Quick Answer
The NVIDIA A100 80GB is an older Ampere-generation GPU that still suits budget batch inference. The H100 (80GB HBM3, 3.35 TB/s) is the best cost-per-token GPU for models that fit in 80GB. The H200 has 141GB HBM3e and 4.8TB/s bandwidth, giving nearly double H100 memory and 1.4x memory bandwidth. It can make 70B-class inference easier, but whether one GPU can serve a 70B model depends on precision, context length, KV cache, concurrency, batching and runtime overhead. The B200 (Blackwell, 180 to 192GB, 8 TB/s, native FP4) delivers roughly 2x training and up to 4x inference versus the H100 per MLPerf v4.1. The GB200 NVL72 pools 72 B200s into one 13.4TB liquid-cooled rack built for trillion-parameter models.
First check total memory fit, then benchmark cost per completed job or cost per token under the required latency SLO. Also include compute throughput, memory bandwidth, interconnect, storage, power/cooling, software maturity and availability.
Every team eventually faces the same expensive question. Which NVIDIA GPU actually fits the workload, and which one just fits the hype? The wrong answer is rarely obvious. It hides in a fine-tune that costs double because two 80GB cards did what one 141GB card could, or in a Blackwell rack serving a model that fit a single H100. With five GPUs across three architectures, a 23x spread in cloud pricing, and marketing claims of 30x speedups, spec sheets alone will mislead you. This comparison cuts through it with verified benchmarks, real INR pricing, and one decision rule.
Comparing Nvidia B200 vs H200 vs GB200 vs H100 vs A100
The table below cuts through NVIDIA’s naming complexity, comparing A100, H100, H200, B200, and GB200 NVL72 by architecture, memory, performance, and scale.
| Specification | A100 SXM | H100 SXM | H200 SXM | B200 GPU* | GB200 NVL72 |
|---|---|---|---|---|---|
| Product type | Single GPU | Single GPU | Single GPU | Single GPU | Complete liquid-cooled rack |
| Architecture | Ampere | Hopper | Hopper | Blackwell | Grace + Blackwell |
| Configuration | 1 GPU | 1 GPU | 1 GPU | 1 GPU | 36 Grace CPUs + 72 Blackwell GPUs |
| GPU memory | 80 GB HBM2e | 80 GB HBM3 | 141 GB HBM3e | 180 GB HBM3e* | 13.4 TB HBM3e total |
| Memory bandwidth | 2.039 TB/s | 3.35 TB/s | 4.8 TB/s | 8 TB/s* | 576 TB/s total |
| FP8, dense | Not supported | 1.979 PFLOPS | 1.979 PFLOPS | 4.5 PFLOPS* | 360 PFLOPS total |
| FP4, dense | Not supported | Not supported | Not supported | 9 PFLOPS* | 720 PFLOPS total |
| NVLink bandwidth | 600 GB/s | 900 GB/s | 900 GB/s | 1.8 TB/s* | 130 TB/s domain |
Note: *B200 values are derived per GPU from NVIDIA’s eight-GPU DGX B200 totals: 1,440 GB memory, 64 TB/s HBM bandwidth, 36 dense FP8 PFLOPS, 72 dense FP4 PFLOPS, and 14.4 TB/s aggregate NVLink bandwidth.
H100: Still the Cost-Per-Token King
- Memory: 80 GB HBM3 at 3.35 TB/s
- Power: 700W (SXM)
- Performance: 3 to 5x A100 on transformer workloads. Up to 4x GPT-3 175B training per NVIDIA and 4.5x inference per MLPerf
- Multi-GPU: NVLink Gen4, 900 GB/s
- Cost: From ₹1,80,000/month
The NVIDIA H100 introduced the Transformer Engine and FP8 precision, and four years on it remains the workhorse of production AI. It has the deepest, most battle-tested software stack in the lineup, the widest availability across 36+ cloud providers, and rental rates down 64 to 75% from their 2023 peak. If your models fit in 80GB at your target precision, meaning anything up to 34B native or 70B quantized, nothing beats its cost per token.
What it does poorly: Anything memory-bound. A 70B model at FP8 needs two H100s with tensor-parallel overhead where one H200 does the job alone. If you’re constantly sharding models across cards, you’ve outgrown it.
Run H100 when: Models fit 80GB, cost-per-token is the KPI, or you’re extending an existing H100 fleet.
Skip it when: Your KV cache is eating VRAM or you’re serving 70B+ models unquantized.
