NVIDIAdiscontinued

NVIDIA GeForce RTX 3080 10GB

Name: NVIDIA GeForce RTX 3080 10GB
Brand: NVIDIA
Price: 399 USD
Availability: OutOfStock

10 GB · 760 GB/s

$399

Updated 2026-03-15

The NVIDIA GeForce RTX 3080 10GB with 10 GB GDDR6X VRAM can handle 42 AI models across chat, coding, ai_coding. Best performance: all-MiniLM-L6-v2 at 5000 tok/s (excellent). Current price: approximately $399.

— OwnRig methodology, data updated 2026-03-15

Builder Capability: Limited

Insufficient VRAM for most AI coding workflows.

Specifications

Memory

Capacity: 10 GB
Bandwidth: 760 GB/s
Type: GDDR6X

Power & Form Factor

TDP: 320W
Rec. PSU: 750W
Slots: 3-slot
Length: 285mm

Compatible Models

Model	Quant	Speed	Rating	Notes
Llama 3.2 1B Instruct	Q8_0	180 tok/s	Excellent	760 GB/s bandwidth makes tiny models fly. Full Q8_0 quality with negligible VRAM.
Llama 3.2 3B Instruct	Q8_0	140 tok/s	Excellent	Very fast at 760 GB/s. Full quality with plenty of headroom.
Phi-4 Mini	Q8_0	120 tok/s	Excellent	3.8B model benefits greatly from 760 GB/s. Excellent quality and speed.
Phi-3 Mini 3.8B Instruct	Q8_0	130 tok/s	Excellent	Very fast at 760 GB/s. Full quality with headroom.
nomic-embed-text v1.5	Q8_0	2500 tok/s	Excellent	Tiny embedding model. Negligible VRAM, runs concurrently with any model.
all-MiniLM-L6-v2	FP16	5000 tok/s	Excellent	23M params. Practically free in VRAM. Ultra-fast embeddings.
Whisper Large V3	Q5_K_M	—	Excellent	Transcription model. 1.5GB VRAM. Real-time capable at 760 GB/s.
Whisper Large V3 Turbo	FP16	—	Excellent	Distilled Whisper. 1.6GB VRAM. Real-time transcription, very fast.
Llama 3.1 8B Instruct	Q5_K_M	50 tok/s	Excellent	760 GB/s bandwidth makes 8B models very fast despite tight 10GB VRAM.
Mistral 7B Instruct v0.3	Q5_K_M	48 tok/s	Excellent	Q5_K_M fits in 10GB. 760 GB/s delivers excellent throughput.
Qwen 2.5 7B Instruct	Q5_K_M	52 tok/s	Excellent	Strong 7B model. Fits comfortably at Q5. Very fast at 760 GB/s.
Gemma 2 9B Instruct	Q4_K_M	45 tok/s	Excellent	Q4_K_M fits in 10GB. 760 GB/s makes 9B feel snappy.
InternLM 2.5 7B Chat	Q5_K_M	50 tok/s	Excellent	7B fits at Q5. 760 GB/s delivers excellent speed.
DeepSeek R1 Distill Qwen 7B	Q5_K_M	48 tok/s	Excellent	R1 distilled reasoning. Q5 fits in 10GB. Very fast inference.
Qwen 2.5 Coder 7B Instruct	Q5_K_M	50 tok/s	Excellent	Coding model. Q5 fits. 760 GB/s makes code completion feel instant.
Gemma 3 4B	Q5_K_M	75 tok/s	Excellent	4B model. Plenty of headroom. Very fast at 760 GB/s.
Gemma 3 12B	Q3_K_M	28 tok/s	Acceptable	Tight fit at Q3_K_M (~7.5GB). 760 GB/s helps despite compression.
Phi-3 Medium 14B Instruct	Q3_K_M	32 tok/s	Acceptable	14B at Q3_K_M fits in 10GB. Tight but usable. Bandwidth helps.
Phi-4 14B	Q3_K_M	26 tok/s	Acceptable	14B at Q3 fits. Tight VRAM. 760 GB/s keeps it usable.
Qwen 2.5 14B Instruct	Q3_K_M	24 tok/s	Acceptable	Q3_K_M fits in 10GB. Tight. Bandwidth helps throughput.
