GPU Cloud Benchmark 2026: Who Offers the Real Best Deal for Running LLMs?
Key Takeaways
- VoltageGPU offers 8×A100 80GB at $6.02/h — 78% cheaper than AWS ($27.45/h)
- H200 pricing is equally aggressive — $26.60/h vs $50+ on CoreWeave
- Hidden costs matter — egress fees, minimum billing, and availability can change the equation
- Always verify before renting — check network, storage IOPS, and uptime
Updated: January 2026. All prices in USD, excluding enterprise discounts and commitments. Prices vary significantly by region.
🍎 Rule #1 of a "Pro" Benchmark: Compare Apples to Apples
When you read "$X/h", you're rarely comparing the same thing:
- Same GPU, same VRAM, same count (e.g., 8× A100 80GB ≠ 8× A100 40GB)
- Same billing model (on-demand vs spot vs capacity blocks vs marketplace)
- Same total cost: network egress, minimum billing, availability, provisioning time
In this post, I'm doing a pricing benchmark (the most reliable publicly available data), and I'll finish with a simple guide to verify pod quality (network, storage, stability) before clicking "Rent now".
Benchmark #1: 8× A100 80GB (The Training / Large LLM Baseline)
The 8× A100 80GB configuration is the gold standard for serious LLM training and large-scale inference. Here's how the major providers stack up:
| Provider (8× A100 80GB) | Total $/h | $/GPU-h | $/month (720h) |
|---|---|---|---|
VoltageGPUBest Price | $6.02 | $0.75 | $4,334 |
| RunPod (8× multiplied) | $11.12 | $1.39 | $8,006 |
| CoreWeave | $21.60 | $2.70 | $15,552 |
| AWS (p4de.24xlarge) | $27.45 | $3.43 | $19,762 |
| Azure (ND96amsr A100 v4) | $32.77 | $4.10 | $23,595 |
| GCP (a2-ultragpu-8g, us-central1) | $40.55 | $5.07 | $29,196 |
Sources (Public Pricing)
VoltageGPU (8×A100 at $6.02/h) ·AWS p4de.24xlarge $27.44705/h ·Azure ND96amsr A100 v4 $32.7702/h ·GCP a2-ultragpu-8g $40.5504/h ·CoreWeave A100 8-GPU $21.60/h ·RunPod GPU pricing (A100 SXM 80GB from ~$1.39/h)
Pro Verdict
Yes, $6.02/h for 8× A100 80GB is an abnormally low price compared to hyperscalers, and even against specialized GPU clouds. At this level, the question isn't "is it expensive?" — it's: "what are the trade-offs?" (network, egress, stability, data locality, interconnect, etc.).
Benchmark #2: 8× H200 141GB (Frontier Inference / Heavy Training)
The H200 is NVIDIA's latest powerhouse with 141GB HBM3e memory — perfect for running the largest models or maximizing inference throughput.
| Provider (8× H200) | Total $/h | $/GPU-h | $/month (720h) |
|---|---|---|---|
VoltageGPUBest Price | $26.60 | $3.33 | $19,152 |
| RunPod (×8) | $28.72 | $3.59 | $20,678 |
| AWS (est. p5e) | $34.64 | $4.33 | $24,941 |
| CoreWeave | $50.44 | $6.31 | $36,317 |
| Azure (est. ND96isr H200 v5) | $84.80 | $10.60 | $61,056 |
Pro Verdict
Example: 2× H200 NVL at $7/h = $3.50/GPU-h. That's in the excellent range vs RunPod H200 (~$3.59/GPU-h) and very aggressive vs CoreWeave. Again: check network + I/O + uptime before calling it a steal.
Mini-Benchmark: Solo GPU (Dev-Friendly Inference & Fine-Tuning)
For most developers, the real daily choice is: RTX 4090 / 5090 / L40S / RTX 6000 Ada. Here are some reference points:
| GPU | RunPod Community | RunPod Secure | VoltageGPU |
|---|---|---|---|
| RTX 5090 | ~$0.52/h | ~$0.69/h | Coming soon |
| L40S | ~$0.79/h | Higher | $0.49/h |
| RTX 4090 | ~$0.44/h | ~$0.59/h | $0.39/h |
| 3× RTX 4090 bundle | N/A | N/A | $0.74/h |
Pro Verdict
For dev/inference, compare $ / GB VRAM / hour + network stability, not just $/h. If you're deploying a model like Qwen3-32B, remember that full fp16 is VRAM-hungry (VRAM + KV cache). The model is 32.8B parameters and can handle 32k native context (or more with YaRN), so VRAM costs explode when you push context length.
Hidden Costs (Where Hyperscalers Catch Up)
💸 Egress (Internet Outbound)
AWS charges for outbound data (~100GB/month free, then $/GB by zone).Azure has similar "Bandwidth" pricing.CoreWeave advertises "no ingress/egress fees".
⏱️ Minimums & Availability
On AWS, some recent GPU offerings use Capacity Blocks (reservation windows), which completely changes the "dev experience". Prices can also shift with announced reductions (AWS announced GPU instance price cuts in 2025).
Translation: If you're training with datasets that move a lot, or serving a high-traffic API, the $/h isn't the end of the story.
So... Yes: These Pods Look Like a Good Deal (With 3 Quick Checks)
✅ VoltageGPU Deal Checker
On pure pricing benchmark, these numbers are aggressive vs AWS/Azure/GCP and very solid vs RunPod/CoreWeave (depending on SKU).
The 3 Checks I Always Do Before Renting
Real Network (Up AND Down)
If you see 0 Mbps upload / very low: dataset transfer, checkpoints, logs = pain. Run iperf3 to a public endpoint.
Storage + IOPS
NVMe local vs slow disk: on training, you'll notice immediately. Run fio to check read/write speeds.
Stability
"Stable 24h+" + long uptime = good signal. VoltageGPU listings show uptime, which is very useful.
Bonus: A "Real Benchmark" You Can Publish (And Readers Can Reproduce)
🔬 Reproducible 30-Minute Benchmark
If you want a 100% solid blog post, run a reproducible benchmark:
- GPU:
nvidia-smi(driver, VRAM, perf state) - Disk:
fio(read/write, IOPS) - Network:
iperf3to a public endpoint or small VPS - LLM throughput: vLLM + script measuring TTFT (time-to-first-token) and tokens/sec
- Normalized cost: $ / 1M tokens generated (from measured tokens/sec)
And conclude with: "Here's the real cost on my prompt, my batch size, my context, my temperature — not a marketing number."
Disclaimer: Prices shown are public list prices as of January 2026. Actual costs may vary based on region, availability, spot pricing, and enterprise agreements. Always verify current pricing on provider websites before making decisions.
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