NVIDIA

NVIDIA GeForce RTX 4060 8GB

Name: NVIDIA GeForce RTX 4060 8GB
Brand: NVIDIA
Price: 289 USD
Availability: InStock

8 GB · 272 GB/s

$289

Updated 2026-03-15

The NVIDIA GeForce RTX 4060 8GB with 8 GB GDDR6 VRAM can handle 42 AI models across embedding, ai_building, coding. Best performance: all-MiniLM-L6-v2 at 8500 tok/s (excellent). Current price: approximately $289.

— OwnRig methodology, data updated 2026-03-15

Builder Capability: Limited

Insufficient VRAM for most AI coding workflows.

Specifications

Memory

Capacity: 8 GB
Bandwidth: 272 GB/s
Type: GDDR6

Power & Form Factor

TDP: 115W
Rec. PSU: 550W
Slots: 2-slot
Length: 240mm

Compatible Models

Model	Quant	Speed	Rating	Notes
all-MiniLM-L6-v2	FP16	8500 tok/s	Excellent	Tiny 23M param embedding model. Negligible VRAM (~0.25GB). Can run concurrently with any other model.
Codestral 22B	Q3_K_M	—	Not Viable	22B model. Minimum Q3_K_M needs 10.3GB. 8GB insufficient.
Code Llama 34B Instruct	Q2_K	—	Not Viable	33.7B params. Minimum quantizations still exceed 8GB.
Command R 35B	Q2_K	—	Not Viable	35B class model. Not viable on 8GB.
DeepSeek Coder V2 Lite 16B	Q3_K_M	45 tok/s	Good	MoE architecture makes this 15.7B total model run like a small model. Excellent coding performance for 8GB.
DeepSeek R1 Distill Qwen 32B	Q2_K	—	Not Viable	32B class. Not viable on 8GB.
DeepSeek R1 Distill Qwen 7B	Q4_K_M	32 tok/s	Good	7.62B reasoning distillate. Q4_K_M fits with ~3GB headroom. 272 GB/s limits speed.
DeepSeek V3	Q2_K	—	Not Viable	671B parameter model. Cloud/inference API only.
FLUX.1 Dev	Q4_K_M	—	Marginal	Q4_K_M fits with ~0.8GB headroom. Slow image generation. FP16 and Q8_0 don't fit.
Gemma 2 27B Instruct	Q3_K_M	—	Not Viable	27B model. Minimum Q3_K_M needs 13.3GB.
Gemma 2 9B Instruct	Q4_K_M	28 tok/s	Good	Q4_K_M 5.6GB fits with ~2.4GB headroom. 9.24B model, 8GB is tight.
Gemma 3 4B	Q5_K_M	55 tok/s	Excellent	4.3B model. Q5_K_M 3.2GB fits easily. Fast and capable for 8GB.
Gemma 3 12B	Q3_K_M	18 tok/s	Marginal	12.2B model. Q3_K_M barely fits. Marginal quality but usable.
Gemma 3 27B	Q3_K_M	—	Not Viable	27B model. Not viable on 8GB.
InternLM 2.5 7B Chat	Q4_K_M	30 tok/s	Good	7.74B model. Q4_K_M fits. Similar to other 7B models on 8GB.
Llama 3.1 70B Instruct	Q2_K	—	Not Viable	70B class. Not viable on 8GB.
Llama 3.1 8B Instruct	Q4_K_M	32 tok/s	Good	Q4_K_M 4.9GB fits with ~3GB headroom. 272 GB/s limits speed. Best 8B for 8GB.
