Which GPU Hardware Offers the Best Performance for AI Inference Workloads

Editor’s note: This version has been tightened for factual safety. Any throughput, latency, cold-start, or cost examples below should be read as decision-making illustrations unless they are explicitly attributed to an official source.

Verify current prices and benchmark your own workload before treating a number as production truth.

The GPU you pick for inference determines more than throughput. It sets your cost per prediction, your maximum concurrent workload capacity, your latency floor, and your scaling efficiency. Pick the wrong one and you're either burning money on idle compute or constantly rejecting traffic because you're out of capacity.

The problem: GPU specs are intentionally confusing. Tensor FLOPS, memory bandwidth, sparsity support, NVLink, unified memory. These words mean something, but they don't directly translate to "how many requests per second can I serve on a budget?"

This article is a translation layer. We'll map GPU specs to actual inference workloads, show you the math that matters, and help you understand when a $10k H100 is the right call and when a $3k L40 does the job for less money.

Then we'll talk about the one thing no GPU benchmark ever admits: total system cost, not just GPU cost.

Key Takeaways

• GPU memory determines your maximum concurrent batch size. Larger models and higher concurrency require more memory.
• Tensor throughput (measured in teraflops) matters for inference less than memory bandwidth and latency
• A smaller, cheaper GPU with lower latency often beats a large GPU because it scales faster
• Quantization changes the GPU calculus: a quantized 7B model might run faster on a $3k L40 than an unquantized version on a $10k H100
• GMI Cloud offers NVIDIA H100, H200, B200, and GB200 NVL72 across multiple regions, with per-GPU-hour pricing so you pay only for what you use

Understanding GPU Specs for Inference

Let's translate GPU datasheets into inference realities.

An NVIDIA H100 has: - 80GB of NVIDIA HBM2e memory - 3.46 PetaFLOPS of tensor throughput (in fp8) - 3.35 TB/s memory bandwidth - 141 watts TGP (thermal/power envelope) - NVLink for GPU-to-GPU communication

Most of those specs are marketing. Here's what actually matters for inference:

Memory capacity: This is your hard ceiling. A 7B parameter LLM in fp16 is 14GB. In kv-cache during inference, add another 10-20GB depending on sequence length. That's 24-34GB total. An H100 with 80GB easily fits this. An L40 with 48GB is tight but works if you quantize or use smaller sequence lengths.

Memory bandwidth: This is your actual performance bottleneck for inference. Inference is memory-bound, not compute-bound. You're reading weights from memory, applying a small computation, and writing results. The GPU spends most of its time waiting for data, not computing.

An H100's 3.35 TB/s is good, but the difference between 3.35 TB/s and an L40's 960 GB/s matters less than you'd think because the L40 still moves data fast enough for reasonable batch sizes.

Latency characteristics: Some GPUs have better latency behavior under load. The H100 is designed to maintain consistent latency even at high utilization. Budget GPUs sometimes have more latency variability. This matters for p99 latency targets.

Power efficiency: An H100 uses 700 watts. An L40 uses 320 watts. If you're paying for power, this is a real cost. Over a year, at $0.10/kWh, an H100 costs 612 per GPU in power alone. An L40 costs 280. That's a 2x power difference, which translates directly to cost.

The Workload-to-GPU Map

Here's how to match your inference workload to hardware:

Small models, high concurrency (DistilBERT, small LLMs under 3B): - L40 is the right choice. It's 48GB memory, which fits most small models with plenty of room for batching. Cost-effective and adequate throughput. - Avoid H100. You're paying for memory and compute you won't use.

- Cost: roughly 3-5 per inference hour on L40 vs 8-12 on H100 for equivalent throughput.

Medium models, moderate concurrency (7B-13B LLMs, image models): - H100 or H200. H100 has 80GB, H200 has 141GB. Both work, but H200 is better if you have long context windows or want to batch larger payloads. The extra memory lets you serve more concurrent requests.

- L40 works with quantization, which can cut model size 50-75%. If your workflow supports int8 or int4 quantization, L40 is cheaper. If you need full precision, H100 is the baseline. - Cost: 8-12 per inference hour on H100 vs 5-8 on quantized L40.

