The NVIDIA A30 (24GB) is equipped with 24 GB of dedicated VRAM and 933 GB/s memory bandwidth. It provides 100% addressable VRAM for CUDA and TensorRT-LLM runtimes, supporting full precision and quantized inference.
Memory
24 GB
Usable Memory Ceiling
24 GB
Bandwidth
933 GB/s
Max Inference Class
32B Models (Q4)
TDP
165W
Simulated memory requirements as context increases. Curves crossing above the reference line will cause Out Of Memory (OOM) errors.
Precision:
Simulated inference throughput and memory feasibility across popular open-weights models.
Context:
KV Cache:
GPUs:
Need custom layer dimensions, offloading configurations, or distributed multi-node topology?
Open in Advanced VRAM CalculatorMaximum model parameter class runnable by weight precision.
Context:
4-bit
32B Models
8-bit
14B Models
16-bit
8B Models
Hardware capacity for local training and adapter fine-tuning.
Context Length:
QLoRA
32B Models
LoRA
14B Models
Full Parameter Training
3B Models
Guidance for optimal precision and operational ceilings on NVIDIA A30 (24GB).
Inference Sweet Spot
Optimized for 14B models at uncompressed or Q8 precision, and 32B models at Q4 with up to 32k context.
Bandwidth & Speed Profile
With 933 GB/s aggregate bandwidth, batch size 1 inference operates in a memory-bandwidth bound regime. Generates approximately 207 tok/s on an 8B Q4 model and 25 tok/s on a 70B Q4 model.
APX AI
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