Llama 3.2 1B Instruct	Q8_0	95 tok/s	Excellent	1.24B model. Q8_0 1.5GB fits easily. Near-instant for 8GB.
Llama 3.2 3B Instruct	Q8_0	65 tok/s	Excellent	3.21B model. Q8_0 3.7GB fits with ~4.3GB headroom. Excellent for 8GB.
Llama 3.3 70B Instruct	Q2_K	—	Not Viable	70B class. Not viable on 8GB.
LLaVA 1.6 13B	Q3_K_M	22 tok/s	Marginal	13B multimodal. Q3_K_M barely fits with ~1.8GB headroom. Image processing adds overhead.
Mistral 7B Instruct v0.3	Q4_K_M	31 tok/s	Good	Q4_K_M 4.5GB fits with ~3.5GB headroom. 7.24B model.
Mistral Small 24B Instruct	Q3_K_M	—	Not Viable	24B class. Q3_K_M ~11GB. Not viable.
Mixtral 8x7B Instruct	Q4_K_M	—	Not Viable	46.7B total params. Not viable on 8GB.
nomic-embed-text v1.5	Q8_0	4200 tok/s	Excellent	137M embedding model. Q8_0 0.4GB. Can run concurrently with any LLM.
Phi-3 Medium 14B Instruct	Q3_K_M	20 tok/s	Marginal	14B model. Q3_K_M barely fits. Quality loss at Q3.
Phi-3 Mini 3.8B Instruct	Q5_K_M	52 tok/s	Excellent	3.82B model. Q5_K_M 3.0GB fits easily. Excellent for 8GB.
Phi-4 14B	Q3_K_M	19 tok/s	Marginal	14.66B model. Q3_K_M barely fits. Aggressive quantization.
Phi-4 Mini	Q5_K_M	55 tok/s	Excellent	3.82B model. Q5_K_M 2.8GB fits easily. Excellent for 8GB.
Qwen 2.5 14B Instruct	Q3_K_M	17 tok/s	Marginal	14.77B model. Q3_K_M barely fits. Tight.
Qwen 2.5 7B Instruct	Q4_K_M	30 tok/s	Good	7.62B model. Q4_K_M fits. Similar to Llama 8B on 8GB.
Qwen 2.5 72B Instruct	Q2_K	—	Not Viable	72B class. Not viable on 8GB.
Qwen 2.5 Coder 32B Instruct	Q2_K	—	Not Viable	32B coding model. Not viable on 8GB.
Qwen 2.5 Coder 7B Instruct	Q4_K_M	31 tok/s	Good	7.62B coding model. Q4_K_M fits. Good for coding on 8GB.
QwQ 32B Preview	Q2_K	—	Not Viable	32B model. Not viable on 8GB.
Stable Diffusion 3.5 Large	Q8_0	—	Not Viable	Q8_0 needs 9GB, FP16 needs 12.5GB. 8GB insufficient.
Stable Diffusion XL 1.0	FP16	—	Good	~15-25 seconds per 1024x1024 image at 30 steps. 6.5GB VRAM. Tight but fits.
Stable Diffusion 3 Medium	FP16	—	Good	~12-18 seconds per image. 5GB VRAM fits with headroom.
StarCoder 2 15B	Q3_K_M	16 tok/s	Marginal	15.73B coding model. Q3_K_M at limit. Marginal.
Whisper Large V3	Q5_K_M	—	Excellent	1.55B transcription model. Q5_K_M 1.5GB. Real-time transcription on 8GB.
Whisper Large V3 Turbo	FP16	—	Excellent	8x faster than full Whisper. 1.6GB VRAM. Real-time on any GPU.
Yi 1.5 34B Chat	Q2_K	—	Not Viable	34B model. Not viable on 8GB.

