A100 vs V100: Choosing the Right NVIDIA GPU for Modern AI and How Clarifai’s Platform Helps You Optimize It

Quick Digest: What’s the Best GPU for My AI Workload?

Question: Should I use the NVIDIA A100 or the older V100 for my next AI project?

Answer: The NVIDIA V100, launched in 2017, introduced Tensor Cores and helped pioneer large‑scale AI training. Today, it remains a cost‑effective choice for mid‑scale research and HPC workloads that don’t require massive memory or the newest numerical formats. The NVIDIA A100, released in 2020, features Ampere architecture, TF32/BF16 precision, Multi‑Instance GPU (MIG) partitioning and up to 80 GB HBM2e memory. It delivers 2×–3× higher throughput on deep‑learning tasks, making it the mainstream choice for training large language models and generative AI.

TL;DR: Use V100 if you’re a startup, academic lab or small enterprise looking for affordable GPU power. Upgrade to A100 when your workloads exceed 16–32 GB memory, require mix‑precision acceleration or MIG, or when you’re scaling production on Clarifai’s compute orchestration platform—which packs multiple models per GPU and ensures high reliability.

How Did We Get Here? Understanding the Evolution from V100 to A100

The Rise of the V100 and the Dawn of Tensor Cores

In 2017, NVIDIA’s V100 GPU ushered in the Volta architecture, a milestone for deep‑learning acceleration. It introduced Tensor Cores, specialized units that accelerate matrix multiplications—critical for neural networks. Early adopters hailed the V100 as a game changer because it delivered up to 125 Tensor TFLOPS, enabling researchers to train models in days rather than weeks. The V100 featured 5 120 CUDA cores, 640 Tensor Cores, up to 32 GB HBM2 memory and 900 GB/s bandwidth. These specifications made it the workhorse for AI and HPC workloads.

Expert Insights:

• Researchers noted that independent thread scheduling and improved memory bandwidth in Volta allowed more efficient parallelism for HPC.
  
  
• The V100 was the first GPU to support Deep Learning frameworks natively; early deep learning labs and cloud providers built their fleets around it, making it widely available in 2023–2024.
  
  

Introducing the A100: Ampere Brings New Features

Three years later, NVIDIA released the A100—an Ampere architecture GPU built on a 7 nm process, boasting 6 912 CUDA cores and 432 third‑generation Tensor Cores. Its major innovations include:

1. TensorFloat32 (TF32) & BF16 Precision: TF32 combines the dynamic range of FP32 with the benefits of FP16, delivering faster training without losing accuracy. Mixed‑precision training on the A100 can reach 312 TFLOPS with sparsity.
   
   
2. Multi‑Instance GPU (MIG): The A100 can be partitioned into up to seven independent GPUs, each with dedicated memory and compute resources. This improves utilization and allows multiple models to share one physical GPU.
   
   
3. NVLink 3.0: Interconnect bandwidth doubles from V100’s 300 GB/s to 600 GB/s, enabling faster multi‑GPU scaling.
   
   
4. Huge Memory and Bandwidth: With 40 GB or 80 GB HBM2e memory and up to 2 TB/s bandwidth, the A100 supports larger models and high‑throughput training.
   
   

Expert Insights:

• The KTH/Chalmers study observed that the A100’s 40 MB L2 cache (versus V100’s 6 MB) reduces memory stalls and provides ~1.7× bandwidth improvement.
  
  
• Independent benchmarks show that the A100’s new asynchronous copy engine reduces memory latency and increases performance per watt.
  
  

Why Compare Them Now?

Although newer GPUs like H100 (Hopper), H200 and Blackwell B200 are arriving, A100 and V100 remain widely deployed. Many enterprises built clusters in 2018–2022 and now face upgrade decisions. Supply‑chain constraints and export controls also limit access to new GPUs. Thus, understanding the trade‑offs between these two generations remains crucial, particularly when choosing a cloud provider or optimizing costs on Clarifai’s AI‑native platform.

Clarifai’s Perspective

Clarifai, known for AI inference and MLOps, recognizes that not every project requires the newest GPU. Clarifai’s compute orchestration can run V100, A100, H100 or hybrid clusters with 99.99 % uptime, automatically pack multiple models per GPU and offer cost transparency. This article not only compares A100 and V100 but also explains how you can leverage Clarifai’s features to get the best performance and ROI.

