A100 GPU Server Comparison 2026: Pricing, Specs & Best Hosting Providers

Hardware

A100 80GB — Specs & Variants

Ampere-generation data center GPU. The PCIe vs SXM4 variant and 40GB vs 80GB VRAM distinction determine which workloads you can run.

40GB vs 80GB: At FP16, LLaMA 3 70B requires ~140GB VRAM — impossible on a single 40GB A100. The 80GB runs LLaMA 3 70B at INT4/AWQ (~38–40GB) with KV cache headroom. If your target model is 30B+ parameters, 80GB is the floor, not an upgrade. Note: GPU Mart's multi-GPU A100 uses the 40GB variant (4× A100 40GB). For 80GB-per-GPU, the single-GPU A100 80GB server is the correct plan.

Pricing Explained

Why A100 Server Prices Vary So Much

The GPU chip is one cost component. CPU allocation, RAM, and storage differ dramatically between plans — and directly affect production performance.

Key impact: 120GB RAM constrains vLLM concurrency on 70B models; shared vCPUs add latency on tokenization and batching. Storage on RunPod is $0.07/GB/mo extra — 10TB = +$512/mo not visible in the headline rate.

Pricing Comparison

A100 80GB Server Pricing & A100 Hosting Comparison

Eight A100 GPU server options compared across infrastructure model, hardware spec, a100 server pricing, and true monthly cost. All a100 server price data verified June 2026.

Sources: RunPod May 2026; Lambda Labs on-demand; Hyperstack June 2026; HostKey provider data; AWS p4d.24xlarge $32.77/hr ÷ 8; GCP a2-highgpu-1g $5.07/hr. Verify before purchase.

GPU Selection Guide

A100 80GB vs H100, RTX A6000, RTX Pro 6000

Four GPU options on GPU Mart's platform compared side-by-side. See FAQ for a100 vs 4090 and a100 vs rtx pro 6000 detail.

FAQ

A100 Server — Common Questions

What is the cheapest A100 GPU rental in 2026, and when does flat-rate bare metal make more sense?

Cheapest per-hour: Hyperstack at $1.35/hr ($972/mo equivalent) and RunPod Community at $1.39/hr ($1,001/mo). Both add storage costs on top. At 10TB storage and 24/7 utilization, GPU Mart's A100 80GB (from $1,559/mo flat rate) is competitive — and delivers bare-metal physical hardware, 256GB RAM, and a 99.9% SLA that neither option provides.

Bare metal vs container: what actually changes for A100 LLM inference?

On a bare-metal A100 server you have full root access to the physical OS — any NVIDIA driver, kernel modules, huge pages tuning, custom CUDA builds. GPU access is via PCIe passthrough with zero virtualization overhead. On a container pod (RunPod) or cloud VM (Lambda, Hyperstack), driver versions are fixed to the image, kernel changes are not possible, and VM/container overhead reduces GPU throughput by 5–15%. For standard vLLM or Ollama, containers work fine. For production performance-critical inference or custom builds, bare metal removes a class of constraints entirely.

What is the best A100 GPU server for LLM inference in production?

For 24/7 production inference on 30B+ parameter models, the best a100 hosting combines: bare-metal dedicated hardware (no virtualization), 256GB+ system RAM for vLLM concurrency, and flat-rate pricing. GPU Mart's A100 80GB (from $1,559/mo) covers all three. For budget-constrained teams not needing 24/7 uptime, RunPod Secure Cloud ($1.49/hr) is a reliable runpod alternative with more flexibility.

What models can an A100 80GB server run, and at what throughput?

INT4/AWQ: LLaMA 3 70B, Qwen2.5-72B, most 30–70B models. FP16: up to ~35B. GPU Mart measured: LLaMA 3 70B INT4 → ~28 tok/s at batch=1 via vLLM on bare metal. 80GB leaves ~38–42GB headroom for KV cache, supporting 8–16 concurrent users at short context. For a 70B model GPU server, the A100 80GB is the minimum single-GPU option — and more cost-effective than H100 at $2,099/mo if FP8 isn't required.

How does GPU Mart compare to RunPod and Lambda Labs as a dedicated server alternative?

GPU Mart: physical bare-metal server, your A100 exclusively yours. RunPod: Docker container pods — Community uses 3rd-party hardware with no SLA; Secure Cloud more stable but still container-based. Lambda Labs: cloud VMs, good reliability, highest per-GPU rate among specialty providers ($2.79/hr = ~$2,009/mo). For teams evaluating a lambda labs alternative for cost, or a runpod alternative for dedicated infrastructure, GPU Mart's A100 80GB from $1,559/mo offers bare-metal performance at lower total cost than Lambda and comparable to RunPod Secure at 24/7 utilization.

A100 vs H100: which is better for LLM inference in 2026?

H100 has 3,350 GB/s bandwidth vs A100's 1,935 GB/s — ~1.7× faster per request, 2–3× better at high-concurrency batch serving. H100 also supports FP8. For teams where throughput is the primary constraint, H100 justifies the $540/mo premium ($2,099 vs $1,559). For moderate concurrency (under ~10 users) on 70B INT4, A100 delivers sufficient throughput at lower cost with a 5-year mature ecosystem.

A100 vs RTX 4090, A6000, RTX Pro 6000: which GPU?

RTX 4090 (24GB, ~$159/mo): insufficient VRAM for 70B models; good for up to ~13B at FP16. RTX A6000 48GB ($599+/mo): fits 70B INT4 with less headroom; 768 GB/s bandwidth is 2.5× slower for tok/s. RTX Pro 6000 96GB (from $479/mo VPS): more VRAM, Blackwell FP4, but workstation GPU without FP64/MIG and emerging ecosystem. A100 80GB wins when you need data-center compute (FP64, MIG), proven 5-year ecosystem, and 80GB VRAM for 70B+ models. For a100 vs a6000 cost-sensitive workloads under 48GB, A6000 saves $1,100/mo.

Does GPU Mart offer 4× A100 80GB? What's the A6000 NVLink config?

GPU Mart's multi-GPU A100 is 4× A100 40GB (160GB total VRAM), not 80GB. For 80GB-per-GPU, use the single-GPU A100 80GB server. For A6000: GPU Mart offers 1×, 3×, and 4× configs — NVLink is supported on 4× A6000 only (not 2×). 4× A6000 with NVLink = 4 × 48GB = 192GB pooled VRAM. For custom configurations, please contact our support team.