Your AI workload is growing. Your current GPU infrastructure isn’t cutting it anymore. You’re looking at NVIDIA’s lineup and thinking:
- Do we really need H200?
- Is H100 still enough?
- Is L4 production-grade, or just the cheap option?
The risk is simple: choose wrong, and you burn money on overkill hardware or struggle with underpowered GPUs that miss latency and throughput targets.
You don’t need the most expensive GPU. You need the right GPU for your models, traffic pattern and scaling plan.
In this post, we’ll compare top five NVIDIA GPUs (H200, H100, A100, L40S and L4) with honest assessments of what each does well, what it does poorly, and exactly when you should (or shouldn’t) run it in production. By the end, you’ll have a clear, practical way to match each GPU to your actual workloads.
Let us compare these GPUs in detail.
TL;DR – Pick Your GPU Machine in 30 Seconds
| Your Situation | GPU to Use | Why |
|---|---|---|
| We’re training a 405B model or serving 100K-token contexts | H200 | Only GPU with enough memory (141GB). Period. |
| We’re serving 70B parameter models at scale with <100ms latency | H200 | 45% faster than H100. NVLink for multi-GPU. Worth the cost. |
| We have existing H100s and they’re working fine | Keep them | No urgent reason to migrate. Upgrade when 3-year ROI breaks even. |
| We’re adding capacity on a budget and H100s are working | H100 | Proven, consistent with your fleet. H200 upgrade ROI is 12+ months. |
| We need to serve thousands of small models (7B or less) cheaply | L4 | 72W power, dirt cheap, horizontal scaling beats vertical. |
| We’re doing real-time rendering and AI in one pipeline | L40S | Ada Lovelace graphics optimizations and inference in one box. This is its exact use case. |
| We have A100s. Should we upgrade? | Not yet | A100 is EOL (Feb 2024) but still viable if it’s working. Plan 2-3 year exit. |
The Specs That Actually Matter
Here’s what you need to know about each GPU. Everything else is noise.
H200: The Current Standard for Serious AI Work
- Memory: 141 GB HBM3e at 4.8 TB/s
- Power: Up to 700 W (SXM)
- LLM Inference Speed: Up to 2× NVIDIA H100 throughput on large LLMs; often 30–50% faster at similar batch sizes
- Multi-GPU Scaling: NVLink 4.0 (up to ~900 GB/s between GPUs)
- Cost: Typically high five-figure USD per card ($35–40K+, cloud pricing varies)
The NVIDIA H200 Tensor Core GPU is built for serious, large-scale generative AI and transformer-based LLM inference in modern data centers. With 141 GB of HBM3e and huge memory bandwidth, it lets enterprise AI teams run 100B+ parameter models, long-context chat, complex RAG pipelines and multimodal workloads without aggressive quantization or painful model sharding. In well-tuned GPU clusters, H200 usually wins on tokens-per-second and energy per token compared to H100, especially for production AI inference at high QPS.
H200 makes the most sense as the core of a new GPU cloud platform, high-density on-prem AI cluster or sovereign cloud where long-term AI infrastructure strategy matters. It is less compelling if your traffic is dominated by small LLMs and classic ML workloads that fit comfortably on cheaper data center GPUs. In those environments, the extra HBM capacity and NVLink bandwidth can sit underutilized while you carry higher capex and power per slot.
What it’s great at:
- Hosting 100B+ parameter LLMs and large transformer models without extreme quantization
- Long-context chatbots, AI agents and RAG systems with heavy token throughput
- Consolidating multiple H100/A100 nodes into fewer, denser high-end GPU servers
- High-throughput LLM inference with strict latency SLOs in production environments
- Mixed training + inference clusters where HBM bandwidth is the main bottleneck
- Improving energy efficiency per token vs H100 for large generative AI workloads
Why NOT to use it for everything:
- Primary workload is 7B–13B models where L4 or L40S offer better cost-per-inference
- Existing H100 fleet already meets latency, throughput and SLO requirements
- Budget constraints make the capex gap vs H100 hard to justify
- Roadmap does not require larger memory footprints or extended context windows
- You expect to refresh directly into Blackwell (B200) in the next hardware cycle
H100: The Proven Workhorse
- Memory: 80 GB HBM3 (SXM) or HBM2e (PCIe), up to 3.35 TB/s bandwidth on SXM
- Power: Up to 700 W (SXM)
- LLM Inference Speed: Baseline 1× in this comparison
- Multi-GPU Scaling: NVLink 4.0 (up to 900 GB/s between GPUs)
- Cost: Typically $28–32K per GPU (region and supply dependent)
The NVIDIA H100 Tensor Core GPU is still the default choice for many enterprise AI and MLOps teams standardizing their AI infrastructure. Built on the Hopper architecture, it delivers strong performance for LLM inference, model fine-tuning, classical deep learning and mixed HPC with AI workloads. For model sizes up to 70B – 80B parameters with reasonable context lengths, H100 gives predictable latency and throughput, especially when paired with frameworks like TensorRT-LLM, PyTorch and optimized CUDA libraries.
