Which AI Inference Chips Are Recommended for High-Performance AI?

GMI Cloud Blog | AI Infrastructure Guide | gmicloud.ai

For high-performance AI inference, the chip you choose determines your latency ceiling, throughput capacity, and cost-per-request floor. The current NVIDIA data center lineup offers a clear hierarchy: H200 and H100 lead for production inference, A100 covers budget deployments, and L4 handles lightweight workloads.

This guide compares them across the specs that matter, with decision frameworks for technical leads, procurement teams, and industry researchers.

Infrastructure providers like GMI Cloud deliver these GPUs on-demand with 100+ optimized models ready to run on them.

We focus on NVIDIA data center GPUs; AMD MI300X, Google TPUs, and AWS Trainium are outside scope.

What Makes a Chip Good for Inference

Inference and training place very different demands on hardware. Picking a chip optimized for training when you need inference performance is a common and expensive mistake.

VRAM (memory capacity). Determines the largest model you can load on a single GPU. A 70B model at FP8 needs ~70 GB. If it doesn't fit, you split across GPUs (adding latency) or quantize further (risking quality).

Memory bandwidth. Determines token generation speed for LLMs. Each token requires reading the full weight matrix. Faster bandwidth = faster tokens. This is the single most important spec for LLM inference.

FP8 compute (TFLOPS). Determines speed for compute-bound workloads like diffusion models. Each denoising step involves heavy matrix math. Higher TFLOPS = faster image/video generation.

Secondary factors include TDP for total cost of ownership, MIG support for multi-tenant serving, and NVLink bandwidth for multi-GPU inference on oversized models.

The NVIDIA Inference Lineup: Head-to-Head

Architecture

• H100 SXM: Hopper
• H200 SXM: Hopper
• A100 80GB: Ampere
• L4: Ada Lovelace
• B200 (est.): Blackwell

VRAM

• H100 SXM: 80 GB HBM3
• H200 SXM: 141 GB HBM3e
• A100 80GB: 80 GB HBM2e
• L4: 24 GB GDDR6
• B200 (est.): 192 GB HBM3e

Bandwidth

• H100 SXM: 3.35 TB/s
• H200 SXM: 4.8 TB/s
• A100 80GB: 2.0 TB/s
• L4: 300 GB/s
• B200 (est.): 8.0 TB/s

FP8

• H100 SXM: 1,979 TFLOPS
• H200 SXM: 1,979 TFLOPS
• A100 80GB: N/A
• L4: 242 TOPS
• B200 (est.): ~4,500 TFLOPS (est.)

INT8

• H100 SXM: 3,958 TOPS
• H200 SXM: 3,958 TOPS
• A100 80GB: 624 TOPS
• L4: 485 TOPS
• B200 (est.): N/A

NVLink

• H100 SXM: 900 GB/s*
• H200 SXM: 900 GB/s*
• A100 80GB: 600 GB/s
• L4: None (PCIe)
• B200 (est.): 1,800 GB/s

TDP

• H100 SXM: 700W
• H200 SXM: 700W
• A100 80GB: 400W
• L4: 72W
• B200 (est.): 1,000W

MIG

• H100 SXM: Up to 7
• H200 SXM: Up to 7
• A100 80GB: Up to 7
• L4: No
• B200 (est.): TBD

NVLink 4.0: 900 GB/s bidirectional aggregate per GPU on HGX/DGX platforms. B200 specs are estimates based on GTC 2024 disclosures; will update when MLPerf/independent benchmarks land. Sources: NVIDIA H100 Datasheet (2023), H200 Product Brief (2024), A100 Datasheet, L4 Datasheet.

H100 SXM: The Production Standard

The most widely deployed data center GPU for AI inference. In MLPerf Inference v3.1, H100-based systems were the most widely submitted platform for LLM and image generation tasks (source: mlcommons.org/benchmarks/inference-datacenter).

80 GB HBM3 at 3.35 TB/s handles 7B-70B models at FP8. Native FP8 via the Transformer Engine. MIG support (up to 7 instances) enables multi-tenant serving on a single GPU. The safe, battle-tested choice.

H200 SXM: The Bandwidth Upgrade

Same Hopper compute as H100, but 141 GB HBM3e at 4.8 TB/s: 76% more VRAM, 43% more bandwidth. Per NVIDIA's H200 Product Brief (2024), up to 1.9x inference speedup on Llama 2 70B vs. H100 (TensorRT-LLM, FP8, batch 64, 128/2048 tokens). Independent cloud tests confirm 1.4-1.6x in typical workloads.

