TDP 285 W

▸ PERF/W (FP32) 0.141 TFL/W

▸ MODEL FITD0

▸ RUNS 7B (INT) yes

▸ RUNS 13B no

▸ RUNS 70B (4-bit) no

▸ PLATFORM CUDA · ROCm via HIP

Comparable GPUs

// head-to-head comparisons

rx 7900 xtx

RX 7900 XTX has 2x the VRAM for ~20% more money — better for larger models, but ROCm tooling lags CUDA.

Compare ▸

rtx 3090

RTX 3090 doubles VRAM to 24GB for larger models; the 4070 Ti is newer and more power-efficient but capped at 12GB.

Compare ▸

Analysis notes

Quick Summary

The RTX 4070 Ti has 12GB VRAM, which sets its practical limit for local AI. It runs the listed Llama 8B q4 workload at about 95 tok/s but cannot fit 70B q4 quantized models without slow CPU offload. It suits workloads centered on 7B inference; larger models need tighter quantization or offload.

Specs

Architecture: Ada Lovelace
CUDA cores: 7680
VRAM: 12GB GDDR6X
FP16 TFLOPS: 80.18
TDP: 285W

LLM Inference Benchmarks

Model	Quantization	Tokens/sec
Llama 3 8B	q4_K_M	95
Llama 3 70B	q4_K_M	OOM

Verdict

A great pick for AI inference up to 13B parameters. Bottlenecked above that by VRAM, not compute.

Frequently Asked Questions

Can the RTX 4070 Ti run Llama 3 70B?: Not at usable quality. 12GB VRAM only fits Llama 3 70B at extreme quantization (q2_K, ~26GB needed) with CPU offload, dropping speeds below 2 tok/s. Stick to 7B–13B quantized models for this card.
Is 12GB VRAM enough for AI in 2026?: For inference of 7B and quantized 13B models, yes. For training, fine-tuning beyond LoRA, or 30B+ models, no — go 24GB+.
RTX 4070 Ti vs RX 7900 XTX for AI?: RX 7900 XTX wins on VRAM (24GB) and raw price/GB, but CUDA ecosystem still dominates. 4070 Ti faster end-to-end for most LLM stacks today.