80GB GPU Servers for LLMs,
Fine-Tuning & Production AI
Rent an A100 GPU server or H100 GPU server with the full 80GB card — not a slice of one. Run 70B-class LLM hosting, large-batch fine-tuning, and high-concurrency LLM deployment on bare-metal, with one fixed monthly invoice instead of a volatile H100 rental bill.
A100 80GB & H100 Dedicated Server Plans
Two 80GB cards, two different jobs. Pick by FP8 requirement and concurrency target — not by raw spec sheet. Both are bare-metal: full SSH root, any CUDA version, no container layer between you and the silicon.
A100 80GB PCIe Dedicated Server — Ampere architecture, large-model production without an FP8 requirement.
H100 80GB PCIe Dedicated Server — Hopper architecture with FP8 Transformer Engine, built for production API throughput.
Enterprise Dedicated GPU Server - A100
- GPU Model: A100
- CPU: 36-Core Dual E5-2697v4
- Memory: 256GB RAM
- Disk: 240GB SSD+2TB NVMe+8TB SATA
- Bandwidth: 100Mbps Unmetered
- GPU Memory: 40 GB HBM2
- IP: 1 Dedicated IPv4
- Location: USA
Enterprise Dedicated GPU Server - A100(80GB)
- GPU Model: A100(80GB)
- CPU: 36-Core Dual E5-2697v4
- Memory: 256GB RAM
- Disk: 240GB SSD+2TB NVMe+8TB SATA
- Bandwidth: 100Mbps Unmetered
- GPU Memory: 80 GB HBM2e
- IP: 1 Dedicated IPv4
- Location: USA
Enterprise Multi-GPU Dedicated Server - 4xA100
- GPU Model: 4 x A100
- CPU: 44-core Dual E5-2699v4
- Memory: 512GB RAM
- Disk: 240GB SSD+4TB NVMe+16TB SATA
- Bandwidth: 1000Mbps Unmetered
- NVLink: 6xNVLink
- GPU Memory: 40 GB HBM2
- IP: 1 Dedicated IPv4
- Location: USA
Enterprise Dedicated GPU Server - H100
- GPU Model: H100
- CPU: 36-Core Dual E5-2697v4
- Memory: 256GB RAM
- Disk: 240GB SSD+2TB NVMe+8TB SATA
- Bandwidth: 100Mbps Unmetered
- GPU Memory: 80 GB HBM2e
- IP: 1 Dedicated IPv4
- Location: USA
See the full configuration list of all GPU server hosting and current promotions.
When Does a Workload Actually Need an 80GB VRAM GPU?
This isn't a marketing line — it's the VRAM math. An A100 GPU server and H100 GPU server both ship with 80GB of HBM, and that headroom is what separates a workload that runs from one that OOMs.
The math: a 70B-parameter model needs roughly 140GB of VRAM at FP16, ~70GB at INT8, and ~35–40GB at INT4/AWQ (full formula in GPU Mart's VRAM requirements guide). An 80GB card is the smallest single GPU that runs a 70B model at INT8 with real KV-cache headroom for concurrent users — the precision level most production LLM hosting teams pick over squeezing onto a 48GB card via INT4.
A Qwen2.5-72B or LLaMA 3 70B deployment at INT8 needs ~70GB just for weights — a 48GB card can't load it without INT4 compression that visibly degrades reasoning-heavy output.
Full-parameter or large-batch fine-tuning on 13B–70B models needs the extra headroom 80GB provides to avoid constant gradient-checkpointing slowdowns.
Vision-language models, and agent stacks running a planning LLM + tool-calling + memory retrieval concurrently, routinely push combined VRAM past 48GB even at moderate batch sizes.
RAG pipelines stacking an LLM + embedding model + reranker simultaneously consume 35–45GB before a single user request arrives, leaving a 48GB card no room for concurrency.
Production APIs serving 30+ simultaneous requests need KV cache scaling into double-digit gigabytes on top of weights — a 48GB card hits OOM exactly when traffic peaks.
Scientific/HPC workloads loading large in-memory datasets, and batch video generation with multi-LoRA pipelines, hit the same 48GB ceiling from a different direction.
A100 80GB vs H100: Which One Fits Your Workload?
The spec sheet says H100 wins everywhere. If you're trying to decide whether to rent A100 GPU or rent H100 GPU capacity for your LLM hosting stack, the decision in practice comes down to one question: does your workload use FP8?
