NVIDIA

NVIDIA GeForce RTX 4070 Ti 12GB

Name: NVIDIA GeForce RTX 4070 Ti 12GB
Brand: NVIDIA
Price: 749 USD
Availability: InStock

12 GB · 504 GB/s

$749

Updated 2026-03-15

The NVIDIA GeForce RTX 4070 Ti 12GB with 12 GB GDDR6X VRAM can handle 42 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.

— OwnRig methodology, data updated 2026-03-15

Builder Capability: Starter AI Coding

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

Specifications

Memory

Capacity: 12 GB
Bandwidth: 504 GB/s
Type: GDDR6X

Power & Form Factor

TDP: 285W
Rec. PSU: 700W
Slots: 3-slot
Length: 310mm

Compatible Models

Model	Quant	Speed	Rating	Notes
all-MiniLM-L6-v2	FP16	12000 tok/s	Excellent	Tiny embedding model. 504 GB/s bandwidth makes it instant.
Codestral 22B	Q3_K_M	12 tok/s	Marginal	Q3_K_M fits in 10.3GB with ~1.7GB headroom. Compressed quality but usable for coding.
Code Llama 34B Instruct	Q2_K	—	Not Viable	Requires 16GB+ for viable quality.
Command R 35B	Q2_K	—	Not Viable	Requires 24GB+ for viable quality.
DeepSeek Coder V2 Lite 16B	Q4_K_M	55 tok/s	Excellent	Best coding model for 12GB. MoE efficiency + good quantization = excellent experience.
DeepSeek R1 Distill Qwen 32B	Q2_K	—	Not Viable	Requires 24GB+ for viable quality.
DeepSeek R1 Distill Qwen 7B	Q5_K_M	48 tok/s	Excellent	Q5_K_M fits comfortably. Good bandwidth for reasoning workloads.
DeepSeek V3	Q2_K	—	Not Viable	671B parameter model. Cloud/inference API only.
FLUX.1 Dev	Q8_0	—	Not Viable	12B image model. Q8_0 needs 13GB. Q4_K_M (7.2GB) would fit but quality loss significant.
Gemma 2 27B Instruct	Q3_K_M	—	Not Viable	Requires 16GB+ for viable quality.
Gemma 2 9B Instruct	Q5_K_M	48 tok/s	Excellent	Q5_K_M 6.6GB fits with 5.4GB headroom. 504 GB/s delivers good throughput.
Gemma 3 4B	Q8_0	85 tok/s	Excellent	Q8_0 4.8GB fits with 7.2GB headroom. Excellent everyday model.
Gemma 3 12B	Q4_K_M	32 tok/s	Good	Q4_K_M fits comfortably. Good balance of quality and speed for 12GB.
Gemma 3 27B	Q3_K_M	—	Not Viable	Requires 16GB+ for viable quality.
InternLM 2.5 7B Chat	Q5_K_M	46 tok/s	Excellent	Q5_K_M fits with headroom. Good 7B option.
Llama 3.1 70B Instruct	Q2_K	—	Not Viable	Requires 48GB+ for viable quality.
Llama 3.1 8B Instruct	Q5_K_M	52 tok/s	Excellent	Q5_K_M 5.8GB fits with 6.2GB headroom. 504 GB/s delivers good throughput.