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Frequently Asked Questions

What AI models can NVIDIA GeForce RTX 4060 8GB run?: The NVIDIA GeForce RTX 4060 8GB 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 4060 8GB good for AI coding?: With 8 GB, the NVIDIA GeForce RTX 4060 8GB has limited VRAM for AI coding workflows.
How much VRAM does NVIDIA GeForce RTX 4060 8GB have?: The NVIDIA GeForce RTX 4060 8GB has 8 GB of GDDR6 VRAM with 272 GB/s bandwidth.
Can NVIDIA GeForce RTX 4060 8GB run 70B models?: 70B models can run on the NVIDIA GeForce RTX 4060 8GB with CPU offloading, but performance will be reduced. Consider a GPU with 48GB+ VRAM for full-speed 70B inference.
Is NVIDIA GeForce RTX 4060 8GB worth it for AI?: At $289, the NVIDIA GeForce RTX 4060 8GB offers 8 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	8500 tok/s	Excellent	Tiny 23M param embedding model. Negligible VRAM (~0.25GB). Can run concurrently with any other model.
Codestral 22B	Q3_K_M	—	Not Viable	22B model. Minimum Q3_K_M needs 10.3GB. 8GB insufficient.
Code Llama 34B Instruct	Q2_K	—	Not Viable	33.7B params. Minimum quantizations still exceed 8GB.
Command R 35B	Q2_K	—	Not Viable	35B class model. Not viable on 8GB.
DeepSeek Coder V2 Lite 16B	Q3_K_M	45 tok/s	Good	MoE architecture makes this 15.7B total model run like a small model. Excellent coding performance for 8GB.
DeepSeek R1 Distill Qwen 32B	Q2_K	—	Not Viable	32B class. Not viable on 8GB.
DeepSeek R1 Distill Qwen 7B	Q4_K_M	32 tok/s	Good	7.62B reasoning distillate. Q4_K_M fits with ~3GB headroom. 272 GB/s limits speed.
DeepSeek V3	Q2_K	—	Not Viable	671B parameter model. Cloud/inference API only.
FLUX.1 Dev	Q4_K_M	—	Marginal	Q4_K_M fits with ~0.8GB headroom. Slow image generation. FP16 and Q8_0 don't fit.
Gemma 2 27B Instruct	Q3_K_M	—	Not Viable	27B model. Minimum Q3_K_M needs 13.3GB.
Gemma 2 9B Instruct	Q4_K_M	28 tok/s	Good	Q4_K_M 5.6GB fits with ~2.4GB headroom. 9.24B model, 8GB is tight.
Gemma 3 4B	Q5_K_M	55 tok/s	Excellent	4.3B model. Q5_K_M 3.2GB fits easily. Fast and capable for 8GB.
Gemma 3 12B	Q3_K_M	18 tok/s	Marginal	12.2B model. Q3_K_M barely fits. Marginal quality but usable.
Gemma 3 27B	Q3_K_M	—	Not Viable	27B model. Not viable on 8GB.
InternLM 2.5 7B Chat	Q4_K_M	30 tok/s	Good	7.74B model. Q4_K_M fits. Similar to other 7B models on 8GB.
Llama 3.1 70B Instruct	Q2_K	—	Not Viable	70B class. Not viable on 8GB.
Llama 3.1 8B Instruct	Q4_K_M	32 tok/s	Good	Q4_K_M 4.9GB fits with ~3GB headroom. 272 GB/s limits speed. Best 8B for 8GB.
Llama 3.2 1B Instruct	Q8_0	95 tok/s	Excellent	1.24B model. Q8_0 1.5GB fits easily. Near-instant for 8GB.
Llama 3.2 3B Instruct	Q8_0	65 tok/s	Excellent	3.21B model. Q8_0 3.7GB fits with ~4.3GB headroom. Excellent for 8GB.
Llama 3.3 70B Instruct	Q2_K	—	Not Viable	70B class. Not viable on 8GB.