Choose A100 80GB when
- Running 30–40B models at FP16, or up to ~40B at INT8, with no FP8-specific need
- Your framework or checkpoint doesn't yet support FP8
- Budget matters more than the last 20–40% of throughput — A100 costs 26% less than H100 at GPU Mart's rates
- Training or fine-tuning rather than serving a high-QPS production API
Choose H100 when
- Serving 100+ concurrent users on a production API — native FP8 Transformer Engine is the deciding factor, not TFLOPS
- Your model is FP8-quantized (Qwen3, Llama-class checkpoints) and you want the throughput gain
- Running 70–80B models at FP8 in a single-GPU footprint for production-grade latency
Real Inference Benchmarks: A100 vs H100 on 27B-FP8
Numbers from GPU Mart's own vLLM test bed, not vendor marketing slides. Input 1,024 tokens + output 512 tokens, Qwen3.6-27B at FP8 quantization.
| GPU | Concurrency | Mean TTFT (s) | Per-User Tok/s | Aggregate Tok/s | Mean E2E Latency (s) |
|---|---|---|---|---|---|
| A100-80G | 1 | 1.366 | 15.75 | 15.75 | 32.50 |
| A100-80G | 8 | 4.281 | 13.22 | 105.76 | 37.54 |
| A100-80G | 32 | 7.480 | 7.10 | 227.11 | 69.36 |
| H100-80G | 1 | 0.347 | 37.79 | 37.79 | 13.55 |
| H100-80G | 8 | 1.438 | 32.16 | 257.26 | 15.27 |
| H100-80G | 32 | 2.914 | 15.61 | 499.39 | 30.55 |
The TTFT (time-to-first-token) gap is what most comparisons skip: A100 climbs to a 7.48-second first-token delay at 32 concurrent requests — past the point where a chat UI feels responsive — while H100 stays under 3 seconds at the same load. For any FP8-quantized 27B+ model serving real-time traffic, H100 is the only single-GPU choice that holds up past 8 concurrent users; at lower concurrency or on FP16-only models, the gap compresses and A100 remains the lower-cost pick.
Source: GPU Mart production hardware, vLLM continuous batching. Full benchmark methodology at gpu-mart.com/guides/self-hosted-llm.
80GB GPU Hosting Cost Comparison, 2026
GPU Mart's headline rate isn't the lowest in the market — we won't pretend otherwise. If you're comparing A100 rental or H100 rental options and want the real H100 server cost, what matters is the all-in monthly total once storage, bandwidth, and infrastructure type are priced identically.
A100 80GB: True Monthly Cost at 720 Hours + 10TB Storage
| Provider | Infrastructure | GPU Config | Compute (720h) | 10TB Storage | True Monthly Total |
|---|---|---|---|---|---|
| GPU Mart | Bare-metal dedicated | A100-80G, 256GB RAM, 10.2TB disk incl. | $1,559 flat | Included | $1,559 |
| RunPod Community | Container, 3rd-party host | A100-80G, $1.39/hr | $1,000.80 | +$512.00 (Network Volume, $0.05/GB) | $1,512.80 |
| RunPod Secure | Container, RunPod DC | A100-80G, $1.49/hr | $1,072.80 | +$512.00 (Network Volume, $0.05/GB) | $1,584.80 |
| Hyperstack | Cloud VM | A100-80G, $1.35/hr | $972.00 | +$716.80 (block storage, $0.07/GB) | $1,688.80 |
| Lambda Labs | Cloud VM (SXM4) | A100-80G, $2.79/hr | $2,008.80 | +$2,048.00 (persistent FS, $0.20/GiB) | $4,056.80 |
| HostKey | Bare-metal | A100-80G, ~$3.47/hr equiv. | $2,496.00 | Base disk not published — custom 10TB quote required; est. +$200–400/mo | $2,696.00–$2,896.00 |
| AWS (p4d.24xlarge, per-GPU) | 8-GPU node minimum | A100-40G, ~$4.10/hr/GPU | $2,952.00 | +$230.00 (EBS gp3, $0.023/GB) + egress | $3,182.00+ |
| Google Cloud (a2-highgpu, per-GPU) | 8-GPU node minimum | A100-40G, $5.07/hr/GPU | $3,650.40 | +$400.00 (PD-SSD, $0.04/GB) + egress | $4,050.40+ |
Pricing collected June 2026 from public provider pages; reverify before purchase. RunPod/Hyperstack billing is per-second/minute — totals use 720 hours as a standardized monthly equivalent. AWS/Google Cloud A100 instances are 40GB (80GB only exists in 8-GPU minimums); HostKey doesn't publish a standard disk size, so its storage figure is an estimate.