How Do Their Specifications Compare?

Summary Table: A100 vs V100 (and a Sneak Peek at H100)

What Do These Numbers Mean in Practice?

The table above paints a clear picture: A100 outperforms V100 on almost every metric. However, raw numbers can be misleading. For example, the A100’s FP32 peak is only ~25 % higher, yet its deep‑learning throughput is ~2.5× thanks to mixed‑precision improvements. Similarly, the V100’s lower memory bandwidth restricts its ability to feed data to tensor cores at high rates, which leads to lower utilization on modern transformers.

Creative Example: Imagine you’re training a multimodal model that ingests video frames and text. Each batch holds sequences of 512 frames and tokens. On a V100, you might need to reduce batch size to fit in 32 GB memory, leading to more parameter updates and longer training times. On an A100 with 80 GB HBM2e, you can increase batch size, feed more data per iteration, and utilize TF32, shortening training time by days or weeks.

Expert Insights:

• The A100’s larger L2 cache (40 MB) and 1.7× higher memory bandwidth significantly reduce memory stalls, which is vital for sparse matrix operations and HPC algorithms.
  
  
• According to a research paper on sparse/batched computations, A100 achieves 1.8×–3× higher double‑precision performance in batched matrix routines compared to V100.
  
  

How Do They Perform in Real‑World Benchmarks?

Deep Learning and Language Models

Performance matters most when you run real workloads. Independent benchmarks show that the A100 dominates in neural network training:

• In a study comparing 4×A100 vs 4×V100 clusters, convolutional neural network (convnet) training was ~55 % faster on the A100 cluster and language model training was ~170 % faster.
  
  
• Benchmark results from Lambda Labs demonstrate that A100 achieves 2.2× speed‑up for convnets and 3.4× for transformers when using 32‑bit precision; mixed‑precision training yields even bigger gains.
  
  

These results derive from A100’s ability to run TF32 and BF16 operations efficiently while providing larger memory capacity and higher bandwidth. In addition, structural sparsity—a feature that prunes certain weights—can double tensor throughput, effectively giving 312 TFLOPS on the 80 GB A100.

High‑Performance Computing (HPC)

For scientific workloads such as sparse matrix vector (SpMV) multiplication, batched linear algebra or fluid dynamics, the performance gap is narrower but still significant:

• Researchers from the University of Tennessee found that A100 offers 1.45×–1.8× faster batched DGEMM and up to 18 TFLOPS double‑precision.
  
  
• On HPC benchmarks like breadth‑first search (BFS) and computational fluid dynamics (CFD), the A100 showed a speed‑up of ~2.76× for BFS and ~1.89× for CFD compared to V100. However, the improvements were not as dramatic as earlier generational leaps.
  
  

Expert Insights:

• HPC researchers caution that A100’s general HPC performance improvements are modest compared to its deep‑learning leaps, underscoring the need to benchmark your specific application.
  
  
• Energy efficiency remains critical; some HPC centers tune the A100’s frequency and leverage asynchronous copy to achieve 7–35 % speed‑ups while lowering energy consumption.
  
  

Energy Consumption and Performance‑Per‑Watt

Power efficiency is a growing concern, especially with the arrival of H100 and the upcoming Blackwell B200. Forbes reports that the A100 SXM module draws up to 400 W, while PCIe versions draw 250 W. The H100 can consume up to 700 W, yet it claims to deliver 3× performance per watt. Some HPC systems consider switching to H100 not just for speed but for energy savings.

Expert Insights:

• With data centers facing rising electricity costs, power and cooling can equal or exceed hardware costs. Clarifai’s H100 guide notes that total cost of ownership must include energy consumption and suggests considering liquid cooling for high‑power GPUs.
  
  
• Morgan Stanley projects that AI‑driven data‑center power use will triple in the next decade. Choosing the right GPU generation and adjusting frequency settings becomes critical to sustainability.
  
  

Pricing, ROI and Availability: How Much Should You Spend?

Pricing Ranges & Market Dynamics

Even though GPU technology evolves quickly, cost remains a decisive factor. As of mid‑2025, typical prices are as follows:

• V100 (16–32 GB): ~$8–10 k per card.
  