LLaVA 1.6 13B	Q3_K_M	22 tok/s	Marginal	13B multimodal. Q3_K_M barely fits with ~1.8GB headroom. Image processing adds overhead.
Mistral 7B Instruct v0.3	Q4_K_M	31 tok/s	Good	Q4_K_M 4.5GB fits with ~3.5GB headroom. 7.24B model.
Mistral Small 24B Instruct	Q3_K_M	—	Not Viable	24B class. Q3_K_M ~11GB. Not viable.
Mixtral 8x7B Instruct	Q4_K_M	—	Not Viable	46.7B total params. Not viable on 8GB.
nomic-embed-text v1.5	Q8_0	4200 tok/s	Excellent	137M embedding model. Q8_0 0.4GB. Can run concurrently with any LLM.
Phi-3 Medium 14B Instruct	Q3_K_M	20 tok/s	Marginal	14B model. Q3_K_M barely fits. Quality loss at Q3.
Phi-3 Mini 3.8B Instruct	Q5_K_M	52 tok/s	Excellent	3.82B model. Q5_K_M 3.0GB fits easily. Excellent for 8GB.
Phi-4 14B	Q3_K_M	19 tok/s	Marginal	14.66B model. Q3_K_M barely fits. Aggressive quantization.
Phi-4 Mini	Q5_K_M	55 tok/s	Excellent	3.82B model. Q5_K_M 2.8GB fits easily. Excellent for 8GB.
Qwen 2.5 14B Instruct	Q3_K_M	17 tok/s	Marginal	14.77B model. Q3_K_M barely fits. Tight.
Qwen 2.5 7B Instruct	Q4_K_M	30 tok/s	Good	7.62B model. Q4_K_M fits. Similar to Llama 8B on 8GB.
Qwen 2.5 72B Instruct	Q2_K	—	Not Viable	72B class. Not viable on 8GB.
Qwen 2.5 Coder 32B Instruct	Q2_K	—	Not Viable	32B coding model. Not viable on 8GB.
Qwen 2.5 Coder 7B Instruct	Q4_K_M	31 tok/s	Good	7.62B coding model. Q4_K_M fits. Good for coding on 8GB.
QwQ 32B Preview	Q2_K	—	Not Viable	32B model. Not viable on 8GB.
Stable Diffusion 3.5 Large	Q8_0	—	Not Viable	Q8_0 needs 9GB, FP16 needs 12.5GB. 8GB insufficient.
Stable Diffusion XL 1.0	FP16	—	Good	~15-25 seconds per 1024x1024 image at 30 steps. 6.5GB VRAM. Tight but fits.
Stable Diffusion 3 Medium	FP16	—	Good	~12-18 seconds per image. 5GB VRAM fits with headroom.
StarCoder 2 15B	Q3_K_M	16 tok/s	Marginal	15.73B coding model. Q3_K_M at limit. Marginal.
Whisper Large V3	Q5_K_M	—	Excellent	1.55B transcription model. Q5_K_M 1.5GB. Real-time transcription on 8GB.
Whisper Large V3 Turbo	FP16	—	Excellent	8x faster than full Whisper. 1.6GB VRAM. Real-time on any GPU.
Yi 1.5 34B Chat	Q2_K	—	Not Viable	34B model. Not viable on 8GB.

Frequently Asked Questions

What AI models can NVIDIA GeForce RTX 4060 8GB run?

The NVIDIA GeForce RTX 4060 8GB 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 4060 8GB good for AI coding?

With 8 GB, the NVIDIA GeForce RTX 4060 8GB has limited VRAM for AI coding workflows.

How much VRAM does NVIDIA GeForce RTX 4060 8GB have?

The NVIDIA GeForce RTX 4060 8GB has 8 GB of GDDR6 VRAM with 272 GB/s bandwidth.

Can NVIDIA GeForce RTX 4060 8GB run 70B models?

70B models can run on the NVIDIA GeForce RTX 4060 8GB with CPU offloading, but performance will be reduced. Consider a GPU with 48GB+ VRAM for full-speed 70B inference.

Is NVIDIA GeForce RTX 4060 8GB worth it for AI?

At $289, the NVIDIA GeForce RTX 4060 8GB offers 8 GB VRAM and runs 42 AI models. It's a good entry point for smaller models and experimentation.