H100 is ideal when you want a homogeneous GPU fleet across hybrid cloud, hyperscalers and on-prem data centers. Operational playbooks, monitoring, observability and autoscaling pipelines are well-understood at this point. The main reasons to move beyond H100 are long-term memory constraints, aggressive context lengths, or a need to maximize tokens-per-rack as generative AI usage increases.
What it’s great at:
- Production LLM inference on model sizes up to ~70B–80B parameters
- Homogeneous GPU clusters where operational consistency and SRE playbooks matter
- Distributed training on mature Hopper-era tooling and CUDA/TensorRT ecosystems
- Mixed AI + HPC workloads using FP16/BF16 and tensor operations heavily
- Enterprise AI platforms standardizing on widely deployed data center GPUs
- Incremental scaling of existing H100-based GPU clouds and on-prem clusters
Why NOT to use it for everything:
- New workloads require larger single-GPU memory footprints than 80 GB HBM
- You are building net-new, long-lived AI infrastructure and can adopt H200 directly
- You must maximize tokens per second per rack for large transformer models
- Roadmap includes many long-context, multimodal and 100B+ parameter LLMs
- Power and cooling are fixed and you need better efficiency per token than H100 can deliver
A100: End-of-Life (Use for Legacy Only)
- Memory: Up to 80 GB HBM2e at >2 TB/s
- Power: Up to 400 W (SXM)
- LLM Inference Speed: Typically 2–3× slower than H200 per GPU on large LLMs
- Multi-GPU Scaling: NVLink 3.0 (up to 600 GB/s aggregate)
- Cost: Remaining inventory is discounted, but supply is shrinking
The NVIDIA A100 is an Ampere architecture data center GPU that powered the first wave of large-scale AI and ML platforms. It is now end-of-life, but many enterprises still run substantial A100 capacity in existing clusters. For classical deep learning, older NLP models, vision workloads and non-frontier LLMs, A100 remains serviceable and stable. If the hardware is fully depreciated and your platform is tuned for Ampere, it continues to provide useful AI compute without new capex.
However, for modern generative AI and high-volume LLM inference, A100 falls behind newer Hopper and upcoming Blackwell GPUs in both throughput and energy efficiency. Buying fresh A100 hardware today locks you into a legacy architecture just as model sizes, context windows and memory requirements are accelerating. The rational strategy is to treat A100 as legacy capacity and plan a staged migration to H100, H200 or B200 rather than building new AI infrastructure on an EOL platform.
What it’s great at:
- Running existing A100-based GPU clusters without new capital expenditure
- Classical ML, older NLP models and non-frontier generative AI workloads
- HPC jobs and simulation workloads tuned for Ampere architecture
- Environments with fully depreciated data center GPUs still in good condition
- Transitional capacity while planning migration to H100/H200/B200 GPU clouds
Why NOT to use it for everything:
- New deployments expected to run in production for 3–5 years or longer
- Frontier or near-frontier LLMs where efficiency and latency drive economics
- Environments standardizing on Hopper/Blackwell features and software stacks
- Scenarios where long-term support, spares and vendor roadmaps are critical
- Any “bargain” purchase justified only by short-term discount vs H100/H200
Also Read: NVIDIA H100 Price In India – Rent Or Buy?
L40S: For Graphics-Heavy and AI Pipelines
- Memory: 48 GB GDDR6 with ECC at 864 GB/s
- Power: Max 350 W
- LLM Inference Speed: Strong for small/medium models; constrained for very large, interactive LLMs
- Multi-GPU Scaling: PCIe Gen4 x16 only (no NVLink, no MIG)
- Cost: Roughly $8–12K per GPU
The NVIDIA L40S is an Ada-generation GPU designed as a hybrid for graphics, video workloads and AI inference. It combines high-performance Tensor Cores with strong RT and rasterization capabilities, making it ideal for media and entertainment pipelines, digital twins, 3D content creation, virtual production and real-time rendering with AI-enhanced effects. For 7B – 13B LLMs, diffusion models, vision transformers and multimodal inference, its 48 GB GDDR6 memory is often sufficient and delivers solid performance in modern production pipelines.