StarCoder 2 15B	Q3_K_M	22 tok/s	Acceptable	15B at Q3 fits. Tight. Code completion usable.
DeepSeek Coder V2 Lite 16B	Q4_K_M	55 tok/s	Excellent	MoE — only 2.4B active per token. Q4 9.1GB fits well. Excellent coding speed.
LLaVA 1.6 13B	Q3_K_M	18 tok/s	Acceptable	Q4 needs ~8GB, Q3_K_M ~6.5GB fits. Multimodal vision+text.
Stable Diffusion XL 1.0	FP16	—	Excellent	~6.5GB VRAM. ~8-12 sec per 1024x1024 at 30 steps. 760 GB/s helps. Excellent.
Stable Diffusion 3.5 Large	Q8_0	—	Acceptable	Q8_0 9GB fits barely in 10GB. Tight. Good image quality.
Stable Diffusion 3 Medium	FP16	—	Excellent	~4GB VRAM. Plenty of headroom. Fast image generation.
FLUX.1 Dev	Q4_K_M	—	Not Viable	Q4 7.2GB technically fits but image gen overhead and quality tradeoffs make it not viable for 10GB. Offloading too slow.
Gemma 2 27B Instruct	Q3_K_M	—	Not Viable	Q3 10.3GB exceeds 10GB. 24B+ models don't fit.
Gemma 3 27B	Q3_K_M	—	Not Viable	Q3 13.3GB exceeds 10GB. Large models don't fit.
Codestral 22B	Q3_K_M	—	Not Viable	Q3 10.3GB exceeds 10GB with context. Doesn't fit.
Mistral Small 24B Instruct	Q2_K	—	Not Viable	24B exceeds 10GB even at Q2. Doesn't fit.
Yi 1.5 34B Chat	Q2_K	—	Not Viable	34B doesn't fit in 10GB.
Code Llama 34B Instruct	Q2_K	—	Not Viable	34B doesn't fit in 10GB.
Command R 35B	Q2_K	—	Not Viable	35B doesn't fit in 10GB.
QwQ 32B Preview	Q2_K	—	Not Viable	32B doesn't fit in 10GB.
Qwen 2.5 Coder 32B Instruct	Q2_K	—	Not Viable	32B doesn't fit in 10GB.
DeepSeek R1 Distill Qwen 32B	Q2_K	—	Not Viable	32B doesn't fit in 10GB.
Llama 3.1 70B Instruct	Q2_K	—	Not Viable	70B doesn't fit in 10GB.
Llama 3.3 70B Instruct	Q2_K	—	Not Viable	70B doesn't fit in 10GB.
Qwen 2.5 72B Instruct	Q2_K	—	Not Viable	72B doesn't fit in 10GB.
Mixtral 8x7B Instruct	Q2_K	—	Not Viable	MoE 46.7B total. Q2 16.4GB exceeds 10GB. Doesn't fit.
DeepSeek V3	Q2_K	—	Not Viable	671B MoE. Q2 115GB. Doesn't fit. Requires multi-GPU or 128GB+.

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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 3080 10GB run?: The NVIDIA GeForce RTX 3080 10GB 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 3080 10GB good for AI coding?: With 10 GB, the NVIDIA GeForce RTX 3080 10GB has limited VRAM for AI coding workflows.
How much VRAM does NVIDIA GeForce RTX 3080 10GB have?: The NVIDIA GeForce RTX 3080 10GB has 10 GB of GDDR6X VRAM with 760 GB/s bandwidth.
Can NVIDIA GeForce RTX 3080 10GB run 70B models?: 70B models can run on the NVIDIA GeForce RTX 3080 10GB with CPU offloading, but performance will be reduced. Consider a GPU with 48GB+ VRAM for full-speed 70B inference.
Is NVIDIA GeForce RTX 3080 10GB worth it for AI?: At $399, the NVIDIA GeForce RTX 3080 10GB offers 10 GB VRAM and runs 42 AI models. It's a good entry point for smaller models and experimentation.