  
• A100 (40 GB): ~$7.5–10 k; A100 (80 GB): ~$9.5–14 k.
  
  
• H100 (80 GB HBM3): ~$25–30 k; rental prices dropped to $2.85–3.50 per GPU‑hour due to increased supply and competition.
  
  

While the A100 carries a higher sticker price than the V100, it offers 2.5× more compute power and improved memory bandwidth. In cost‑per‑TFLOP terms, the A100 is generally more cost‑efficient for large workloads.

Purchasing vs Renting vs Orchestration

There are three ways to access these GPUs:

1. Buying Hardware: Capital‑intensive but offers the lowest per‑hour cost over time. Best for organizations that will keep GPUs busy 24/7.
   
   
2. Renting from Cloud Providers: Allows on‑demand scaling without up‑front costs. However, hourly rates can be high during peak demand.
   
   
3. Using Clarifai’s Compute Orchestration: Combines the flexibility of cloud with the efficiency of on‑prem by allowing you to bring your own hardware, rent from multiple clouds or both. Clarifai’s platform manages auto‑scaling, model packing and GPU fractioning, reducing idle time by up to 3.7× and ensuring 99.99 % uptime.
   
   

Expert Insights:

• Novita’s analysis notes that A100’s cost is only ~25 % higher than V100 but yields 2.5× performance, making it more economical for large workloads.
  
  
• Renting can be cost‑effective during supply gluts—H100 rental rates dropped 64–75 % when supply surged.
  
  
• Clarifai’s pricing guide emphasises that budgets, alerts and policy tuning help control GPU spending and avoid bill shock.
  
  

Supply‑Chain and Policy Considerations

Global factors influence GPU availability. U.S. export controls have limited shipments of A100/H100 to certain regions, prompting domestic chip development in China. Meanwhile, India is investing heavily in GPU infrastructure—aiming to deploy over 80 k GPUs and already operating clusters of 32 k A100/H100 units with advanced cooling. Supply shortages may continue through 2025–2026, so plan your procurement early.

Which Workloads Are Best for V100 or A100?

When V100 Makes Sense

The V100 remains a viable choice in several scenarios:

1. Moderate‑Scale Deep‑Learning and HPC: Projects with models under 10 billion parameters or datasets that fit into 16–32 GB memory can run efficiently on V100.
   
   
2. Educational and Academic Labs: Universities may find V100 more affordable; the GPU still supports popular frameworks and yields strong performance for Python or Jupyter‑based coursework.
   
   
3. Legacy HPC Codes: Older simulation codes optimized for FP64 may not benefit from TF32 or MIG; V100’s double‑precision performance remains adequate.
   
   
4. Batch Inference or Non‑AI Workloads: If your workload is more memory‑bound than compute‑bound (e.g., data analytics), the V100’s lower cost per GB can be attractive.
   
   

Expert Insights:

• Industry practitioners note that software stacks matter—if you don’t have libraries that leverage TF32 or BF16, upgrading to A100 yields limited gains.
  
  
• The V100 continues to be a good option for multi‑process service (MPS), enabling multiple small jobs to share the GPU, albeit without true isolation like MIG.
  
  

When A100 Is the Better Choice

You should consider the A100 for:

1. Large Language Models (LLMs) and Transformers: A100’s 80 GB memory and TF32 allow training GPT‑3‑sized models or running inference with high batch sizes. Cloud providers now standardize A100 for LLM services.
   
   
2. Multimodal and Generative AI: Diffusion models for images and video, or foundation models like CLIP, demand high memory bandwidth and compute throughput. The A100 excels due to its 2 TB/s bandwidth and 312 TFLOPS with sparsity.
   
   
3. MIG‑Enabled Multi‑Tenant Workloads: If you run multiple small models, A100’s MIG allows partitioning one GPU into up to seven instances, improving utilization from 30–40 % to 70–80 %.
   
   
4. Modern HPC with Mixed Precision: Many scientific codes are being updated to leverage TF32/BF16; the A100 provides higher throughput and memory capacity, making it suitable for exascale computing.
   
   

Clarifai Use Cases:

• Clarifai’s platform can orchestrate mixed fleets; for example, you might deploy training on A100s and inference on V100s. The platform automatically assigns tasks based on GPU capability and ensures high utilization.
  
