Rent A30
Compare live on-demand and spot rental prices across 97+ cloud providers. Slimmed-down A100 die with HBM2. Excellent memory bandwidth for its TDP class.
Choosing the right billing model for A30
Provision and terminate at any time. Ideal for development, short experiments, and workloads with unpredictable duration.
Instances can be reclaimed when demand spikes. Best for fault-tolerant batch jobs, training with checkpointing, and preprocessing.
Lock in a rate for 1–3 months. Right for sustained production inference or long training runs where cost predictability matters.
Best use cases
- Inference at scale
- Low-power HPC
- Edge data centers
A30 — Specs & Benchmarks
Performance bars, compute tiers (FP32/FP16/BF16/FP8/INT8), memory specs, LLM model size guidance, and related GPU comparisons.
A30 Rental Guide
Rent an A30 when 24GB is enough and low power, HBM bandwidth, MIG, or data-center reliability matters. It is a practical middle ground for inference and compact training workloads that do not warrant an A100, especially in environments that value density.
Spot instances are appropriate for restartable batch inference, preprocessing, and checkpointed training, where the lower price outweighs interruption risk. Choose on-demand for multi-tenant endpoints or scheduled workflows that depend on a stable MIG configuration and continuous availability.
Compare it with the L4 for maximum efficiency, the A10G for graphics-oriented inference, and the A100 40GB for more capacity and bandwidth. The A30’s 24GB ceiling remains decisive, so evaluate model weights, activations, and KV cache together rather than relying on parameter count alone.
Frequently Asked Questions
How much does it cost to rent a A30?
A30 on-demand rental prices vary by provider and region. On-demand rates typically range based on availability and provider margins — use the comparison table above to see current live rates across all providers. Spot instances are generally 40–70% cheaper than on-demand but can be interrupted. Monthly cost estimates (hourly rate × 730 hours) are shown in the table for sustained workloads.
Which cloud provider has the cheapest A30?
The cheapest A30 provider changes as providers update their pricing. The comparison table above shows live rates sorted by price, so the cheapest option is always at the top. Factors beyond headline price include region (latency to your users), availability (high/medium/low), and billing granularity (per-second vs per-hour minimums).
What can I run on a A30?
With 24GB of HBM2, the A30 can run LLM models up to approximately 12B parameters at FP16, 24B at INT8, or 48B at INT4/GGUF quantization. Common workloads include: Inference at scale, Low-power HPC, Edge data centers. Slimmed-down A100 die with HBM2. Excellent memory bandwidth for its TDP class.
Should I use on-demand or spot pricing for A30?
Spot instances save 40–70% vs on-demand but can be interrupted when the provider needs capacity back. Use spot for: batch inference jobs, training runs with checkpointing, preprocessing pipelines, and any workload that can tolerate interruption and restart. Use on-demand for: production inference serving, interactive workloads, and jobs that cannot be interrupted. Most providers bill per second, so short on-demand jobs are not penalized by hourly minimums.
How does the A30 compare to the H100 for cloud rental?
The H100 80GB delivers 1,979 TFLOPS FP16 with 3,350 GB/s HBM3 bandwidth, compared to the A30's 165 TFLOPS FP16 and 933 GB/s bandwidth. The H100 is significantly more expensive — typically $2.50–$5.00/hr vs lower rates for the A30. For workloads that fit within 24GB and don't require FP8 precision, the A30 often delivers better cost-per-token than the H100.
What is the memory bandwidth of the A30 and why does it matter?
The A30 has 933 GB/s of memory bandwidth. For LLM inference, memory bandwidth is often more important than raw TFLOPS — each autoregressive token generation reads the full model weight matrix from VRAM, so bandwidth directly determines tokens-per-second throughput. Higher bandwidth means faster inference for the same model at the same batch size. For batch inference (processing many requests simultaneously), compute throughput becomes more important.
Can I use the A30 for Stable Diffusion or image generation?
Yes — the A30 is capable for Stable Diffusion and image generation workloads. Image generation is primarily FP32 and FP16 compute-bound, and the A30's 10.3 TFLOPS FP32 throughput determines images-per-second. The 24GB VRAM fits SDXL (requires ~6GB) and most ControlNet pipelines. For high-throughput image generation at scale, compare cost-per-image across providers using the GPU cost calculator.