Llama 3.2 1B Instruct	Q8_0	140 tok/s	Excellent	Tiny model. Full Q8_0 quality. Instant.
Llama 3.2 3B Instruct	Q8_0	95 tok/s	Excellent	Q8_0 fits easily. Fast and capable.
Llama 3.3 70B Instruct	Q2_K	—	Not Viable	Requires 48GB+ for viable quality.
LLaVA 1.6 13B	Q4_K_M	28 tok/s	Good	Multimodal vision+text. Q4_K_M fits comfortably for image understanding.
Mistral 7B Instruct v0.3	Q5_K_M	50 tok/s	Excellent	Q5_K_M 5.3GB fits with 6.7GB headroom. Strong 7B option.
Mistral Small 24B Instruct	Q3_K_M	—	Not Viable	Requires 16GB+ for viable quality.
Mixtral 8x7B Instruct	Q4_K_M	—	Not Viable	Requires 24GB+ for viable quality.
nomic-embed-text v1.5	Q8_0	6500 tok/s	Excellent	Embedding model. Negligible VRAM. Excellent for RAG pipelines.
Phi-3 Medium 14B Instruct	Q3_K_M	35 tok/s	Acceptable	Q3_K_M fits. Q4 would need ~10GB — tight. Marginal but usable.
Phi-3 Mini 3.8B Instruct	Q8_0	78 tok/s	Excellent	Q8_0 4.5GB fits with 7.5GB headroom. Full quality.
Phi-4 14B	Q3_K_M	34 tok/s	Acceptable	Q3_K_M fits. Q4 would need ~9.5GB — marginal. Compressed but usable.
Phi-4 Mini	Q8_0	82 tok/s	Excellent	Q8_0 4.3GB fits with 7.7GB headroom. Full quality.
Qwen 2.5 14B Instruct	Q4_K_M	30 tok/s	Good	Q4_K_M fits with headroom. Good 14B option.
Qwen 2.5 7B Instruct	Q5_K_M	48 tok/s	Excellent	Q5_K_M fits comfortably. Strong 7B.
Qwen 2.5 72B Instruct	Q2_K	—	Not Viable	Requires 48GB+ for viable quality.
Qwen 2.5 Coder 32B Instruct	Q2_K	—	Not Viable	Requires 24GB+ for viable quality.
Qwen 2.5 Coder 7B Instruct	Q5_K_M	49 tok/s	Excellent	Q5_K_M fits. Excellent coding model for 12GB.
QwQ 32B Preview	Q2_K	—	Not Viable	Requires 24GB+ for viable quality.
Stable Diffusion 3.5 Large	Q8_0	—	Good	Q8_0 9GB fits with headroom. ~8-12 sec per 1024x1024 image. FP16 12.5GB doesn't fit.
Stable Diffusion XL 1.0	FP16	—	Excellent	~5-8 seconds per 1024x1024 image. 504 GB/s helps. Room for LoRA.
Stable Diffusion 3 Medium	FP16	—	Excellent	~4-6 seconds per image. Plenty of headroom.
StarCoder 2 15B	Q3_K_M	28 tok/s	Acceptable	Q3_K_M fits. Q4 would need ~10.5GB — tight. Compressed but usable for coding.
Whisper Large V3	FP16	—	Excellent	Full FP16 quality. Real-time transcription with headroom.
Whisper Large V3 Turbo	FP16	—	Excellent	Fastest local transcription option. Plenty of headroom.
Yi 1.5 34B Chat	Q2_K	—	Not Viable	Requires 24GB+ for viable quality.

