Best Local AI Models for NVIDIA RTX 4070 Ti (12 GB VRAM)

Q: What is the VRAM and memory bandwidth of the NVIDIA RTX 4070 Ti?

The NVIDIA RTX 4070 Ti features 12 GB of VRAM and a memory bandwidth of 504 GB/s (GDDR6X 192-bit). In local language model inference, VRAM determines which model sizes fit on the card, while memory bandwidth dictates how many tokens per second the GPU generates.

Q: What is the best local AI model to run on a NVIDIA RTX 4070 Ti?

The best overall model for the NVIDIA RTX 4070 Ti is Qwen/Qwen3-8B-GGUF using the recommended Q8_0 quantization (9.47 GB total memory). With 12 GB of VRAM, this GPU typically runs a 14B model at Q5, comfortably at full GPU speed. Check our guide to the best LLMs for 12 GB VRAM for details.

The NVIDIA RTX 4070 Ti has 12 GB of VRAM. Here are the popular AI models it can run locally (4,096-token context, ~16.0 GB system RAM assumed), ranked by popularity.

Verdict: High-bandwidth 12 GB GPU: fast token throughput for models up to 14B.

VRAM & Memory

12 GB

GDDR6X 192-bit

Bandwidth

504 GB/s

Ada Lovelace

Fits in VRAM

33 models

Zero offload

TDP / Assumed RAM

285W

RAM: 16.0 GB

At a glance — 33 of the 36 most popular models run fully in VRAM on the NVIDIA RTX 4070 Ti (12 GB), with about 84 tokens/s estimated on an 8B model in Q4. A solid pick is Qwen/Qwen3-8B-GGUF (Q8_0, 9.47 GB, ~62.1 t/s estimated).

The NVIDIA RTX 4070 Ti is built on NVIDIA's Ada Lovelace architecture featuring GDDR6X 192-bit delivering 504 GB/s of raw memory bandwidth. High-bandwidth 12 GB GPU: fast token throughput for models up to 14B.

Equipped with 12 GB of dedicated VRAM, the NVIDIA RTX 4070 Ti can run 33 popular open-source models completely in GPU memory without offloading. This includes full-speed execution for weights like Qwen3-8B-GGUF, Qwen3-4B-GGUF, Qwen2.5-7B-Instruct-GGUF. For a comprehensive breakdown of compatible model weights, see our guide to the best LLMs for 12 GB VRAM.

With a memory bandwidth of 504 GB/s, this card can generate tokens at an estimated peak rate of ~84.0 tokens/second on an 8B parameter model (Q4_K_M). Its rated power draw is 285W TDP, so ensure your system's power supply and case ventilation are adequate for sustained local inferencing.

New to this? Read: How much VRAM do you need?

How the NVIDIA RTX 4070 Ti compares with similar cards

Compared with the NVIDIA RTX 4070, the NVIDIA RTX 4070 Ti has the same 12 GB of VRAM, so the same models fit fully on the GPU; the same 504 GB/s of memory bandwidth, so the same estimated ~84 tokens/s on an 8B model in Q4 and a higher board power (285 vs 200 W). For local LLMs the two generate tokens at the same speed: the difference comes down to GPU compute, which mainly affects prompt processing. Side-by-side comparison
Compared with the NVIDIA RTX 4070 Super, the NVIDIA RTX 4070 Ti has the same 12 GB of VRAM, so the same models fit fully on the GPU; the same 504 GB/s of memory bandwidth, so the same estimated ~84 tokens/s on an 8B model in Q4 and a higher board power (285 vs 220 W). For local LLMs the two generate tokens at the same speed: the difference comes down to GPU compute, which mainly affects prompt processing. Side-by-side comparison
Compared with the NVIDIA RTX 3060 12 GB, the NVIDIA RTX 4070 Ti has the same 12 GB of VRAM, so the same models fit fully on the GPU; about 40% faster token generation (~84 vs ~60 tokens/s on an 8B model in Q4, from 504 vs 360 GB/s); a higher board power (285 vs 170 W) and a newer architecture (Ada Lovelace vs Ampere). Side-by-side comparison

Token speeds here are a conservative estimate (memory bandwidth ÷ 6, for an 8B model in Q4 that reads about 5 GB of weights per token, plus overhead); prompt processing depends on GPU compute instead.