H200: The Memory Upgrade Everyone Misreads
- Memory: 141 GB HBM3e at 4.8 TB/s, which is 76% more capacity and 43% more bandwidth than H100
- Power: H200 SXM has similar power class and is designed as a Hopper memory upgrade, but “drop-in H100 swap” should be replaced with “validate server BIOS/firmware, thermal design, tray compatibility, driver stack, cloud SKU and vendor support before assuming upgrade compatibility”
- Performance: Same Hopper-generation compute class as H100, with larger/faster memory. For memory-bound LLM inference, H200 can be faster, but the uplift should be quoted from a specific benchmark or measured on your model
- Multi-GPU: NVLink Gen4, 900 GB/s
- Cost: From ₹2,22,775month (₹381.46/hr)
Here’s the fact that saves (or wastes) lakhs. The NVIDIA H200 uses the identical GH100 die as the H100. Same CUDA cores, same Tensor Cores, same Transformer Engine. What changed is memory, and for memory-bound work that changes everything. One H200 holds a 70B model at FP8 with KV-cache headroom, replacing two H100s and their interconnect overhead. Long contexts, RAG pipelines, and high-QPS serving all feed on that bandwidth.
What it does poorly: Compute-bound work. If your workload maxes out cores rather than memory, the H200 performs exactly like an H100 at a higher price. You’d be buying a bigger fuel tank for a car with the same engine.
Run H200 when: You serve 70 to 100B models, 32K+ contexts, or KV-cache-heavy workloads.
Skip it when: You’re compute-bound, or your models comfortably fit 80GB.
A100: The Budget Bridge, Not a Foundation
- Memory: 80 GB HBM2e at 2.0 TB/s
- Power: 400W
- Performance: No FP8, and an H100 finishes transformer jobs 3 to 5x faster
- Status: End-of-life since Feb 2024
- Cost: ₹90,000/month
The NVIDIA A100 powered the first LLM wave, and at ₹125/hr it’s still the cheapest ticket into serious GPU compute. For batch inference, embedding pipelines, and stable legacy workloads that fit 80GB, it quietly earns its keep.
What it does poorly: Modern precision. No FP8 means every transformer job runs slower and often costs more per job than an H100 despite the lower hourly rate. Do that math before assuming cheap wins.
Run A100 when: The workload is stable, fits 80GB, and migration costs more than it saves.
Skip it when: You’re building anything new. It’s a bridge with a 2 to 3 year exit, not a foundation.
B200: The First Real Leap Since 2022
- Memory: 180 GB HBM3e at 7.7 to 8.0 TB/s
- Power: 1,000W, so dense racks exceed 50kW and effectively require liquid cooling
- Performance: 2x GPT-3 175B training, 2.2x Llama-70B fine-tuning, and up to 4x inference with FP4 per MLPerf v4.1, plus ~2.5x H200 tokens/sec
- Multi-GPU: NVLink Gen5, 1.8 TB/s
- Cost: $30K to 50K to buy
Blackwell doesn’t refine Hopper. It retires it. Two reticle-limit dies totaling 208 billion transistors are fused into one GPU, fifth-generation Tensor Cores bring native FP4, and interconnect bandwidth doubles. Blackwell FP4/NVFP4 can improve inference throughput on supported models and software stacks, but quality must be validated per model, task and quantization recipe. Do not claim FP4 keeps quality acceptable for most production inference without benchmark evidence.
And about that 30x keynote number. It compared 72 air-cooled H100s against one liquid-cooled NVL72 rack at an operating point where the H100s had already collapsed. Independent re-analysis by Adrian Cockcroft puts the realistic rack-scale gain at 5.3 to 8.3x. Budget on the MLPerf 2 to 4x per GPU and enjoy anything beyond as a bonus.
What it does poorly: Dropping into your existing datacenter. At 1,000W it demands power and cooling your H100 racks don’t have, and its FP4 software stack is newer than Hopper’s mature FP8 path.
Run B200 when: Models need 141 to 192GB, inference tolerates FP4, or training runs exceed 500 GPU-hours, where 2.2x speed compounds into real money.
Skip it when: Models fit smaller cards, or you can’t feed and cool it.
GB200 NVL72: A Different Shape of Computer
- Configuration: 36 Grace CPUs + 72 B200 dies in one NVLink domain
- Memory: 13.4 TB HBM3e pooled, so software sees one colossal GPU
- Power: 120 kW per rack with mandatory liquid cooling
- Performance: Up to 3.4x per-GPU vs 8x H200 on Llama 3.1 405B. On DeepSeek-R1 it delivers 5,790 vs 2,558 tok/s/GPU against B200 nodes at $0.11 vs $0.23 per million tokens per SemiAnalysis InferenceX
- Cost: $2M to 3M per rack. From ~$8/GPU-hr as reserved capacity only
The NVL72 isn’t a bigger card. It’s the whole house. When a trillion-parameter model plus its KV cache outgrows every single node you can buy, this pooled 13.4TB domain is the entry ticket, not a luxury. Retrofitting an air-cooled facility for 120kW racks runs $5M to 10M per megawatt, which is exactly why most teams should rent this class of capacity rather than build for it.