L40S fits best where you want a single GPU type to power both DCC (digital content creation) tools and AI workloads: rendering, generative video, scene understanding, asset generation and content upscaling. It is not positioned as a primary LLM training or large-model inference engine; lack of NVLink and reliance on PCIe bandwidth limit efficient model-parallel scaling compared to H100/H200. Think of L40S as a media and AI specialist GPU, not as a general-purpose replacement for high-end data center accelerators.
What it’s great at:
- Combined graphics with AI pipelines (rendering, VFX and neural networks together)
- VFX, animation, virtual production and real-time 3D content workflows
- Video analytics and generative video use cases
- Serving 7B – 13B LLMs and diffusion models where 48 GB VRAM is enough
- Vision, multimodal and generative image workloads in media pipelines
- Media/entertainment teams standardizing on one GPU for DCC tools and AI models
Why NOT to use it for everything:
- Large LLM training or inference that benefits from NVLink fabric bandwidth
- 70B+ parameter models that do not shard efficiently over PCIe-only interconnects
- Ultra-low-latency interactive LLMs where HBM-based GPUs reduce tail latency
- AI workloads that never use graphics, rendering or video acceleration
- Clusters designed around dense multi-GPU model parallelism per server node
L4: Horizontal Scaling on a Budget
- Memory: 24 GB GDDR6 at 300 GB/s
- Power: 72 W TDP
- LLM Inference Speed: Very efficient for small models; great cost-per-token at scale
- Multi-GPU Scaling: PCIe Gen4 x16 only (no NVLink, no MIG), designed for scale-out
- Cost: Typically $4 – 6K per GPU
The NVIDIA L4 is a low-power, low-profile data center GPU optimized for scale-out AI inference and edge deployment. With 24 GB of GDDR6 and a 72 W TDP, it allows you to pack multiple GPUs per server and deploy dense fleets across regions, POPs and on-prem environments. For 7B-class LLMs, recommendation systems, transcription, document intelligence, image and video analytics, L4 frequently delivers the best cost-per-inference and tokens-per-kilowatt compared to heavyweight H200/H100 nodes.
L4 is ideal for horizontal scaling and multi-region distribution: many small models, lots of traffic, strict cost controls. It is not designed for large-model training or advanced model-parallel setups. The 24 GB memory ceiling and PCIe-only interconnect limit its usefulness for 70B+ LLMs, very long-context transformers or large-scale fine-tuning. Use L4 as the backbone of a lightweight inference tier, not as your primary platform for frontier-scale generative AI.
What it’s great at:
- Serving 7B-parameter models to thousands of concurrent users at low cost
- Edge and POP deployments (low power, compact, easy to integrate in standard servers)
- Batch processing: speech recognition, transcription, document understanding, classification
- Video and image analytics at scale with modest model sizes
- Cost-per-inference optimization when models are small and easily replicated
- Packing many GPUs per rack within tight power and cooling budgets in data centers
Why NOT to use it for everything:
- 70B+ LLMs or large vision models that exceed 24 GB VRAM per device
- Training-heavy workloads that require high-bandwidth HBM memory
- Multi-GPU tensor/model parallelism inside a node for frontier-scale LLMs
- Ultra-low-latency, long-context chat where PCIe and GDDR6 increase tail latency
- Clusters where operational simplicity favors fewer, larger GPUs over many small ones
Key Specifications of NVIDIA H200, H100, A100, L40S, and L4
Let us compare the GPUs based on major specifications.
| Aspect | H200 | H100 | A100 | L40S | L4 |
| Architecture | Hopper (Next-gen) | Hopper | Ampere | Ada Lovelace | Ada Lovelace |
| Tensor Cores | 4th Gen | 4th Gen | 3rd Gen | 4th Gen | 4th Gen |
| Memory | 141 GB | 80 GB | 80 GB | 48 GB | 24 GB |
| Memory Bandwidth | 4.8 TB/s | 3.35/3.9 TB/s | 2039 GB/s | 864GB/s | 300 GB/s |
| Form Factor | SXM/PCIe | SXM/PCIe | SXM/PCIe | Dual-slot PCIe | Single-slot PCIe |
| Power Consumption | 700 W | 700 W | 400 W | 350 W | 72 W |
| FP64 | 34 TFLOPS | 34 TFLOPS | 9.7 TFLOPS | – | – |
| FP64 Tensor Core | 67 TFLOPS | 67 TFLOPS | 19.5 TFLOPS | – | – |
| FP32 | 67 TFLOPS | 67 TFLOPS | 19.5 TFLOPS | 91.6 TFLOPS | 30.3 TFLOPS |
| TF32 Tensor Core | 989 TFLOPS | 989 TFLOPS | 156 TFLOPS | 312 TFLOPS | 183 I 366* TFLOPS | 120 TFLOPS |
| BFLOAT16 Tensor Core | 1,979 TFLOPS | 1,979 TFLOPS | 312 TFLOPS | 624 TFLOPS* | 362.05 I 733* TFLOPS | 262 TFLOPS |
| FP16 Tensor Core | 1,979 TFLOPS | 1,979 TFLOPS | 312 TFLOPS | 624 TFLOPS* | 362.05 I 733* TFLOPS | 262 TFLOPS |
| FP8 Tensor Core | 3,958 TFLOPS | 3,958 TFLOPS | – | 733 I 1,466* TFLOPS | 485 TFLOPS |
| INT8 Tensor Core | 3,958 TOPS | 3,958 TOPS | 624 TOPS | 1248 TOPS* | – | 485 TOPS |
The Real Decision Matrix
Answer these 4 questions before buying or renting GPUs.
