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NVIDIA RTX 4070 SUPER (12GB)

The NVIDIA RTX 4070 SUPER (12GB) features 12 GB of dedicated VRAM and 504 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

504.2 GB/s

Max Inference Class

14B Models (Q4)

TDP

220W

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)Qwen3.5-4B (4B)Sahabat-AI-Gemma2-9B (9.2B)K2 Horizon MoVA 36B A4B (36B (4B active))

Precision:

Best Models to Run on NVIDIA RTX 4070 SUPER (12GB)

Inference throughput, memory compatibility, and generation speeds for open-weights LLMs on NVIDIA RTX 4070 SUPER (12GB).

Sort by:

Context:

KV Cache:

GPUs:

RankModelParametersPrecisionTPS (Approx.)TTFTCan Run

#102

Phi-4 Reasoning Plus

14B

INT4

31 tok/s

~1.3s

#121

Qwen3.5-9B

9B

INT4
Q8

67 tok/s

39 tok/s

~720 ms

~791 ms

#127

K2 Horizon 7B

7B

INT4
Q8

82 tok/s

51 tok/s

~565 ms

~572 ms

#130

Gemma 4 12B

11.95B

INT4

37 tok/s

~1.0s

#136

Qwen3.5-4B

4B

INT4
Q8
FP16

121 tok/s

80 tok/s

46 tok/s

~334 ms

~338 ms

~347 ms

#140

GLM-4V

9B

INT4

49 tok/s

~725 ms

#145

NVIDIA Nemotron 3 Nano 30B-A3B

3.5B (30B active)

INT4
Q8
FP16

22 tok/s

13 tok/s

6 tok/s

~2.3s

~2.3s

~2.6s

#152

Granite 4.2 8B

8B

INT4
Q8

74 tok/s

43 tok/s

~642 ms

~705 ms

#157

Gemma 4 E4B

8B

INT4
Q8

74 tok/s

43 tok/s

~642 ms

~705 ms

#164

Phi-4

14B

INT4

46 tok/s

~1.1s

#176

DeepSeek-R1 14B

14B

INT4

48 tok/s

~1.1s

#177

Ministral 3 14B

14B

INT4

31 tok/s

~1.3s

#180

Gemma 3 12B

12B

INT4

53 tok/s

~953 ms

#185

Ministral 3 8B

8B

INT4
Q8

53 tok/s

34 tok/s

~647 ms

~767 ms

#187

OLMo 3 7B Think

7B

INT4
Q8

59 tok/s

40 tok/s

~569 ms

~625 ms

#188

Qwen3.5-2B

2B

INT4
Q8
FP16

177 tok/s

129 tok/s

80 tok/s

~180 ms

~182 ms

~187 ms

#189

MiniCPM5-2B

2B

INT4
Q8
FP16

177 tok/s

129 tok/s

80 tok/s

~180 ms

~182 ms

~187 ms

#190

Qwen2.5-14B

14B

INT4

46 tok/s

~1.1s

#193

Gemma 4 E2B

5.1B

INT4
Q8
FP16

103 tok/s

66 tok/s

33 tok/s

~418 ms

~424 ms

~509 ms

#195

Ministral 3 3B

3B

INT4
Q8
FP16

98 tok/s

76 tok/s

50 tok/s

~260 ms

~263 ms

~270 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 4070 SUPER (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 504.2 GB/s aggregate bandwidth, batch size 1 inference operates in a memory-bandwidth bound regime. Generates approximately 112 tok/s on an 8B Q4 model and 13 tok/s on a 70B Q4 model.

Frequently Asked Questions