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NVIDIA RTX 3080 Ti (12GB)

The NVIDIA RTX 3080 Ti (12GB) features 12 GB of dedicated VRAM and 912 GB/s memory bandwidth, supporting CUDA and TensorRT-LLM runtimes for full-precision and quantized inference.

Memory

12 GB

Usable Memory Ceiling

12 GB

Bandwidth

912.4 GB/s

Max Inference Class

14B Models (Q4)

TDP

350W

VRAM Scaling by Context Length

Estimated memory requirements as context length increases. Curves crossing above the reference line indicate Out of Memory (OOM).

Gemma 3 270M (0.27B)ChatGLM2-6B (6B)Yi-9B (9B)K2 Horizon MoVA 36B A4B (36B (4B active))

Precision:

Best Models to Run on NVIDIA RTX 3080 Ti (12GB)

Inference throughput, memory compatibility, and generation speeds for open-weights LLMs on NVIDIA RTX 3080 Ti (12GB).

Sort by:

Context:

KV Cache:

GPUs:

RankModelParametersPrecisionTPS (Approx.)TTFTCan Run

#100

Phi-4 Reasoning Plus

14B

INT4

52 tok/s

~1.3s

#109

Gemma 4 12B

11.95B

INT4

61 tok/s

~1.1s

#113

Qwen3.5-9B

9B

INT4
Q8

106 tok/s

65 tok/s

~742 ms

~811 ms

#118

K2 Horizon 7B

7B

INT4
Q8

125 tok/s

83 tok/s

~582 ms

~586 ms

#128

NVIDIA Nemotron 3 Nano 30B-A3B

3.5B (30B active)

INT4
Q8
FP16

39 tok/s

23 tok/s

12 tok/s

~2.3s

~2.3s

~2.6s

#132

Qwen3.5-4B

4B

INT4
Q8
FP16

173 tok/s

123 tok/s

75 tok/s

~344 ms

~346 ms

~351 ms

#146

Granite 4.2 8B

8B

INT4
Q8

115 tok/s

71 tok/s

~662 ms

~723 ms

#150

Gemma 4 E4B

8B

INT4
Q8

115 tok/s

71 tok/s

~662 ms

~723 ms

#158

GLM-4V

9B

INT4

80 tok/s

~746 ms

#160

Phi-4

14B

INT4

76 tok/s

~1.1s

#169

OLMo 3 7B Think

7B

INT4
Q8

94 tok/s

66 tok/s

~585 ms

~639 ms

#172

DeepSeek-R1 14B

14B

INT4

78 tok/s

~1.1s

#173

Ministral 3 14B

14B

INT4

52 tok/s

~1.3s

#177

Gemma 3 12B

12B

INT4

86 tok/s

~983 ms

#182

Ministral 3 8B

8B

INT4
Q8

87 tok/s

56 tok/s

~664 ms

~784 ms

#183

Qwen3.5-2B

2B

INT4
Q8
FP16

231 tok/s

182 tok/s

124 tok/s

~186 ms

~187 ms

~189 ms

#184

MiniCPM5-2B

2B

INT4
Q8
FP16

231 tok/s

182 tok/s

124 tok/s

~186 ms

~187 ms

~189 ms

#185

Qwen2.5-14B

14B

INT4

76 tok/s

~1.1s

#188

Gemma 4 E2B

5.1B

INT4
Q8
FP16

152 tok/s

104 tok/s

56 tok/s

~431 ms

~434 ms

~516 ms

#189

Qwen3-4B

4B

INT4
Q8
FP16

173 tok/s

123 tok/s

75 tok/s

~344 ms

~346 ms

~351 ms

Inference Capacity

Maximum model parameter class runnable by weight precision.

Context:

4-bit

Q4

14B Models

8-bit

Q8

8B Models

16-bit

FP16

3B Models

Fine-Tuning Capacity

Hardware capacity for local training and adapter fine-tuning.

Context Length:

QLoRA

4-bit

14B Models

LoRA

16-bit

8B Models

Full Parameter Training

1B Models

Workload Recommendations

Guidance for optimal precision and operational ceilings on NVIDIA RTX 3080 Ti (12GB).

Inference Sweet Spot

Optimal for 8B models across extended context (32k+). 14B models fit comfortably at Q4 precision with standard context.

Bandwidth & Speed Profile

With 912.4 GB/s aggregate bandwidth, batch size 1 inference operates in a memory-bandwidth bound regime. Generates approximately 203 tok/s on an 8B Q4 model and 24 tok/s on a 70B Q4 model.

Frequently Asked Questions