1. What’s Your Largest Model’s Memory Footprint?
| Model Size | Best GPU | Alternative | Avoid |
|---|---|---|---|
| >100B parameters or 100K+ tokens | H200 (141GB) | None. Won’t fit elsewhere | H100, L4, L40S |
| 70B-100B parameters | H200 or H100 (80GB with quantization) | L40S (48GB, tight fit) | L4 (too small) |
| 13B-70B parameters | H100 or L40S | H200 (overkill on memory) | L4 (too small) |
| <13B parameters | L4 or L40S | H100 or H200 (overkill) | A100 (EOL) |
2. What’s Your Latency SLA?
| Latency Requirement | Best GPU | Why | Bottleneck Risk |
|---|---|---|---|
| <50ms per token (interactive AI, real-time chat) | H200 or H100 | HBM memory + NVLink minimize latency | L4/L40S: GDDR6 latency + PCIe overhead |
| 50-200ms per token (acceptable for most) | H100, H200, or L40S | All viable; choose by model size | L4: Acceptable if model fits |
| >200ms per token (batch processing) | L4 | Throughput matters more than latency | None. L4 is fine here |
3. How Many Servers Are You Building?
| Scale | Strategy | GPU Choice | Rationale |
|---|---|---|---|
| 1-4 servers | Vertical scaling (one powerful box) | H200 or H100 | Use NVLink for multi-GPU within-server parallelism |
| 10-50 servers | Mixed approach | H200/H100 + L4 | Mix: expensive GPU for big models, cheap GPU for scale |
| 50-1000+ servers | Horizontal scaling (many cheap boxes) | L4 | PCIe bottleneck doesn’t matter; density wins |
4. What’s Your Budget and Timeline?
| Budget | Timeline | GPU Choice | Reasoning |
|---|---|---|---|
| Unlimited | Immediate | H200 | Best available, deploy now |
| Unlimited | Can wait | Wait for B200 (mid-2026) | 11-15× better throughput at similar cost |
| Tight | Immediate | H100 | Proven, cheaper than H200, deploy today |
| Tight | Can wait | H100 now, migrate to B200 later | Buy H100s for immediate need, plan 2-3 year migration |
| Very tight | Immediate | L4 | Only works for small models, but unbeatable cost-per-inference |
Also Read: Cloud GPU Pricing Comparison India
What’s Coming: Blackwell (B200/B300)
NVIDIA’s B200 and B300 are now in limited production. Here’s what you need to know:
Performance: 11-15× better LLM throughput versus Hopper (H100/H200)
Power: Same 700W TDP as H200 (more performance, same power bill)
Availability: Limited quantities from major cloud providers, wider availability Q2-Q3 2026
Pricing: Not yet public; expect 20-30% premium over H200 initially
Source: NVIDIA
Should you wait?
- Yes if: You can delay infrastructure for 6-12 months. B200’s cost-per-TFLOPS will eventually be superior.
- No if: You have revenue-generating workloads today. H200 delivers now; B200 is a 2026 story.
- Maybe if: You’re on the fence between H100 and H200. Consider H100 as interim; B200 ROI is clearer when pricing lands.
Realistic timeline: Expect B200 to be widely available at stable pricing by mid-2026. For now, H200 is your best option.
Learn:
Mistakes to Avoid
Following are common mistakes you need to avoid when choosing graphics card for your workloads:
- Buying “biggest GPU” for everything. L4 beats H200 on cost-per-inference for small models. Size doesn’t equal best.
- Assuming more memory = better. H200’s 141GB is only useful if your model needs it. Unused memory is wasted money.