Generation: Ampere. Last updated: 2026-03-15.

Compatible Models

Model	Quant	Speed	Rating	Notes
Llama 3.2 1B Instruct	Q8_0	180 tok/s	Excellent	760 GB/s bandwidth makes tiny models fly. Full Q8_0 quality with negligible VRAM.
Llama 3.2 3B Instruct	Q8_0	140 tok/s	Excellent	Very fast at 760 GB/s. Full quality with plenty of headroom.
Phi-4 Mini	Q8_0	120 tok/s	Excellent	3.8B model benefits greatly from 760 GB/s. Excellent quality and speed.
Phi-3 Mini 3.8B Instruct	Q8_0	130 tok/s	Excellent	Very fast at 760 GB/s. Full quality with headroom.
nomic-embed-text v1.5	Q8_0	2500 tok/s	Excellent	Tiny embedding model. Negligible VRAM, runs concurrently with any model.
all-MiniLM-L6-v2	FP16	5000 tok/s	Excellent	23M params. Practically free in VRAM. Ultra-fast embeddings.
Whisper Large V3	Q5_K_M	—	Excellent	Transcription model. 1.5GB VRAM. Real-time capable at 760 GB/s.
Whisper Large V3 Turbo	FP16	—	Excellent	Distilled Whisper. 1.6GB VRAM. Real-time transcription, very fast.
Llama 3.1 8B Instruct	Q5_K_M	50 tok/s	Excellent	760 GB/s bandwidth makes 8B models very fast despite tight 10GB VRAM.
Mistral 7B Instruct v0.3	Q5_K_M	48 tok/s	Excellent	Q5_K_M fits in 10GB. 760 GB/s delivers excellent throughput.
Qwen 2.5 7B Instruct	Q5_K_M	52 tok/s	Excellent	Strong 7B model. Fits comfortably at Q5. Very fast at 760 GB/s.
Gemma 2 9B Instruct	Q4_K_M	45 tok/s	Excellent	Q4_K_M fits in 10GB. 760 GB/s makes 9B feel snappy.
InternLM 2.5 7B Chat	Q5_K_M	50 tok/s	Excellent	7B fits at Q5. 760 GB/s delivers excellent speed.
DeepSeek R1 Distill Qwen 7B	Q5_K_M	48 tok/s	Excellent	R1 distilled reasoning. Q5 fits in 10GB. Very fast inference.
Qwen 2.5 Coder 7B Instruct	Q5_K_M	50 tok/s	Excellent	Coding model. Q5 fits. 760 GB/s makes code completion feel instant.
Gemma 3 4B	Q5_K_M	75 tok/s	Excellent	4B model. Plenty of headroom. Very fast at 760 GB/s.
Gemma 3 12B	Q3_K_M	28 tok/s	Acceptable	Tight fit at Q3_K_M (~7.5GB). 760 GB/s helps despite compression.
Phi-3 Medium 14B Instruct	Q3_K_M	32 tok/s	Acceptable	14B at Q3_K_M fits in 10GB. Tight but usable. Bandwidth helps.
Phi-4 14B	Q3_K_M	26 tok/s	Acceptable	14B at Q3 fits. Tight VRAM. 760 GB/s keeps it usable.
Qwen 2.5 14B Instruct	Q3_K_M	24 tok/s	Acceptable	Q3_K_M fits in 10GB. Tight. Bandwidth helps throughput.
StarCoder 2 15B	Q3_K_M	22 tok/s	Acceptable	15B at Q3 fits. Tight. Code completion usable.
DeepSeek Coder V2 Lite 16B	Q4_K_M	55 tok/s	Excellent	MoE — only 2.4B active per token. Q4 9.1GB fits well. Excellent coding speed.