Large models, diverse workloads (70B+ LLMs, multi-model orchestration): - H200 or B200. H200 is 141GB, which comfortably fits a 70B model plus kv-cache. B200 is newer and has better memory bandwidth. Both are good choices. - If you're running multi-GPU inference (model parallelism), NVLink matters.

H100 and H200 have NVLink, which means GPU-to-GPU communication is fast. This matters when your model is split across multiple GPUs. - Cost: 12-18 per inference hour.

Extreme scale, lowest latency (10k+ req/s, p99 < 50ms): - GB200 NVL72 clusters. This is a purpose-built inference system with 72 H100-equivalent GPUs in a single chassis, connected by NVLink. You're not buying a single GPU; you're buying a distributed system.

Cost per inference hour is higher in absolute terms, but per-request cost can be lower due to efficiency and automatic load balancing. - Not typical for most teams, but critical for teams serving millions of predictions daily.

The Memory Math

Let's be concrete. Say you're serving a 13B parameter LLM:

• Model weights in fp16: 26GB
• KV-cache for batch_size=4, seq_len=2048: 8GB (rough estimate)
• Total: 34GB

With an L40 (48GB), you fit comfortably. You can serve 4 concurrent requests with minimal latency.

With an H100 (80GB), you fit easily and can batch larger (8-16 requests) without hitting memory limits.

Now quantize the same model to int8: - Model weights in int8: 13GB - KV-cache: still 8GB (because cache is stored as fp16 by default) - Total: 21GB

Now an L40 is more comfortable and cheaper per inference.

The point: quantization changes the hardware requirement. Don't pick hardware based on the unquantized model; pick it based on the model you'll actually deploy.

Throughput vs. Latency Trade-off

An H100 can deliver higher absolute throughput than an L40, but the difference depends on batch size and memory bandwidth utilization.

For a 13B LLM with moderate batch sizes (4-8): - H100: roughly 200-300 tokens/second throughput - L40: roughly 100-150 tokens/second throughput

The H100 is 2x faster. But you're paying 2.5x as much per GPU-hour.

For cost per token, the L40 sometimes wins because it's cheaper per hour and still delivers acceptable throughput.

For latency, the H100 maintains lower p99 latency under load because of its memory bandwidth advantage. If you have strict latency constraints (< 50ms response time for requests), H100 might be necessary.

The calculus changes if you add more L40s. Two L40s together cost less than one H100 and can deliver similar total throughput with easier load balancing. But you're now managing two GPUs instead of one, which adds operational complexity.

The Quantization Multiplier

This is the hidden lever that most GPU comparisons miss.

A quantized 7B LLM (int4) can run 2-3x faster than the same model in fp16. This means:

• An L40 serving quantized models might outperform an H100 serving unquantized models
• A B200 serving quantized models is overkill; you're paying for compute you don't need
• The GPU choice should account for quantization strategy from the start

If you have full control over your model and deployment:

1. Quantize first (measure accuracy loss)
2. Benchmark on a small GPU (L40 or A6000)
3. Scale up only if that GPU can't handle your traffic
4. Then pick the smallest GPU that covers your traffic with headroom

Most teams reverse this process, pick an expensive GPU first, then wonder why they're overpaying.

The Total Cost of Ownership

GPU price is not the same as inference cost. Total cost includes:

• GPU hourly rate (what you pay the cloud provider)
• Power consumption (embedded in cloud pricing for managed services, but real cost for on-premises)
• Memory bandwidth efficiency (affects how many requests you can serve per GPU)
• Operational overhead (how much engineering time to set up, monitor, update)

A cheaper GPU that requires custom optimization, special quantization work, and two weeks of engineering is more expensive than a standard GPU that works out of the box.

For this reason, GMI Cloud's approach matters: standardized GPUs (H100, H200, B200, GB200 NVL72) priced transparently per GPU-hour, with serverless inference handling batching and scaling automatically.

You're not paying for optimization work you haven't done; you're paying for the GPU you use, nothing more.

