What AI models can NVIDIA GeForce RTX 4070 Ti 12GB run?

The NVIDIA GeForce RTX 4070 Ti 12GB can run 55 AI models. Top performers include all-MiniLM-L6-v2, nomic-embed-text v1.5, Llama 3.2 1B Instruct. See the full compatibility table above for speeds and quality ratings.

Is NVIDIA GeForce RTX 4070 Ti 12GB good for AI coding?

With 12 GB, the NVIDIA GeForce RTX 4070 Ti 12GB runs 7-8B coding models at the Starter tier. Good for basic code completion.

How much VRAM does NVIDIA GeForce RTX 4070 Ti 12GB have?

The NVIDIA GeForce RTX 4070 Ti 12GB has 12 GB of GDDR6X VRAM with 504 GB/s bandwidth.

Can NVIDIA GeForce RTX 4070 Ti 12GB run 70B models?

70B models can run on the NVIDIA GeForce RTX 4070 Ti 12GB with CPU offloading, but performance will be reduced. Consider a GPU with 48GB+ VRAM for full-speed 70B inference.

Is NVIDIA GeForce RTX 4070 Ti 12GB worth it for AI?

At $749, the NVIDIA GeForce RTX 4070 Ti 12GB offers 12 GB VRAM and runs 55 AI models. It works for smaller models and experimentation.

Desktop GPU

NVIDIA GeForce RTX 4070 Ti 12GB

12 GB GDDR6X · 504 GB/s

From

$749

Estimated street price

VRAM

12 GB

Bandwidth

504 GB/s

TDP

285W

Models

Tier

Starter

The NVIDIA GeForce RTX 4070 Ti 12GB with 12 GB GDDR6X VRAM can handle 55 AI models across embedding, ai_building, coding. Best performance: all-MiniLM-L6-v2 at 12000 tok/s (excellent). For AI coding workflows, it supports the Starter AI Coding tier, good for 7–8B models. Current price: approximately $749.

Source: OwnRig methodology

VRAM

12 GB

Bandwidth

504 GB/s

Memory Type

GDDR6X

TDP

285W

Form Factor

3-slot, 310mm

Builder Capability: Starter AI Coding

Runs 7-8B models comfortably. Good for basic local code completion and small model experiments.

Software

Inference Backends

The software stacks that matter most for real-world inference on this device.

CUDA

production

Primary high-performance backend for NVIDIA inference workloads.

Vulkan

stable

Fallback backend for llama.cpp and related local runtimes.