LLaVA 1.6 13B	Q3_K_M	18 tok/s	Acceptable	Q4 needs ~8GB, Q3_K_M ~6.5GB fits. Multimodal vision+text.
Stable Diffusion XL 1.0	FP16	—	Excellent	~6.5GB VRAM. ~8-12 sec per 1024x1024 at 30 steps. 760 GB/s helps. Excellent.
Stable Diffusion 3.5 Large	Q8_0	—	Acceptable	Q8_0 9GB fits barely in 10GB. Tight. Good image quality.
Stable Diffusion 3 Medium	FP16	—	Excellent	~4GB VRAM. Plenty of headroom. Fast image generation.
FLUX.1 Dev	Q4_K_M	—	Not Viable	Q4 7.2GB technically fits but image gen overhead and quality tradeoffs make it not viable for 10GB. Offloading too slow.
Gemma 2 27B Instruct	Q3_K_M	—	Not Viable	Q3 10.3GB exceeds 10GB. 24B+ models don't fit.
Gemma 3 27B	Q3_K_M	—	Not Viable	Q3 13.3GB exceeds 10GB. Large models don't fit.
Codestral 22B	Q3_K_M	—	Not Viable	Q3 10.3GB exceeds 10GB with context. Doesn't fit.
Mistral Small 24B Instruct	Q2_K	—	Not Viable	24B exceeds 10GB even at Q2. Doesn't fit.
Yi 1.5 34B Chat	Q2_K	—	Not Viable	34B doesn't fit in 10GB.
Code Llama 34B Instruct	Q2_K	—	Not Viable	34B doesn't fit in 10GB.
Command R 35B	Q2_K	—	Not Viable	35B doesn't fit in 10GB.
QwQ 32B Preview	Q2_K	—	Not Viable	32B doesn't fit in 10GB.
Qwen 2.5 Coder 32B Instruct	Q2_K	—	Not Viable	32B doesn't fit in 10GB.
DeepSeek R1 Distill Qwen 32B	Q2_K	—	Not Viable	32B doesn't fit in 10GB.
Llama 3.1 70B Instruct	Q2_K	—	Not Viable	70B doesn't fit in 10GB.
Llama 3.3 70B Instruct	Q2_K	—	Not Viable	70B doesn't fit in 10GB.
Qwen 2.5 72B Instruct	Q2_K	—	Not Viable	72B doesn't fit in 10GB.
Mixtral 8x7B Instruct	Q2_K	—	Not Viable	MoE 46.7B total. Q2 16.4GB exceeds 10GB. Doesn't fit.
DeepSeek V3	Q2_K	—	Not Viable	671B MoE. Q2 115GB. Doesn't fit. Requires multi-GPU or 128GB+.

Frequently Asked Questions

What AI models can NVIDIA GeForce RTX 3080 10GB run?

The NVIDIA GeForce RTX 3080 10GB 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 3080 10GB good for AI coding?

With 10 GB, the NVIDIA GeForce RTX 3080 10GB has limited VRAM for AI coding workflows.

How much VRAM does NVIDIA GeForce RTX 3080 10GB have?

The NVIDIA GeForce RTX 3080 10GB has 10 GB of GDDR6X VRAM with 760 GB/s bandwidth.

Can NVIDIA GeForce RTX 3080 10GB run 70B models?

70B models can run on the NVIDIA GeForce RTX 3080 10GB with CPU offloading, but performance will be reduced. Consider a GPU with 48GB+ VRAM for full-speed 70B inference.

Is NVIDIA GeForce RTX 3080 10GB worth it for AI?

At $399, the NVIDIA GeForce RTX 3080 10GB offers 10 GB VRAM and runs 42 AI models. It's a good entry point for smaller models and experimentation.