33 fit fully in VRAM · 3 run with offload

Only show models that fit fully in VRAM

Model	Size	Quant.	Quality	Memory	Speed~	Verdict
Qwen/Qwen3-8B-GGUF	8.19B	Q8_0	Excellent	9.47 GB	62.1 t/s	Fits in VRAM
Qwen/Qwen3-4B-GGUF	4.02B	Q8_0	Excellent	5.35 GB	126.4 t/s	Fits in VRAM
Qwen/Qwen2.5-7B-Instruct-GGUF	7.62B	Q8_0	Excellent	8.56 GB	66.8 t/s	Fits in VRAM
unsloth/NVIDIA-Nemotron-3.5-Bolt-E4B-GGUF	8.0B	Q8_K_XL	Excellent	8.48 GB	66.1 t/s	Fits in VRAM
unsloth/gpt-oss-20b-GGUF	20.91B	Q4_K_XL	Good	11.95 GB	45.6 t/s	Fits in VRAM
bartowski/Qwen2.5-32B-Instruct-GGUF	32.76B	IQ2_S	Very low	11.47 GB	52.1 t/s	Fits in VRAM
Qwen/Qwen2.5-1.5B-Instruct-GGUF	1.54B	GGUF	Excellent	4.23 GB	152.0 t/s	Fits in VRAM
unsloth/LFM2.5-24B-A2B-GGUF	23.97B	Q3_K_XL	Fair	11.07 GB	49.4 t/s	Fits in VRAM
unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF	30.53B	Q2_K_L	Low	11.73 GB	47.8 t/s	Fits in VRAM
Qwen/Qwen2.5-0.5B-Instruct-GGUF	0.49B	GGUF	Excellent	2.03 GB	427.3 t/s	Fits in VRAM
unsloth/Ornith-1.0-9B-GGUF	—	Q8_0	Excellent	9.8 GB	56.8 t/s	Fits in VRAM
Qwen/Qwen3-14B-GGUF	14.77B	Q5_K_M	Very good	11.22 GB	51.5 t/s	Fits in VRAM
Qwen/Qwen2.5-Coder-7B-Instruct-GGUF	7.62B	Q8_0	Excellent	8.56 GB	66.8 t/s	Fits in VRAM
ggml-org/gpt-oss-120b-GGUF	116.83B	BF16	Very low	2.42 GB	341.0 t/s	Fits in VRAM
LiquidAI/LFM3-1.7B-GGUF	1.73B	BF16	Excellent	4.08 GB	156.1 t/s	Fits in VRAM
unsloth/flint-GGUF	39.79B	IQ2_XXS	Very low	11.95 GB	45.6 t/s	Fits in VRAM
janhq/Jan-v3.5-4B-gguf	4.41B	GGUF	Excellent	9.59 GB	61.3 t/s	Fits in VRAM
Qwen/Qwen2.5-3B-Instruct-GGUF	3.09B	GGUF	Excellent	7.27 GB	79.6 t/s	Fits in VRAM
unsloth/GLM-4.7-Flash-GGUF	31.22B	Q2_K_L	Low	11.64 GB	47.4 t/s	Fits in VRAM
bartowski/Meta-Llama-3.1-8B-Instruct-GGUF	8.03B	Q8_0	Excellent	9.23 GB	63.4 t/s	Fits in VRAM
unsloth/Llama-3.2-3B-Instruct-GGUF	3.21B	F16	Excellent	7.23 GB	84.1 t/s	Fits in VRAM
LiquidAI/LFM2.5-1.2B-Instruct-GGUF	1.17B	BF16	Excellent	3.03 GB	230.9 t/s	Fits in VRAM
lmstudio-community/gemma-3-1b-it-GGUF	1.0B	Q8_0	Excellent	1.96 GB	506.1 t/s	Fits in VRAM
bartowski/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF	34.66B	IQ2_S	Very low	11.13 GB	49.2 t/s	Fits in VRAM
Qwen/Qwen3-0.6B-GGUF	0.75B	Q8_0	Excellent	1.83 GB	846.3 t/s	Fits in VRAM
bartowski/Qwen_Qwen3-0.6B-GGUF	0.75B	BF16	Excellent	2.64 GB	358.5 t/s	Fits in VRAM
unsloth/Qwen3-1.7B-GGUF	1.72B	BF16	Excellent	4.45 GB	157.0 t/s	Fits in VRAM
LiquidAI/LFM2.5-350M-GGUF	0.35B	BF16	Excellent	1.51 GB	760.6 t/s	Fits in VRAM
Qwen/Qwen2.5-Coder-14B-Instruct-GGUF	14.77B	Q5_K_M	Very good	11.34 GB	51.5 t/s	Fits in VRAM
unsloth/DeepSeek-R1-0528-Qwen3-8B-GGUF	8.19B	Q8_K_XL	Excellent	11.44 GB	50.0 t/s	Fits in VRAM
bartowski/Nanbeige_Nanbeige4.3-33B-GGUF	33.12B	IQ2_S	Low	12.0 GB	48.5 t/s	Fits in VRAM
Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF	1.54B	Q8_0	Excellent	2.67 GB	285.6 t/s	Fits in VRAM
bartowski/Qwen2.5-14B-Instruct-GGUF	14.77B	Q5_K_L	Very good	11.78 GB	49.2 t/s	Fits in VRAM
ggml-org/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF	31.58B	Q4_0	Good	18.42 GB	2.8 t/s	Offload
bartowski/Llama-3.3-70B-Instruct-GGUF	70.55B	Q2_K_L	Low	27.74 GB	2.0 t/s	Offload
unsloth/Qwen3-Coder-Next-GGUF	79.67B	IQ3_XXS	Low	27.7 GB	1.9 t/s	Offload

"Fits in VRAM" = fast, fully on GPU. "Offload" = part on system RAM, slower. Speed is a rough estimate from the NVIDIA RTX 4070 Ti's 504 GB/s memory bandwidth (offloaded models are limited by system RAM).

Frequently asked questions

What is the VRAM and memory bandwidth of the NVIDIA RTX 4070 Ti?

The NVIDIA RTX 4070 Ti features 12 GB of VRAM and a memory bandwidth of 504 GB/s (GDDR6X 192-bit). In local language model inference, VRAM determines which model sizes fit on the card, while memory bandwidth dictates how many tokens per second the GPU generates.

What is the best local AI model to run on a NVIDIA RTX 4070 Ti?

The best overall model for the NVIDIA RTX 4070 Ti is Qwen/Qwen3-8B-GGUF using the recommended Q8_0 quantization (9.47 GB total memory). With 12 GB of VRAM, this GPU typically runs a 14B model at Q5, comfortably at full GPU speed. Check our guide to the best LLMs for 12 GB VRAM for details.

Can the NVIDIA RTX 4070 Ti run 14B models locally?

Yes. The NVIDIA RTX 4070 Ti's 12 GB VRAM is the exact sweet spot for running 14B models (such as Qwen 2.5 Coder 14B) in Q4_K_M quantization (~9.2 GB footprint) with full 4,096-token context entirely in VRAM.