What it can run

55 models


all-MiniLM-L6-v2	FP16	12000 tok/s	Excellent
Arcee Trinity Mini 26B	Q3_K_M	7 tok/s	Not viable
Arcee Trinity Nano 6B	Q8_0	89 tok/s	Excellent
Code Llama 34B Instruct	Q2_K	–	Not viable
Codestral 22B	Q3_K_M	12 tok/s	Marginal
Command R 35B	Q2_K	–	Not viable
DeepSeek Coder V2 Lite 16B	Q4_K_M	55 tok/s	Excellent
DeepSeek R1 Distill Qwen 32B	Q2_K	–	Not viable
DeepSeek R1 Distill Qwen 7B	Q5_K_M	48 tok/s	Excellent
DeepSeek V3	Q2_K	–	Not viable
FLUX.1 Dev	Q8_0	–	Not viable
Gemma 2 27B Instruct	Q3_K_M	–	Not viable
Gemma 2 9B Instruct	Q5_K_M	48 tok/s	Excellent
Gemma 3 12B	Q4_K_M	32 tok/s	Good
Gemma 3 27B	Q3_K_M	–	Not viable
Gemma 3 4B	Q8_0	85 tok/s	Excellent
Gemma 4 26B-A4B	Q3_K_M	8 tok/s	Not viable
Gemma 4 E2B	Q8_0	76 tok/s	Excellent
Gemma 4 E4B	Q8_0	47 tok/s	Good
GigaChat Lightning 10B	Q4_K_M	88 tok/s	Acceptable
InternLM 2.5 7B Chat	Q5_K_M	46 tok/s	Excellent
Llama 3.1 70B Instruct	Q2_K	–	Not viable
Llama 3.1 8B Instruct	Q5_K_M	52 tok/s	Excellent
Llama 3.2 1B Instruct	Q8_0	140 tok/s	Excellent
Llama 3.2 3B Instruct	Q8_0	95 tok/s	Excellent
Llama 3.3 70B Instruct	Q2_K	–	Not viable
LLaVA 1.6 13B	Q4_K_M	28 tok/s	Good
Mistral 7B Instruct v0.3	Q5_K_M	50 tok/s	Excellent
Mistral Small 24B Instruct	Q3_K_M	–	Not viable
Mixtral 8x7B Instruct	Q4_K_M	–	Not viable
nomic-embed-text v1.5	Q8_0	6500 tok/s	Excellent
NVIDIA Nemotron-3-super-120B-A12B	Q2_K	–	Not viable
Phi-3 Medium 14B Instruct	Q3_K_M	35 tok/s	Acceptable
Phi-3 Mini 3.8B Instruct	Q8_0	78 tok/s	Excellent
Phi-4 14B	Q3_K_M	34 tok/s	Acceptable
Phi-4 Mini	Q8_0	82 tok/s	Excellent
Qwen 2.5 14B Instruct	Q4_K_M	30 tok/s	Good
Qwen 2.5 72B Instruct	Q2_K	–	Not viable
Qwen 2.5 7B Instruct	Q5_K_M	48 tok/s	Excellent
Qwen 2.5 Coder 32B Instruct	Q2_K	–	Not viable
Qwen 2.5 Coder 7B Instruct	Q5_K_M	49 tok/s	Excellent
Qwen3-14B Instruct	Q5_K_M	23 tok/s	Acceptable
Qwen3-8B Instruct	Q8_0	29 tok/s	Good
Qwen3.5-122B-A10B	Q3_K_M	–	Not viable
Qwen3.5-27B	Q3_K_M	8 tok/s	Marginal
Qwen3.5-397B (MoE)	Q2_K	–	Not viable
Qwen3.6-27B	Q3_K_M	–	Not viable
QwQ 32B Preview	Q2_K	–	Not viable
Stable Diffusion 3 Medium	FP16	–	Excellent
Stable Diffusion 3.5 Large	Q8_0	–	Good
Stable Diffusion XL 1.0	FP16	–	Excellent
StarCoder 2 15B	Q3_K_M	28 tok/s	Acceptable
Whisper Large V3	FP16	–	Excellent
Whisper Large V3 Turbo	FP16	–	Excellent
Yi 1.5 34B Chat	Q2_K	–	Not viable

Showing 55 of 55 entries

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FAQ

Frequently Asked Questions

What AI models can NVIDIA GeForce RTX 4070 Ti 12GB run?: The NVIDIA GeForce RTX 4070 Ti 12GB can run 55 AI models. Top performers include all-MiniLM-L6-v2, nomic-embed-text v1.5, Llama 3.2 1B Instruct. See the full compatibility table above for speeds and quality ratings.
Is NVIDIA GeForce RTX 4070 Ti 12GB good for AI coding?: With 12 GB, the NVIDIA GeForce RTX 4070 Ti 12GB runs 7-8B coding models at the Starter tier. Good for basic code completion.
How much VRAM does NVIDIA GeForce RTX 4070 Ti 12GB have?: The NVIDIA GeForce RTX 4070 Ti 12GB has 12 GB of GDDR6X VRAM with 504 GB/s bandwidth.
Can NVIDIA GeForce RTX 4070 Ti 12GB run 70B models?: 70B models can run on the NVIDIA GeForce RTX 4070 Ti 12GB with CPU offloading, but performance will be reduced. Consider a GPU with 48GB+ VRAM for full-speed 70B inference.
Is NVIDIA GeForce RTX 4070 Ti 12GB worth it for AI?: At $749, the NVIDIA GeForce RTX 4070 Ti 12GB offers 12 GB VRAM and runs 55 AI models. It works for smaller models and experimentation.

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See every AI model it supports, expected performance, and how to build around it.

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