RTX A4000 16GB

The RTX A4000 16GB is a Ampere-generation NVIDIA GPU built on the GA104 architecture, manufactured on a Samsung 8nm process node with 17.4 billion transistors. Released in 2021, it delivers 38.4 TFLOPS of FP16 throughput and 38.4 TFLOPS BF16 — the two precision formats most commonly used for transformer model training and inference. The GA104 architecture represents NVIDIA's approach to balancing compute throughput, memory bandwidth, and power efficiency for data center AI workloads. At 140W TDP, the RTX A4000 16GB sits in the low-power tier (140W), enabling high-density deployments and edge inference scenarios.

Memory capacity is 16GB of GDDR6 with 448 GB/s bandwidth. This determines which models can run without quantization: approximately 8B parameters at FP16 (2 bytes/param), 16B at INT8 (1 byte/param), or up to 32B 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 86 FLOP/byte (38.4 TFLOPS ÷ 448 GB/s). Most autoregressive LLM inference falls well below this threshold, making the 448 GB/s memory bandwidth the binding constraint on tokens-per-second throughput rather than raw TFLOPS.

The RTX A4000 16GB uses PCIe 4.0 for host connectivity. Without NVLink, VRAM cannot be pooled across multiple cards — the single-card 16GB capacity is the hard ceiling for model size without model sharding over slower PCIe. For workloads that exceed 16GB, 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 A4000 16GB best suited for workloads that fit within a single card's VRAM budget.

The primary workloads for the RTX A4000 16GB are Entry professional inference, Budget AI workloads, Low-power deployments. Entry Ampere professional GPU. 16GB GDDR6 at very low cost. Widely available on budget cloud providers. Good for 7B model inference and experimentation. Key limitations to factor into your evaluation: GDDR6 memory bandwidth far below HBM alternatives; No NVLink on A4000/A2000 — single-card VRAM ceiling; Professional but older Ampere architecture — no FP8 support. 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 A4000 16GB is a 5-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 A4000 16GB 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.