What is the cheapest cloud provider to rent NVIDIA NVIDIA A10G 24GB?

Based on our live rate telemetry, the lowest verified rate for NVIDIA A10G 24GB starts at $0.40/hr (spot) available on Amazon Web Services (AWS EC2 AI).

Can I run ComfyUI and vLLM on NVIDIA A10G 24GB?

Yes. With 24GB of GDDR6 memory, the NVIDIA A10G 24GB is capable of running modern AI workloads. It is ideal for ComfyUI FLUX.1 and 8B-14B LLMs.

Ampere Architecture

NVIDIA A10G 24GB Cloud Rental Pricing

Designed specifically for cloud hyperscalers like AWS EC2 G5, the A10G delivers exceptional price/performance for mid-sized LLM and diffusion serving.

Lowest On-Demand

$1.01/hr

Cheapest Spot Rate

$0.40/hr

VRAM Capacity

24 GB

Memory Type

GDDR6

Memory Bandwidth

600 GB/s

FP16 Compute

125 TFLOPS

Power / TDP

150W

Interconnect

PCIe 4.0 x16

Live Cloud Instance Rates

Found 1 cloud instances for NVIDIA A10G 24GB

Cloud Provider	Host Tier	GPU Config	Interconnect	vCPU & RAM	Network & Disk	Spot Price	On-Demand	Action
A Amazon Web Services (AWS EC2 AI)	Datacenter	1x (24GB)	PCIe 4.0 x16	4 vCPUs / 16GB RAM	1.8 GB/s (~14 Gbps) Persistent Volume	$0.40/hr	$1.01/hr	Deploy

Get Restock & Flash Price-Drop Alerts for NVIDIA A10G 24GB

We monitor NVIDIA A10G 24GB capacity across RunPod, Lambda, and Vast.ai 24/7. Get notified immediately when spot capacity opens below average rates.

Optimal Workloads & Workflows

Cost-effective LLM inference, Stable Diffusion, and AWS EC2 G5 instances

Calculate exact model VRAM & context requirements in the AI Calculator

Architecture Deep-Dive & Hardware Analysis

NVIDIA A10G 24GB: The Cloud Inference Standard (2026 Technical Guide)

The NVIDIA A10G 24GB is Ampere's popular cloud inference accelerator, widely deployed on AWS EC2 (G5 instances) and other clouds. Featuring 24GB of GDDR6 memory at 600 GB/s bandwidth and 150W TDP, it rents between $0.35 and $0.70/hr.

Engineering Decision Matrix: Is NVIDIA A10G 24GB Right For You?

Sweet Spot Workloads & Pipelines:

7B-14B parameter model serving in FP16 or INT8.
Stable Diffusion image synthesis on AWS-hosted stacks.

Workloads To Avoid & Bottlenecks:

70B models or FP8 native workflows.

1. Cloud Deployment & Compatibility

The A10G provides full enterprise driver support and high reliability across major hyperscaler cloud providers, serving as the benchmark standard for 24GB inference instances.

Framework Benchmarks & Execution Profiles

vLLM Mistral-7B-Instruct (FP16)EXCELLENT

Memory Footprint: 16.0 GB / 24 GB

Delivers ~85 tokens/second generation throughput.

Technical FAQ: Renting & Deploying NVIDIA A10G 24GB

Why choose A10G over RTX 3090?

A10G is an enterprise datacenter GPU with guaranteed uptime SLAs and official AWS support, whereas RTX 3090 is primarily available on community clouds.

GPU Hardware Watchdog & Audit

Hardware Architecture Traps & Sanity Benchmarks

Avoid fake multi-GPU speedups, PCIe bottlenecks, and slow disk thrashing before paying full price.

SXM5 vs PCIe Interconnect

An 8x H100 SXM5 cluster delivers 900 GB/s bidirectional NVLink. In contrast, 8x H100 PCIe cards run at only 64 GB/s. For multi-GPU LLM training and vLLM tensor parallelism, PCIe cards suffer 40% to 70% throughput collapse due to all-reduce bottlenecks.

Tier 3 Cloud vs P2P Miner Rigs

Community marketplace hosts (Vast.ai) can save up to 60%, but lack power redundancy. A host reboot during a 20-hour LoRA run loses your epoch if checkpoints aren’t saved to remote S3. Choose Tier-3 data centers for production training.

Disk IOPS & Model Weights

Loading a 70B Safetensors model (140GB) on a cheap HDD network share takes 15 minutes. On high-speed local NVMe (>2.5 GB/s), it loads in 45 seconds. Always verify IOPS before debugging slow vLLM startup times.

Run on Fresh SSH Instance (Instant Verification)Takes ~5 seconds to run

# --- GPUy Day-1 Cloud GPU Healthcheck & Benchmark ---
# 1. Check GPU Clocks, Temperature, Power, PCIe Bus & Driver
nvidia-smi --query-gpu=name,driver_version,temperature.gpu,utilization.gpu,power.draw,clocks.current.graphics,memory.total,pcie.link.gen.current,pcie.link.width.current --format=csv

# 2. Check Thermal Throttle Violations & Performance State
nvidia-smi -q -d PERFORMANCE,CLOCK | grep -E "Throttle Reason|Performance State" | head -n 8

# 3. Check NVLink Interconnect Status (Critical for Multi-GPU Training)
nvidia-smi nvlink -s 2>/dev/null || echo "No NVLink detected (PCIe bus fallback)"

# 4. Benchmark NVMe Disk Write Speed (Look for >1.0 GB/s)
dd if=/dev/zero of=/workspace/speed_test.tmp bs=1G count=2 oflag=direct 2>&1 | tail -n 1
rm -f /workspace/speed_test.tmp

# 5. Test Hugging Face CDN Download Speed
curl -s -w 'HuggingFace CDN Speed: %{speed_download} bytes/sec\n' -o /dev/null https://huggingface.co/bert-base-uncased/resolve/main/pytorch_model.bin

Idle Temp< 45°C (Load < 78°C)

PCIe Bus LinkGen4 x16 / Gen5 x16

NVMe Disk Speed> 1.2 GB/s Direct IO

Weights CDN> 50 MB/s (400 Mbps)