When to Use Which GPU

Best for cost: L40 - Small to medium models (up to 13B) - High volume, low margin inference - Workloads that support quantization - Batch inference (latency is less critical)

Best for general purpose: H100 - 13B to 70B models - Moderate to high traffic (100-1,000 req/s) - Need consistent p99 latency - Models that need to stay in full precision

Best for scale: H200 - 70B+ models - Very high traffic with diverse request patterns - Multi-GPU model parallelism - Longest context windows

Best for new workloads: B200 - Latest models optimized for B200 architecture - Highest memory bandwidth per FLOP - Better efficiency than H100 for future-proofed inference - High throughput per dollar for tensor operations

Best for extreme scale: GB200 NVL72 clusters - 10,000+ requests per second - Mission-critical inference with strict SLA - Teams that can justify the operational overhead

The Benchmark Trap

GPU benchmarks you read online typically measure: - Peak throughput at maximum batch size - Single request latency in ideal conditions - Throughput per dollar (which assumes 100% utilization)

None of these match production. Production has: - Variable batch sizes (sometimes 1, sometimes 16) - Network latency mixed in with GPU latency - Idle capacity most of the time (you need headroom) - Model updates that require downtime - Diverse workloads (not all the same inference task)

Real production utilization is often 20-40%. You need headroom for traffic spikes and update windows. Don't size your GPU based on peak throughput benchmarks; size it based on 70% utilization at peak traffic, then round up.

The Practical Selection Process

Here's what actually works:

1. Determine your model's memory footprint. Run it, measure actual memory used including kv-cache. This is your hard constraint.
2. Pick the smallest GPU that fits. If your model is 20GB, an L40 works. If it's 45GB, you need an H100. Don't overshoot.
3. Run a throughput test at 80% of expected peak traffic. Measure token/second, latency, and GPU utilization. If you're at 70-80% GPU utilization, you're good. If you're over 85%, you'll have latency problems under actual peaks.
4. Test with quantization. Measure accuracy, then measure throughput again. If accuracy holds and throughput improves, quantize in production.
5. Baseline on a standard platform. GMI Cloud standardizes on NVIDIA H100, H200, B200, and GB200 NVL72. Run your model on these, measure cost per inference, then scale.

The GPU Tiers at GMI Cloud

GMI Cloud offers a tiered hardware selection aligned with workload needs:

• H100 (80GB): Best for most production inference. Good balance of memory, throughput, and cost.
• H200 (141GB): Better for longer sequences, larger batch sizes, or models above 60B parameters.
• B200 (limited availability): Latest architecture, best for future-proofed deployments and models optimized for B200.
• GB200 NVL72 (available now): For teams that need extreme throughput and can justify cluster-scale infrastructure.

Pricing varies by region and reservation type. See https://www.gmicloud.ai/pricing for current rates. Serverless inference scales to zero, so you pay only for active requests, not idle capacity.

Next Steps

Measure your model's actual memory footprint in production (including kv-cache). Then pick the smallest GPU tier that fits with 20% headroom. Benchmark on that tier, measure cost per inference, and scale only if traffic demands it. Most teams overshoot on GPU size early and regret it later.

If you're uncertain which GPU is right, start with GMI Cloud's serverless inference on H100. This gives you baseline performance data without committing to a specific tier. As your traffic grows or you optimize your model, upgrade to larger GPUs or multiple GPUs only when your benchmark data shows it's necessary.

Frequently asked questions about GMI Cloud

What is GMI Cloud?
GMI Cloud describes itself as an AI-native inference cloud that combines serverless inference, dedicated GPU clusters, and bare metal infrastructure for production AI workloads.

What GPUs does GMI Cloud offer?
As of March 30, 2026, GMI Cloud's pricing page lists H100 from $2.00/GPU-hour, H200 from $2.60/GPU-hour, B200 from $4.00/GPU-hour, and GB200 from $8.00/GPU-hour. GB300 is listed as pre-order rather than generally available.

What is GMI Cloud's Model-as-a-Service (MaaS)?
MaaS is GMI Cloud's model access layer for LLM, image, video, and audio models. Public GMI materials describe it as a unified API layer covering major proprietary and open-source providers across multiple modalities.

How should readers interpret performance, latency, and cost figures in this article?
Treat any throughput, latency, batching, or unit-cost numbers as scenario-based examples unless the article explicitly attributes them to an official benchmark.

Final decisions should be based on current pricing and a benchmark using your own model, batch size, context length, and SLA.