- Forgetting about NVLink topology. L40S and L4 can’t scale efficiently beyond 4-8 GPUs per server. If you need 32-GPU training, only H200/H100 work with NVLink.
- Underestimating power/cooling infrastructure costs. 700W GPU needs industrial-grade infrastructure. 1,000 L4s (72W each) fit in spare data center capacity. The math changes.
- Treating A100 as a budget alternative in 2025. It’s EOL. Future support is unclear. The 20% cost savings aren’t worth it.
- Ignoring latency when comparing GPUs. H200 with HBM memory has 5-10× lower latency than L4 with GDDR6. Throughput-first thinking can tank your SLA.
Getting Started
You now have enough information to make a decision. Here’s what to do next:
Step 1: Answer the 4 questions above. Your GPU should be obvious now.
Step 2: Model the real costs for your workload:
- GPU hardware (cloud or on-prem)
- Power/cooling infrastructure
- Training/inference time-to-completion
Step 3: Calculate your ROI horizon. If it’s <12 months, the more expensive GPU pays for itself.
Step 4: Factor in your timeline. If you can wait 6 months for Blackwell, the equation changes. If you need to deploy today, H200 is your answer.
Frequently Asked Questions
H200 vs A100: which GPU should I choose for LLM inference?
For new LLM infrastructure, H200 is the clear choice. It delivers much more memory, higher bandwidth and better throughput per watt than A100 on large transformer models. A100 still works in existing clusters where the hardware is already paid for, but it is end of life and not a good target for fresh investment. Keep A100 as legacy capacity, and build new LLM stacks on H200 or H100.
What’s the difference between H100 and H200 for enterprise AI?
H100 and H200 share the Hopper architecture, but H200 adds a big jump in HBM capacity and bandwidth. H100 offers 80 GB HBM and is strong for models up to ~70B parameters with typical context lengths. H200 increases that to 141 GB HBM3e, which helps for 100B+ models, long-context chat, higher batch sizes and denser consolidation. If your workloads are hitting memory ceilings or you want more tokens per rack, H200 scales better. If your current models fit comfortably in 80 GB, H100 remains a solid choice.
L4 vs L40S: which NVIDIA GPU fits my workload?
Use L4 when you want efficient, low-power inference for small and medium LLMs, recommendation systems, document and video analytics at scale. It shines when you deploy many GPUs across regions or edge sites. Use L40S when your workloads mix graphics and AI: 3D rendering, VFX, virtual production, digital twins and generative video, plus some LLM or diffusion workloads. L4 is a scale-out inference GPU. L40S is a graphics-plus-AI specialist for media-heavy pipelines.
L4 vs A100: is it worth moving small-model inference off A100?
Yes, in most cases it is. A100 offers more raw compute on one card, but for small models (such as 7B LLMs and classic ML) L4 often wins on cost per inference and power efficiency. L4 uses far less power, fits more easily in dense nodes and is better aligned with modern scale-out deployment patterns. A100 is still fine where it is already installed, but for a new or refreshed inference tier, L4 is usually the better option. Read this to learn more.
Which NVIDIA GPU is best for 7B vs 70B models?
For 7B–13B models, L4 and L40S normally give the best economics, especially when you need to serve many users across multiple regions. For 70B+ models or long-context transformers, you typically want H100 or H200 with high-bandwidth HBM and NVLink. A100 can still run some large models, but as a legacy, end-of-life option, it should not be the basis for new large-model deployments.
Can I mix H200, H100, A100, L4 and L40S in the same cluster?
Yes, you can run a mixed GPU fleet, especially on Kubernetes or similar schedulers, but you should split them into separate node pools or instance groups. Keep latency-sensitive production LLM services on a specific GPU class (such as H100 or H200) and use other GPUs for batch jobs, media workloads or legacy services. Treat each GPU family as its own tier in your architecture to keep capacity planning, SLOs and debugging predictable.
Need Help Modeling This?
If you want to run actual numbers on your specific workload model size, QPS, latency SLA, budget AceCloud can help.
Our GPU infrastructure team has deployed thousands of GPUs across these exact scenarios. We can:
- Model your exact workload on each GPU and show you the performance/cost trade-offs
- Run pilot deployments to verify latency and throughput before committing
- Help you calculate multi-year TCO with real cloud pricing
- Guide you through infrastructure scaling as your workload grows
To learn more, book a free consultation with our experts now.
Book a free consultation and tell us your workload, and we’ll recommend the right GPU and show you the numbers.
Or if you’re ready to deploy:
- See current GPU availability & pricing: Check what’s available today
- Read our GPU buying guide: Deep dive on multi-GPU topologies and best practices
