RTX 5070

The RTX 5070 is a Blackwell-generation NVIDIA GPU built on the GB205 architecture, manufactured on a TSMC 4NP process node with 30.1 billion transistors. Released in 2025, it delivers 144.2 TFLOPS of FP16 throughput and 144.2 TFLOPS BF16 — the two precision formats most commonly used for transformer model training and inference. The GB205 architecture represents NVIDIA's approach to balancing compute throughput, memory bandwidth, and power efficiency for data center AI workloads. At 250W TDP, the RTX 5070 sits in the moderate power envelope (250W), suitable for both workstation and data center deployments.

Memory capacity is 12GB of GDDR7 with 672 GB/s bandwidth. This determines which models can run without quantization: approximately 6B parameters at FP16 (2 bytes/param), 12B at INT8 (1 byte/param), or up to 24B parameters at INT4/GGUF quantization (0.5 bytes/param). These figures are theoretical maximums — actual capacity is reduced by KV cache, framework overhead, and activation memory, typically by 10–20% for inference and 30–40% for training. The arithmetic intensity ceiling is approximately 215 FLOP/byte (144.2 TFLOPS ÷ 672 GB/s). Most autoregressive LLM inference falls well below this threshold, making the 672 GB/s memory bandwidth the binding constraint on tokens-per-second throughput rather than raw TFLOPS.

The RTX 5070 uses PCIe 5.0 for host connectivity. Without NVLink, VRAM cannot be pooled across multiple cards — the single-card 12GB capacity is the hard ceiling for model size without model sharding over slower PCIe. For workloads that exceed 12GB, the alternative is pipeline parallelism (splitting model layers across cards) rather than tensor parallelism, which introduces inter-card communication overhead at each layer boundary. This makes the RTX 5070 best suited for workloads that fit within a single card's VRAM budget.

The primary workloads for the RTX 5070 are Ultra-budget inference, Experimentation, Entry-level AI. Entry Blackwell consumer GPU. 12GB GDDR7 limits to smaller models. Cheapest way to access Blackwell architecture. Key limitations to factor into your evaluation: Only 12GB VRAM — limits to 7B models at FP16; No NVLink or ECC memory; Very limited cloud availability. When comparing this GPU against alternatives at similar price points, the most important metrics are memory bandwidth (for inference throughput), VRAM capacity (for model size), and FP16/BF16 TFLOPS (for training speed). Raw TFLOPS figures can be misleading for inference — a GPU with lower TFLOPS but higher memory bandwidth will often outperform a higher-TFLOPS card on tokens-per-second for memory-bound autoregressive generation.

In the broader GPU market, the RTX 5070 is a one-year-old design that remains competitive for most workloads. CUDA compatibility is a significant advantage: the RTX 5070 benefits from the full NVIDIA software ecosystem including cuDNN, TensorRT, FlashAttention, and all major inference frameworks (vLLM, TGI, TensorRT-LLM). CUDA's maturity means optimized kernels are available for virtually every model architecture. For cloud rental, availability varies significantly by provider — some specialize in this GPU tier while others may have limited stock. Compare on-demand and spot pricing across providers using the rental comparison table on this page, and factor in region availability if latency is a concern for your inference workload.