H100 80GB: True Monthly Cost at 720 Hours + 10TB Storage
| Provider | Infrastructure | GPU Config | Compute (720h equiv.) | 10TB Storage | True Monthly Total |
|---|---|---|---|---|---|
| GPU Mart | Bare-metal dedicated | H100-80G, 256GB RAM, 10.2TB disk incl. | $2,099 flat | Included | $2,099 |
| Hyperstack | Cloud VM | H100-80G, $1.90/hr | $1,368.00 | +$715.18 (block storage, $0.07/GB) | $2,083.18 |
| RunPod (Secure Cloud) | Container, RunPod DC | H100-80G, $2.89/hr | $2,080.80 | +$512.00 (Network Volume, $0.05/GB) | $2,592.80 |
| Lambda Labs (PCIe–SXM) | Cloud VM | H100-80G, $2.99–$3.99/hr | $2,152.80–$2,872.80 | +$2,048.00 (persistent FS, $0.20/GiB) | $4,200.80–$4,920.80 |
| HostKey | Bare-metal | H100-80G, ~$3.54/hr equiv. | $2,546.00 | Base disk not published — custom 10TB quote required; est. +$200–400/mo | $2,746.00–$2,946.00 |
| AWS (p5.48xlarge, per-GPU) | 8-GPU node minimum | H100-80G, ~$12.29/hr/GPU | $8,848.80 | +$230.00 (EBS gp3, $0.023/GB) + egress | $9,078.80+ |
| Google Cloud (a3-highgpu, per-GPU) | 8-GPU node minimum | H100-80G, ~$11.06/hr/GPU | $7,963.20 | +$400.00 (PD-SSD, $0.04/GB) + egress | $8,363.20+ |
AWS p5.48xlarge and Google Cloud a3-highgpu-8g are 8-GPU-minimum instances — the per-GPU rate is the full node price ÷8; neither sells a single H100 below that node price. Reserved/committed-use discounts can cut hyperscaler rates 30–60% with multi-year lock-in.
Cost-per-compute: raw monthly price isn't the full picture — throughput per dollar is. GPU Mart's H100 delivers 499.39 aggregate tok/s at 32 concurrency for $2,099/mo, roughly $4.20 per tok/s. Forcing the same FP8 workload onto two 48GB cards via tensor parallelism costs more in cross-GPU overhead and doubled support surface for a model that fits on one 80GB card.
80GB vs Neighboring VRAM Tiers: When to Size Up or Down
80GB isn't always the answer. Here's where it sits relative to GPU Mart's other VRAM tiers, and when a neighboring tier is the better call.
48GB vs 80GB GPU
A6000, A40, RTX Pro 5000 (48GB) cover 30–35B models at INT4/FP8. Step up only when your model exceeds ~40B params or multi-model stacks push past 48GB. RTX Pro 5000 at $269/mo beats over-provisioning if 48GB fits.
Order RTX Pro 5000 →80GB vs 96GB GPU
RTX Pro 6000 (96GB GDDR7, $479/mo) has more VRAM but lower bandwidth than H100 and lacks its data-center FP8 Transformer Engine. Pro 6000 wins for budget single-card 120B-class quantized models; H100 wins for production API throughput.
Order RTX Pro 6000 →80GB vs 141GB GPU
H200-class 141GB HBM3e suits full-FP16 70B+ deployments without quantization compromise. Most production deployments already quantize to INT8/FP8 — for those, 80GB H100 covers the same model classes at lower monthly cost.
See all configurations →A100 vs RTX 5090
RTX 5090 (32GB, $399–479/mo) has excellent single-stream throughput but its 32GB ceiling caps it below A100 for anything over ~35B params. A100 wins once VRAM, not token speed, is the binding constraint.