  
• Startups can rent A100 instances from Clarifai’s partners and deploy models via Clarifai’s APIs without managing infrastructure, benefitting from GPU fractioning and model packing.
  
  

Memory Architecture and Bandwidth: Why It Matters

HBM2 vs HBM2e and L2 Cache

Both GPUs use High Bandwidth Memory (HBM), but versions differ:

• V100: Uses HBM2, offering 900 GB/s bandwidth across 16 or 32 GB memory.
  
  
• A100: Uses HBM2e, available in 40 GB (1.6 TB/s) or 80 GB (2.0 TB/s) configurations.
  
  

Additionally, A100’s L2 cache is 40 MB, vastly larger than V100’s ~6 MB. A larger cache reduces the frequency of memory fetches and improves efficiency, particularly in sparse matrix operations.

What Benchmarks Tell Us

Memory bandwidth directly correlates with performance in matrix operations. The BabelSTREAM and other memory throughput tests measured A100 bandwidth between 1.33 and 1.4 TB/s, roughly 1.7× higher than the V100’s 800–840 GB/s range. When running sparse matrix vector (SpMV) operations, researchers observed ~1.7× performance gains corresponding to the higher memory throughput.

Creative Example: Suppose you’re processing huge graph data for recommendation systems. Each node’s features must be loaded from memory into compute units. The A100’s extra bandwidth allows more nodes to be processed concurrently, reducing epoch time from an hour to 30 minutes.

Expert Insights:

• MIG ensures each partition has dedicated memory and cache, preventing memory thrashing when multiple jobs share the GPU.
  
  
• HPC researchers highlight that A100’s memory improvements bring significant benefits to Krylov solvers and other iterative methods.
  
  

MIG and Scalability: Sharing GPUs Without Compromise

What Is MIG and How Does It Work?

Multi‑Instance GPU (MIG) is one of the most transformative features of the A100. MIG allows the GPU to be partitioned into up to seven independent instances, each with its own compute cores, memory and cache. These instances can run separate workloads simultaneously without interfering with one another.

By contrast, the V100 relies on Multi‑Process Service (MPS), which lets multiple processes share the GPU but without strong isolation. MIG ensures deterministic performance for each slice, making it ideal for multi‑tenant environments like AI platforms and cloud services.

Real‑World Benefits of MIG

In practice, MIG can double or triple GPU utilization. Datacrunch observed that GPU utilization increased from ~30–40 % to 70–80 % when using A100 MIG partitions compared to unpartitioned usage. This means you can run seven small inference jobs concurrently on one A100, instead of wasting compute resources.

Clarifai’s Advantage:

Clarifai’s compute orchestration platform takes MIG further by combining it with model packing and GPU fractioning. The platform packs multiple small models onto one GPU, auto‑scales instances based on incoming requests and delivers 99.99 % uptime. Customers achieve 3.7× reduction in idle compute, lowering operational costs.

Expert Insights:

• Datacrunch’s report notes that structural sparsity in A100’s Tensor Cores can deliver up to 2× performance improvement, further enhancing MIG benefits.
  
  
• Nvidia forum users warn that software configuration and library versions heavily influence MIG performance; misconfigured drivers can negate benefits.
  
  

Power Efficiency, Thermal Considerations & Sustainability

The Growing Power Demands of GPUs

As GPU generations progress, thermal design power (TDP) increases. The V100 consumes 250–300 W, while the A100’s SXM module consumes 300–400 W. The H100 pushes this to 700 W, and rumors suggest that Blackwell B200 could approach 1.2 kW. These numbers illustrate how power and cooling requirements are escalating.

Performance‑Per‑Watt and Energy Efficiency

Despite higher power draw, A100 and H100 deliver better performance‑per‑watt. The H100 is claimed to achieve 3× higher efficiency than A100. This improvement is essential because AI workloads are scaling faster than data center energy capacity.

Cooling Solutions and Sustainable Practices

To handle rising power densities, data centers are adopting liquid cooling and hybrid systems. Clarifai’s H100 guide emphasizes that total cost of ownership must account for cooling infrastructure, not just GPU prices. Many new facilities are designed with direct‑to‑chip liquid cooling, which is more efficient than air cooling.