Run GB200 NVL72 when: The model literally cannot fit anywhere else.
Skip it when: Your model fits one card, because then the rack is just very expensive furniture.
Which GPU Fits Your Model?
LLM inference is memory-bound. Token speed depends on how fast weights and the KV cache move from HBM into the compute cores, and the KV cache grows with every token of context you serve. Match memory to the model first and the rest of the decision gets easy.
| Model Size | Minimum Viable Config | Why |
|---|---|---|
| Up to 34B parameters | 1x H100 (80GB) | Fits with KV headroom at FP8/FP16 |
| 70B at FP8 | 1x H200 | Replaces 2x H100 with no tensor-parallel overhead |
| 405B at FP8 | 3x B200 (vs 4x H200) | The saved GPU also removes interconnect overhead |
| 671B MoE (DeepSeek-class) | 4x B200 (vs 9x H100) | Full model resident in Blackwell VRAM |
| Trillion-parameter, real-time | GB200 NVL72 | Model plus KV cache span the pooled 13.4TB domain |
How to Choose Between the A100, H100, H200, B200, and GB200 NVL72
| GPU | Reach for It When | Skip It When | The One-Line Verdict |
|---|---|---|---|
| H100 | Models fit 80GB at your target precision and cost per token is the prize | KV cache or model size forces constant sharding across cards | Nothing else pairs that price with that software maturity |
| H200 | Memory is your bottleneck, meaning 70 to 100B models, long contexts, or KV-cache-heavy serving | Compute is your bottleneck, because under the hood it is the same engine as the H100 | Buy it for the memory, not the speed |
| A100 | A stable legacy workload fits 80GB and moving it costs more than it saves | You are building anything new | A bridge, never a foundation |
| B200 | Models need 141 to 192GB, inference tolerates FP4, or training runs stretch past 500 GPU-hours | Models fit smaller cards, or your facility cannot feed and cool 1,000W GPUs | Its speed compounds into real money on long runs |
| GB200 | A model plus its KV cache outgrows every single node you can buy | The model fits one card | Otherwise, the rack is just very expensive furniture |
Match the GPU to the Model, Not the Hype
Now you know the rule this entire comparison proves. Match GPU memory to your model first, then compare the total cost of completing the workload, never cost per hour. One H200 replaces two H100s for 70B serving. A B200 finishing 2.2x faster beats a cheaper card on total spend. A GB200 NVL72 only earns its rack when nothing else holds the model.
AceCloud puts every tier within reach, with the H100 from ₹1,80,000/month, the H200 from ₹2,22,775/month, and B200 waitlist access.
Not sure where your workload lands on the ladder? Book a free consultation with an AceCloud GPU engineer and buy memory, not marketing.
Frequently Asked Questions:
For training runs over roughly 500 GPU-hours or large-model inference, yes. The B200 completes jobs about 2.2x faster, so total job cost is often lower despite the higher hourly rate. For models under 70B or short experimental runs, the H200 is cheaper and available sooner.
Only for memory-bound workloads. Both use the identical GH100 die with identical compute. The H200 adds 76% more memory and 43% more bandwidth. Large-model inference gains 20 to 40% from bandwidth alone, but compute-bound training performs identically on both.
On AceCloud (Noida, INR billing, excluding taxes), a 1x A100 80GB starts at ₹125/hr or ₹90,000/month, a 1x H100 HGX at ₹1,80,000/month (~₹250/hr equivalent), and a 1x H200 NVL at ₹2,22,775/month (~₹381.46/hr equivalent). The B200 is on global rates from ~$4.00/hr. Rates move monthly, so verify current pricing before committing.
The B200 is a single Blackwell GPU rented by the hour. The GB200 Superchip combines two B200s with one Grace CPU on a board. The GB200 NVL72 is a liquid-cooled rack pooling 72 B200s into one 13.4TB memory domain, available only as reserved capacity.
Yes, at FP8 precision. A 70B model quantized to FP8 fits within the H200’s 141GB with headroom for KV cache, replacing the two H100s the same workload previously required. At FP16, with about 140GB of weights alone, it is too tight, so quantization or a second GPU is needed.
Deploy now if you have production workloads. Rubin, arriving H2 2026 onward, will carry a launch premium and constrained supply, just as Blackwell did. Renting rather than buying keeps you flexible, so you capture Blackwell performance today without owning hardware through the generational transition.