Order RTX 5090 →Not sure where your workload lands? Explore all 37 GPU configurations and live pricing →
Who Should (and Shouldn't) Choose an 80GB Server
Good Fit
- Running 70B-class models (LLaMA 3 70B, Qwen2.5-72B) at INT8 or FP8, where 48GB forces a quality-degrading INT4 compromise
- Multi-model RAG or Agent stacks (LLM + embedding + reranker) that collectively exceed 48GB
- Production APIs targeting 30+ concurrent users where TTFT under load is a hard SLA requirement
- Teams past the break-even point on cloud LLM API spend (typically $300+/mo) who need dedicated, not shared, 80GB capacity
Not a Good Fit
- Models that fit comfortably in 24–48GB — RTX Pro 5000 ($269/mo) or A6000 ($409/mo) deliver better cost-per-token without unused headroom
- Short-duration experiments measured in hours — hourly cloud billing suits bursty testing better than a flat monthly invoice
- Thousand-GPU distributed pretraining needing InfiniBand-class interconnect — that scale belongs with Lambda Labs or CoreWeave
Risk-Free to Deploy
99.9% Uptime SLA
Backed by GPU Mart's own U.S. data centers — not a third-party host that can vanish mid-job. SOC-certified facilities available for compliance-sensitive workloads
<5 Min Support
24/7 in-house engineers, not a ticket queue or community Discord
Fixed Monthly Invoice
No per-second billing drift, no surprise storage or egress line items
Full Bare-Metal Root
Any CUDA version, any driver, any framework — no container layer in the way
FAQ: 80GB A100 & H100 Hosting
- Why is the GPU Mart A100/H100 hourly-equivalent rate not the cheapest on the market, and how does an A100 server or H100 server compare to AWS or Google Cloud?
- It isn't the cheapest raw rate — Hyperstack and RunPod Community post lower per-hour numbers, but those are shared cloud VMs or third-party containers, not a dedicated physical card. Once 10TB of comparably-billed storage is matched, GPU Mart's flat $1,559 (A100) and $2,099 (H100) land at or below every bare-metal competitor. The hyperscaler gap is bigger: AWS prices H100 only as an 8-GPU node (~$12.29/GPU-hr, ~$9,078+/mo per-GPU with storage), and Google Cloud runs similarly (~$8,363+/mo) — neither sells a single H100 below the full node price. GPU Mart's H100 is roughly 3.8–4.2× cheaper per card, with no 8-GPU minimum and no egress fees.
- Should I rent an A100 GPU or rent an H100 GPU on an hourly cloud platform instead of a dedicated server?
- For short experiments measured in hours, yes. For anything running continuously for weeks or months — most production LLM hosting and LLM deployment workloads — a flat monthly dedicated server works out cheaper once you total hourly billing over 720 hours, and you avoid the noisy-neighbor and cold-start issues that come with shared cloud GPU rental.
- Who shouldn't buy an 80GB GPU server?
- Anyone running models under ~35B parameters comfortably within 48GB, and anyone needing only a few hours of GPU time for one-off experiments. Both cases are better served by GPU Mart's 48GB-tier dedicated servers or a pay-per-hour cloud provider, respectively.
- Is bandwidth really unmetered on the A100/H100 dedicated servers?
- Yes — there's no data cap or overage billing, as long as usage doesn't impact other customers on the same rack. The default 100Mbps is a shared rate; upgrade to 200Mbps (shared) for $10/month or 1Gbps (shared) for $20/month. See current add-ons at gpu-mart.com/pricing.
- Is H100 always the fastest GPU for LLM inference?
- For 14B-class models at FP16, H100, RTX 5090, and RTX Pro 5000 all land around ~40 tok/s single-user — memory bandwidth is the bottleneck at that size, not compute. H100's edge shows up specifically at 80GB scale and FP8 precision: it's the only one of the three with both, making it the only single-GPU option for 70B–80B FP8 models at production concurrency.
- How much VRAM is required to self-host a 70B LLM?
- FP16 (full precision) needs ~140GB — beyond a single card. INT8 needs ~70GB, fitting an A100-80G or H100-80G. INT4/AWQ/GPTQ drops this to ~35GB, runnable on an RTX Pro 5000-48G or A6000-48G. For most production cases, INT4 on a single 48GB GPU delivers good quality with practical latency; if quality loss is unacceptable, INT8 on an 80GB card is the next step up.
Deploy Your 80GB GPU Server Today
Stop quantizing your model to fit hardware that's the wrong size for the job. Get the full 80GB card, the full root access, and one predictable invoice.
Deploy A100 80GB — $1,559/mo Deploy H100 — $2,099/moExplore More GPU Configurations
Not sure 80GB is the right fit yet? Compare neighboring tiers before you commit.
GPU specs sourced from NVIDIA official documentation. Benchmarks from GPU Mart production infrastructure via vLLM, dated 2026-06. Competitor pricing collected June 2026 — verify current rates before purchase. GPU Mart pricing subject to change; confirm at gpu-mart.com/pricing.