RTX 3080

The RTX 3080 is a Ampere-generation NVIDIA GPU built on the GA102 architecture, manufactured on a Samsung 8nm process node with 28.3 billion transistors. Released in 2020, it delivers 59.6 TFLOPS of FP16 throughput and 59.6 TFLOPS BF16 — the two precision formats most commonly used for transformer model training and inference. The GA102 architecture represents NVIDIA's approach to balancing compute throughput, memory bandwidth, and power efficiency for data center AI workloads. At 320W TDP, the RTX 3080 sits in the mid-range data center tier (320W), fitting standard GPU server form factors.

Memory capacity is 10GB of GDDR6X with 760 GB/s bandwidth. This determines which models can run without quantization: approximately 5B parameters at FP16 (2 bytes/param), 10B at INT8 (1 byte/param), or up to 20B 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 78 FLOP/byte (59.6 TFLOPS ÷ 760 GB/s). Most autoregressive LLM inference falls well below this threshold, making the 760 GB/s memory bandwidth the binding constraint on tokens-per-second throughput rather than raw TFLOPS.

The RTX 3080 uses PCIe 4.0 for host connectivity. Without NVLink, VRAM cannot be pooled across multiple cards — the single-card 10GB capacity is the hard ceiling for model size without model sharding over slower PCIe. For workloads that exceed 10GB, 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 3080 best suited for workloads that fit within a single card's VRAM budget.

The primary workloads for the RTX 3080 are Ultra-budget inference, Experimentation, Small models only. Entry-level Ampere option. Only 10GB VRAM — limits to very small models. No ECC; consumer-grade reliability. Key limitations to factor into your evaluation: Only 10–12GB VRAM — limits to very small quantized models; No BF16 hardware acceleration; Consumer-grade reliability — no ECC memory. 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 3080 is a 6-year-old design that is increasingly being replaced by newer architectures in cloud deployments, though it remains available at competitive rental rates. CUDA compatibility is a significant advantage: the RTX 3080 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.