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Prices and availability vary. Inspect hardware before purchasing.

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Frequently Asked Questions

What AI models can NVIDIA GeForce RTX 4070 Ti 12GB run?: The NVIDIA GeForce RTX 4070 Ti 12GB can run 42 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 42 AI models. It's a good entry point for smaller models and experimentation.

Generation: Ada Lovelace. Last updated: 2026-03-15.

Compatible Models

Model	Quant	Speed	Rating	Notes
all-MiniLM-L6-v2	FP16	12000 tok/s	Excellent	Tiny embedding model. 504 GB/s bandwidth makes it instant.
Codestral 22B	Q3_K_M	12 tok/s	Marginal	Q3_K_M fits in 10.3GB with ~1.7GB headroom. Compressed quality but usable for coding.
Code Llama 34B Instruct	Q2_K	—	Not Viable	Requires 16GB+ for viable quality.
Command R 35B	Q2_K	—	Not Viable	Requires 24GB+ for viable quality.
DeepSeek Coder V2 Lite 16B	Q4_K_M	55 tok/s	Excellent	Best coding model for 12GB. MoE efficiency + good quantization = excellent experience.
DeepSeek R1 Distill Qwen 32B	Q2_K	—	Not Viable	Requires 24GB+ for viable quality.
DeepSeek R1 Distill Qwen 7B	Q5_K_M	48 tok/s	Excellent	Q5_K_M fits comfortably. Good bandwidth for reasoning workloads.
DeepSeek V3	Q2_K	—	Not Viable	671B parameter model. Cloud/inference API only.
FLUX.1 Dev	Q8_0	—	Not Viable	12B image model. Q8_0 needs 13GB. Q4_K_M (7.2GB) would fit but quality loss significant.
Gemma 2 27B Instruct	Q3_K_M	—	Not Viable	Requires 16GB+ for viable quality.
Gemma 2 9B Instruct	Q5_K_M	48 tok/s	Excellent	Q5_K_M 6.6GB fits with 5.4GB headroom. 504 GB/s delivers good throughput.
Gemma 3 4B	Q8_0	85 tok/s	Excellent	Q8_0 4.8GB fits with 7.2GB headroom. Excellent everyday model.
Gemma 3 12B	Q4_K_M	32 tok/s	Good	Q4_K_M fits comfortably. Good balance of quality and speed for 12GB.
Gemma 3 27B	Q3_K_M	—	Not Viable	Requires 16GB+ for viable quality.
InternLM 2.5 7B Chat	Q5_K_M	46 tok/s	Excellent	Q5_K_M fits with headroom. Good 7B option.
Llama 3.1 70B Instruct	Q2_K	—	Not Viable	Requires 48GB+ for viable quality.
Llama 3.1 8B Instruct	Q5_K_M	52 tok/s	Excellent	Q5_K_M 5.8GB fits with 6.2GB headroom. 504 GB/s delivers good throughput.
Llama 3.2 1B Instruct	Q8_0	140 tok/s	Excellent	Tiny model. Full Q8_0 quality. Instant.
Llama 3.2 3B Instruct	Q8_0	95 tok/s	Excellent	Q8_0 fits easily. Fast and capable.
Llama 3.3 70B Instruct	Q2_K	—	Not Viable	Requires 48GB+ for viable quality.
LLaVA 1.6 13B	Q4_K_M	28 tok/s	Good	Multimodal vision+text. Q4_K_M fits comfortably for image understanding.
Mistral 7B Instruct v0.3	Q5_K_M	50 tok/s	Excellent	Q5_K_M 5.3GB fits with 6.7GB headroom. Strong 7B option.
Mistral Small 24B Instruct	Q3_K_M	—	Not Viable	Requires 16GB+ for viable quality.
Mixtral 8x7B Instruct	Q4_K_M	—	Not Viable	Requires 24GB+ for viable quality.
nomic-embed-text v1.5	Q8_0	6500 tok/s	Excellent	Embedding model. Negligible VRAM. Excellent for RAG pipelines.
Phi-3 Medium 14B Instruct	Q3_K_M	35 tok/s	Acceptable	Q3_K_M fits. Q4 would need ~10GB — tight. Marginal but usable.
Phi-3 Mini 3.8B Instruct	Q8_0	78 tok/s	Excellent	Q8_0 4.5GB fits with 7.5GB headroom. Full quality.
Phi-4 14B	Q3_K_M	34 tok/s	Acceptable	Q3_K_M fits. Q4 would need ~9.5GB — marginal. Compressed but usable.
Phi-4 Mini	Q8_0	82 tok/s	Excellent	Q8_0 4.3GB fits with 7.7GB headroom. Full quality.
Qwen 2.5 14B Instruct	Q4_K_M	30 tok/s	Good	Q4_K_M fits with headroom. Good 14B option.
Qwen 2.5 7B Instruct	Q5_K_M	48 tok/s	Excellent	Q5_K_M fits comfortably. Strong 7B.
Qwen 2.5 72B Instruct	Q2_K	—	Not Viable	Requires 48GB+ for viable quality.
Qwen 2.5 Coder 32B Instruct	Q2_K	—	Not Viable	Requires 24GB+ for viable quality.
Qwen 2.5 Coder 7B Instruct	Q5_K_M	49 tok/s	Excellent	Q5_K_M fits. Excellent coding model for 12GB.
QwQ 32B Preview	Q2_K	—	Not Viable	Requires 24GB+ for viable quality.
Stable Diffusion 3.5 Large	Q8_0	—	Good	Q8_0 9GB fits with headroom. ~8-12 sec per 1024x1024 image. FP16 12.5GB doesn't fit.
Stable Diffusion XL 1.0	FP16	—	Excellent	~5-8 seconds per 1024x1024 image. 504 GB/s helps. Room for LoRA.
Stable Diffusion 3 Medium	FP16	—	Excellent	~4-6 seconds per image. Plenty of headroom.
StarCoder 2 15B	Q3_K_M	28 tok/s	Acceptable	Q3_K_M fits. Q4 would need ~10.5GB — tight. Compressed but usable for coding.
Whisper Large V3	FP16	—	Excellent	Full FP16 quality. Real-time transcription with headroom.
Whisper Large V3 Turbo	FP16	—	Excellent	Fastest local transcription option. Plenty of headroom.
Yi 1.5 34B Chat	Q2_K	—	Not Viable	Requires 24GB+ for viable quality.

Frequently Asked Questions

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

The NVIDIA GeForce RTX 4070 Ti 12GB can run 42 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 42 AI models. It's a good entry point for smaller models and experimentation.