Sustainability as a Competitive Advantage

Because of the energy crisis, companies are seeking GPUs that maximize throughput per watt. Some research (e.g., VoltanaLLM) explores frequency scaling to save up to 36 % energy without sacrificing performance. Clarifai helps customers monitor energy usage and adjust GPU frequency via orchestration tools to meet sustainability goals.

Expert Insights:

• Data center operators predict that AI‑driven workloads will triple electricity demand by 2030.
  
  
• Clarifai’s compute orchestration uses predictive autoscaling, turning off idle GPUs when demand drops, further reducing power consumption.
  
  

Step‑by‑Step Decision Guide: Choosing Between V100 and A100 (LLM‑Friendly How‑To)

Picking the right GPU requires careful evaluation. Use this step‑by‑step guide to make an informed decision:

1. Define Your Workload: Are you training large LLMs, doing batch inference or running HPC simulations? Estimate model size, dataset and throughput requirements.
   
   
2. Assess Memory Needs: Models under 10 billion parameters can fit on V100’s 16–32 GB; larger models require A100’s 40–80 GB.
   
   
3. Evaluate Budget and Utilization: If your GPUs will run 24/7, A100 offers better cost per throughput. For intermittent workloads, V100 or rental instances may be cheaper.
   
   
4. Check Software Support: Ensure your frameworks support TF32, BF16 and MIG. Without proper library support, you won’t realize A100’s full benefits.
   
   
5. Plan for Supply and Future‑Proofing: Consider lead times and export restrictions. If you need GPUs immediately, V100 may be more readily available. Evaluate H100 or H200 only if your budget allows.
   
   
6. Use Orchestration Tools: Leverage Clarifai’s compute orchestration to pack multiple models, autoscale and monitor costs, ensuring high utilization and reliability.
   
   

Expert Insights:

• Clarifai’s step‑by‑step decision framework emphasises that workload characteristics should drive GPU choice, not hype.
  
  
• Analysts from independent articles suggest that A100 is the best compromise between performance and price for most AI workloads, while V100 remains ideal for mid‑scale research.
  
  

Future‑Proofing: Beyond A100 and V100

Hopper (H100) and H200: The Next Big Steps

The H100, launched in 2022, introduced FP8 precision and a Transformer Engine that doubles performance on attention mechanisms. It delivers 2–4× speed‑ups over the A100, albeit with a much higher price tag. In 2024, H200 added 141 GB HBM3e memory and 4.8 TB/s bandwidth, offering ~45 % more tokens per second for inference.

Expert Insights:

• Clarifai’s guides caution that, despite the hype, H100/H200 will co‑exist with A100 for years due to supply constraints; high costs may limit adoption.
  
  

Blackwell (B100/B200) and Alternative Accelerators

NVIDIA’s Blackwell architecture (expected in 2025) promises even larger memory and compute capacity—rumors suggest B200 could reach 1.2 kW TDP. Meanwhile, AMD’s MI300 and Intel’s Gaudi 3 offer competitive price‑performance ratios and should not be overlooked.

Global Supply and Geopolitical Context

Export controls have restricted A100/H100 shipments to specific regions, prompting investments in domestic GPUs within China. India’s AI revolution aims to deploy over 80 k GPUs with advanced cooling systems. These trends underscore the importance of diversifying supply and planning ahead.

Data Center Innovations and Sustainability

Next‑generation GPUs will require innovative cooling and energy‑efficient architectures. Expect liquid cooling to become standard and chip‑integrated power systems to reduce energy losses. Clarifai continues to invest in R&D to ensure its platform remains compatible with emerging hardware while optimizing for sustainability.

Expert Insights:

• Clarifai’s H100 guide explains the trade‑offs between H100, H200 and Blackwell, noting that A100 will remain a cost‑efficient workhorse for years.
  
  
• Industry analysts predict that diversification to alternative accelerators (e.g., Gaudi 3) will increase competition and drive down prices.
  
  

How Clarifai’s Compute Orchestration Enhances A100 and V100

Unified Control Across Any Environment

Clarifai’s platform offers a unified control plane that works across public clouds (AWS, GCP, Azure), on‑prem clusters and edge devices. This means you can manage A100 and V100 GPUs from a single dashboard.

