Quadro RTX 4000 8GB

The Quadro RTX 4000 8GB is a Turing-generation NVIDIA GPU built on the TU104 architecture, manufactured on a TSMC 12nm process node with 13.6 billion transistors. Released in 2018, it delivers 57 TFLOPS of FP16 throughput and 57 TFLOPS BF16 — the two precision formats most commonly used for transformer model training and inference. The TU104 architecture represents NVIDIA's approach to balancing compute throughput, memory bandwidth, and power efficiency for data center AI workloads. At 160W TDP, the Quadro RTX 4000 8GB sits in the moderate power envelope (160W), suitable for both workstation and data center deployments.

Memory capacity is 8GB of GDDR6 with 416 GB/s bandwidth. This determines which models can run without quantization: approximately 4B parameters at FP16 (2 bytes/param), 8B at INT8 (1 byte/param), or up to 16B 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 137 FLOP/byte (57 TFLOPS ÷ 416 GB/s). Most autoregressive LLM inference falls well below this threshold, making the 416 GB/s memory bandwidth the binding constraint on tokens-per-second throughput rather than raw TFLOPS.

The Quadro RTX 4000 8GB uses PCIe 3.0 for host connectivity. Without NVLink, VRAM cannot be pooled across multiple cards — the single-card 8GB capacity is the hard ceiling for model size without model sharding over slower PCIe. For workloads that exceed 8GB, 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 Quadro RTX 4000 8GB best suited for workloads that fit within a single card's VRAM budget.

The primary workloads for the Quadro RTX 4000 8GB are Ultra-budget inference, Small model experimentation, Legacy workloads. Entry Turing professional GPU. 8GB GDDR6 limits to very small models. Low cost per hour. Has Tensor Cores for FP16 acceleration. Key limitations to factor into your evaluation: Older Turing architecture — no BF16 hardware support; GDDR6 memory bandwidth far below HBM alternatives; Limited cloud availability as legacy hardware. 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 Quadro RTX 4000 8GB is a 8-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 Quadro RTX 4000 8GB 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.