GPU comparisons lead with FLOPS, which is the wrong headline for most work. Inference speed is set by memory bandwidth, training time by compute and interconnect, and rendering by neither. This guide compares the cards on the axis that matches your workload, with the prices and configurations MassiveGRID actually offers rather than list prices for hardware you cannot rent.
The three cards here sit at genuinely different points, and not on a single quality axis. The RTX 6000 Ada offers the most memory per dollar and the slowest memory. The A100 gives up some capacity for roughly double the bandwidth. The H100 costs more than either and delivers around three times the A100’s compute. A FLOPS ranking collapses all of that into one number and hides the tradeoff that actually decides the purchase.
The MassiveGRID GPU catalogue: RTX 4000 Ada 20 GB from $449.99/mo · RTX 6000 Ada 48 GB from $1,420.85/mo · A100 40 GB from $1,649/mo · A100 80 GB from $2,499/mo · H100 80 GB from $3,999/mo · H100, H200 and B200 configurations up to 1,440 GB of VRAM by quote
GPU cloud with hourly and monthly options
Dedicated GPU servers with fully managed HA deployment
The Cards Side by Side
| RTX 6000 Ada | A100 40 GB | A100 80 GB | H100 80 GB | |
|---|---|---|---|---|
| Memory | 48 GB GDDR6 ECC | 40 GB HBM2e | 80 GB HBM2e | 80 GB HBM3 |
| Bandwidth | ~960 GB/s | ~1.6 TB/s | ~1.9 TB/s | ~3.4 TB/s |
| FP16 tensor | ~360 TFLOPS | 312 TFLOPS | 312 TFLOPS | 989 TFLOPS |
| FP8 support | Yes | No | No | Yes |
| Compute capability | 8.9 | 8.0 | 8.0 | 9.0 |
| NVLink | No | Yes | Yes | Yes |
| MassiveGRID price | $1,420.85/mo | $1,649/mo | $2,499/mo | $3,999/mo |
| Hourly on-demand | — | $2.26/hr | $3.42/hr | $5.48/hr |
Bandwidth figures are approximate and vary between SXM and PCIe variants. TFLOPS figures are tensor-core throughput as published for these configurations.
Why Bandwidth Decides Inference
Generating one token requires reading every active parameter out of memory. Nothing about that is compute-intensive; it is a memory sweep. So the theoretical ceiling on generation speed is bandwidth divided by model size.
Take a 4-bit 70B model at roughly 41 GB of weights:
| Card | Bandwidth | Theoretical ceiling |
|---|---|---|
| RTX 6000 Ada | ~960 GB/s | ~23 tokens/sec |
| A100 80 GB | ~1.9 TB/s | ~46 tokens/sec |
| H100 80 GB | ~3.4 TB/s | ~83 tokens/sec |
Real throughput lands below these numbers, and batching improves aggregate throughput considerably because a batched forward pass amortises the memory read across several sequences. But the ordering never changes, and no amount of tuning makes a GDDR6 card match an HBM3 one on single-stream generation.
This is why the RTX 6000 Ada is an awkward recommendation for interactive serving despite having 48 GB. It holds larger models than an A100 40 GB, and it generates their tokens more slowly.
Why Compute Decides Training
Training inverts the picture. Backpropagation is dense matrix multiplication, gradients and optimiser state must be held alongside weights, and the job runs for days rather than milliseconds. Here the H100's roughly threefold FP16 advantage over the A100 translates almost directly into wall-clock time.
FP8 widens the gap further. On Hopper and Ada cards, training and inference in FP8 roughly halves memory traffic against FP16 with careful scaling. The A100 cannot do it at all, being compute capability 8.0.
The economic comparison is worth doing properly. An H100 at $3,999/mo is 60 percent more expensive than an A100 80 GB at $2,499/mo. If it finishes the run in a third of the time, it is substantially cheaper per experiment, and the researcher waiting on the result is usually the most expensive component in the system.
Interconnect Is Not a Footnote
The RTX 6000 Ada has no NVLink. For a single-card workload that is irrelevant. For anything sharded across cards it is decisive, because tensor parallelism requires collective communication at every forward pass and PCIe becomes the bottleneck immediately.
If your roadmap includes models that will not fit on one card, choose a card that can be paired. A100 and H100 both support NVLink, and multi-GPU MassiveGRID configurations with NVLink and InfiniBand are available on request. Buying the cheapest card with the most VRAM is a decision that can quietly foreclose that option.
Matching Cards to Workloads
| Workload | Recommended | Reasoning |
|---|---|---|
| Serving models up to 13B | RTX 4000 Ada | 20 GB is enough at 4-bit and it is the cheapest entry at $449.99/mo |
| Batch inference, latency irrelevant | RTX 6000 Ada | Most VRAM per dollar. Queue depth hides the bandwidth deficit |
| Interactive serving up to 34B | A100 40 GB | HBM2e bandwidth is what users feel. Hourly option at $2.26/hr for spiky demand |
| Serving 70B to real users | A100 80 GB | Fits 4-bit 70B with a usable KV cache |
| High-concurrency serving | H100 80 GB | Bandwidth and FP8 let one card serve many more sessions |
| Fine-tuning up to 13B | A100 80 GB | Headroom for optimiser state with LoRA or QLoRA |
| Full fine-tuning, large models | H100, multi-GPU | Compute-bound and memory-hungry. NVLink required |
| 3D rendering, CAD, visualisation | RTX 6000 Ada | Ada RT cores and display outputs. Data center cards lack both |
| Scientific computing, FP64 | A100 or H100 | Consumer and workstation cards have weak double precision |
Hourly or Monthly
The A100 40 GB at $2.26/hr reaches its $1,649 monthly price at around 730 hours, which is roughly a full month. So the arithmetic is simple: continuous workloads belong on monthly billing, and anything running less than about three weeks a month is cheaper hourly.
Hourly suits training runs, evaluation sweeps and proof-of-concept work with a defined end. Monthly suits a production endpoint, where you also want the reserved capacity rather than depending on availability at the moment you need it.
The Mistake Worth Avoiding
Buying on VRAM alone is the most common and most expensive error. Two cards with the same capacity can differ by a factor of three in bandwidth, which is the number that determines how fast tokens appear.
Work in this order. Establish the model and context you need, size the VRAM properly including the KV cache, then choose among the cards that clear that bar by bandwidth for inference or by compute for training. Our VRAM requirements guide covers the sizing step, including the cache arithmetic that catches people out.
Dedicated Hardware on HA Infrastructure
Benchmarks only transfer to production if the card is dedicated. On oversubscribed platforms, neighbouring tenants change both throughput and available memory, and the variance is invisible until it matters.
Every MassiveGRID GPU instance gives you the whole card, with substantial CPU and RAM alongside it: 16 vCPU and 120 GB with the A100 40 GB, 24 vCPU and 240 GB with the A100 80 GB, 32 vCPU and 480 GB of DDR5 with the H100. That system memory is not decoration, since tokenisation, data loading and retrieval layers all run on the host side.
The platform underneath is the same one running the rest of MassiveGRID: a Proxmox high-availability cluster with automatic failover, Ceph triple-replicated NVMe storage, 12 Tbps DDoS protection, R1Soft backup, and placement in any of the 85+ metros on our datacenter map. The AI stack, including PyTorch, CUDA, cuDNN, Docker with the NVIDIA container runtime, Jupyter, Hugging Face libraries and vLLM, is pre-installed.
For requirements beyond a single card, H100, H200 and B200 configurations reaching 1,440 GB of VRAM are available by quote with NVLink and InfiniBand. Configure a dedicated GPU server or talk to the AI solutions team.