A10 24GB

The A10 24GB 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 2021, it delivers 125 TFLOPS of FP16 throughput and 125 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 150W TDP, the A10 24GB sits in the low-power tier (150W), enabling high-density deployments and edge inference scenarios.

Memory capacity is 24GB of GDDR6 with 600 GB/s bandwidth. This determines which models can run without quantization: approximately 12B parameters at FP16 (2 bytes/param), 24B at INT8 (1 byte/param), or up to 48B 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 208 FLOP/byte (125 TFLOPS ÷ 600 GB/s). Most autoregressive LLM inference falls well below this threshold, making the 600 GB/s memory bandwidth the binding constraint on tokens-per-second throughput rather than raw TFLOPS.

The A10 24GB uses PCIe 4.0 for host connectivity. Without NVLink, VRAM cannot be pooled across multiple cards — the single-card 24GB capacity is the hard ceiling for model size without model sharding over slower PCIe. For workloads that exceed 24GB, 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 A10 24GB best suited for workloads that fit within a single card's VRAM budget.

The primary workloads for the A10 24GB are Inference serving, Graphics + compute, Low-power deployments. Data center GPU optimized for inference and graphics. Similar to A10G but with different driver stack. Very low TDP (150W). Widely available on major clouds. Key limitations to factor into your evaluation: Only 24GB GDDR6 — limits to ~13B models at FP16; No NVLink — single-card VRAM ceiling; Lower memory bandwidth than HBM-based alternatives. 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 A10 24GB 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 A10 24GB 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.