The NVIDIA RTX 4070 SUPER (12GB) is equipped with 12 GB of dedicated VRAM and 504 GB/s memory bandwidth. It provides 100% addressable VRAM for CUDA and TensorRT-LLM runtimes, supporting 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
Simulated memory requirements as context increases. Curves crossing above the reference line will cause Out Of Memory (OOM) errors.
Precision:
Simulated inference throughput and memory feasibility across popular open-weights models.
Context:
KV Cache:
GPUs:
Need custom layer dimensions, offloading configurations, or distributed multi-node topology?
Open in Advanced VRAM CalculatorMaximum model parameter class runnable by weight precision.
Context:
4-bit
14B Models
8-bit
8B Models
16-bit
3B Models
Hardware capacity for local training and adapter fine-tuning.
Context Length:
QLoRA
14B Models
LoRA
8B Models
Full Parameter Training
1B Models
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.
APX AI
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