Model Packing, GPU Fractioning and Autoscaling

To maximize GPU utilization, Clarifai implements model packing—the practice of combining multiple models into one container—and GPU fractioning, which assigns fractional GPU resources to different tasks. When combined with MIG, these features allow you to run many models simultaneously on an A100, achieving 99.99 % uptime and 3.7× reduction in idle compute.

Cost Transparency and Monitoring

Clarifai offers budgets, alerts and policy controls, so you can set spending limits, receive notifications when approaching thresholds and adjust resource allocation in real time. This transparency helps teams avoid surprise bills and make data‑driven decisions.

Security and Compliance

Enterprises can deploy Clarifai within virtual private clouds (VPC) or air‑gapped environments, ensuring compliance with industry regulations. The platform provides role‑based access control (RBAC), encryption and audit logs, making it suitable for sensitive workloads.

Developer‑Friendly Tools

Clarifai supports a rich set of interfaces: web GUI, command‑line tools, Python and Java SDKs, containerization for custom models, streaming APIs and gRPC endpoints for low‑latency inference. Developers can integrate existing workflows seamlessly.

Success Stories and Real‑World Impact

Clarifai’s platform has enabled customers to process up to 1.6 million inputs per second by packing and batching models efficiently. This helps startups launch applications quickly without hiring a dedicated DevOps team. Combined with Clarifai’s AI model zoo and workflow builder, users can build end‑to‑end pipelines using V100 or A100 hardware.

Expert Insights:

• Clarifai’s compute orchestration is designed by engineers who previously built large GPU clusters; their expertise ensures high reliability and cost efficiency.
  
  
• The platform’s unified cross‑environment control allows enterprises to avoid vendor lock‑in and migrate workloads as needed.
  
  

Frequently Asked Questions (FAQs)

Is the V100 still viable in 2025?
Yes—for education, small research projects and cost‑sensitive applications, the V100 remains useful. However, its 16–32 GB memory and lack of FP8/TF32 support limit future‑proofing.

What’s the difference between CUDA cores and Tensor Cores?
CUDA cores handle general‑purpose parallel computation, suitable for HPC and graphics. Tensor Cores accelerate matrix multiplications and operate at lower precision (FP16/TF32/FP8), delivering higher throughput for deep‑learning.

Should I buy or rent GPUs?
It depends on workload duration and capital. Buying hardware yields the lowest per‑hour cost if utilization is high; renting offers flexibility but can be expensive during peak demand. Clarifai’s orchestration allows hybrid strategies and cost monitoring.

How does MIG differ from multi‑process service (MPS)?
MIG partitions A100 into isolated instances with dedicated memory and compute; MPS lets multiple processes share a GPU without isolation. MIG ensures deterministic performance and better utilization.

Are alternative accelerators like Gaudi 3 or AMD MI300 worth considering?
Yes—both Intel’s Gaudi 3 and AMD’s MI300 offer competitive price‑performance and are gaining support in AI frameworks. They could be attractive if you’re evaluating a diverse hardware portfolio.

What research papers should I read for deeper technical detail?
We recommend NVIDIA’s Volta and Ampere whitepapers, the KTH/Chalmers benchmark study on A100 performance, the sparse/batched computation paper comparing V100 and A100, and Clarifai’s detailed guides on A100 and H100. These sources inform the benchmarks and insights in this article.

Conclusion: Making an Informed Choice

Choosing between the A100 and V100 is not just about selecting the faster GPU; it’s about aligning hardware capabilities with your workload requirements, budget, energy constraints and future‑proofing plans. The V100 remains a reliable and affordable option for moderate workloads, while the A100 delivers exceptional throughput, memory capacity and scalability for modern AI.

Incorporating Clarifai’s compute orchestration amplifies the value of both GPUs by offering model packing, GPU fractioning, autoscaling, cost transparency and unified control, enabling teams to deploy AI at scale without deep infrastructure expertise. As the AI hardware landscape evolves toward H100, H200, Blackwell and alternative accelerators, Clarifai’s platform provides the flexibility to adapt and optimize.

Ultimately, the right choice is contextual: assess your workload, consider your budget, evaluate memory and power needs, and leverage the tools available to you. By doing so, you’ll ensure that your AI projects are not only performant but also sustainable, cost‑effective and ready for the future.