The extra VRAM means 70B+ models fit comfortably with room for KV-cache at high concurrency. The upgrade that pays for itself on large-model workloads.

A100 80GB: The Budget Option

Ampere-generation, no native FP8. Handles 7B-34B at FP16/INT8. At 2.0 TB/s bandwidth, decode-heavy LLM workloads run noticeably slower than Hopper. Solid when cost is the primary constraint and models fit within Ampere's range.

L4: Lightweight Inference

24 GB GDDR6, 72W TDP, PCIe only. Runs 7B models at INT8/INT4. No NVLink, no MIG. Best for edge deployments, small models, or development environments.

B200: Next Generation (Estimates)

192 GB HBM3e, 8.0 TB/s, ~4,500 FP8 TFLOPS (est.). All specs are GTC 2024 estimates; treat as directional until independent benchmarks land. If numbers hold, B200 roughly doubles H200's bandwidth and VRAM.

Decision Framework: Matching Chips to Workloads

Workload (Primary Bottleneck / Recommended / Why)

• LLM inference (7B-70B) - Primary Bottleneck: Bandwidth - Recommended: H100 SXM - Why: Production standard, native FP8, MIG
• LLM inference (70B+, long context) - Primary Bottleneck: Bandwidth + VRAM - Recommended: H200 SXM - Why: 141 GB fits large models + KV-cache
• Image/video generation - Primary Bottleneck: Compute - Recommended: H100 or H200 - Why: Both deliver 1,979 FP8 TFLOPS
• Multi-model serving - Primary Bottleneck: Isolation - Recommended: H100/H200 + MIG - Why: Up to 7 isolated instances per GPU
• Budget inference (7B-34B) - Primary Bottleneck: Cost - Recommended: A100 80GB - Why: Lower $/hr, adequate for smaller models
• Lightweight / edge - Primary Bottleneck: Power + cost - Recommended: L4 - Why: 72W, PCIe, entry-level
• Future-proofing (100B+) - Primary Bottleneck: All three - Recommended: B200 (when available) - Why: 2x bandwidth and VRAM vs. H200

Recommendations by Role

For Technical Leads

Start with the workload framework above. For LLM workloads, H200's bandwidth advantage translates directly to faster tokens and higher concurrency. For diffusion workloads, H100 and H200 deliver identical FLOPS, so H100 offers better cost-efficiency unless you need the extra VRAM.

Validate with real benchmarks before committing. Run your model on both H100 and H200 and compare tokens-per-second at your target batch size.

For Procurement Teams

Factor in utilization, not just $/GPU-hour. A cheaper GPU at 40% utilization costs more per inference than a pricier GPU at 80%. MIG improves utilization by serving multiple models on one card.

Cloud anchors: H100 ~$2.10/GPU-hour, H200 ~$2.50/GPU-hour (check gmicloud.ai/pricing for current rates). Compare against on-premises costs including power, cooling, and ops. For variable workloads, on-demand avoids idle hardware costs.

Also consider API inference as a procurement alternative. Per-request pricing ($0.03-$0.10 for video, $0.005-$0.10 for TTS) eliminates hardware management entirely for many use cases.

For Industry Researchers

Key landscape signals: H100/H200 dominate current deployments. B200 is the next inflection but awaits validation. Consumer GPUs (RTX 4090/5090) carry compliance risk; NVIDIA's GeForce EULA restricts data center use (see nvidia.com/en-us/drivers/geforce-license).

Watch for: B200 MLPerf results (expected 2025), AMD MI300X positioning, and cloud pricing dynamics as H200 supply stabilizes.

Software Stack: Capturing Hardware Performance

The chip sets the ceiling; the software stack determines how close you get. TensorRT-LLM provides NVIDIA-specific kernel optimizations for peak throughput. vLLM offers flexible memory management via PagedAttention. Both support FP8 and continuous batching.

Quantization (FP16→FP8) halves VRAM with minimal quality loss on Hopper GPUs. Continuous batching can improve throughput 2-3x vs. static batching. These optimizations apply regardless of which chip you choose.

Getting Started

Two paths. If you're evaluating chips, provision GPU instances and benchmark your model on H100 vs. H200. If you're ready to deploy, use the workload framework to select, then optimize with FP8 and continuous batching.

Cloud platforms like GMI Cloud offer on-demand GPU instances for benchmarking and a model library for API-based inference.

Start with whichever path matches your decision stage.

FAQ

Is H200 worth the premium over H100?

For LLM inference on 70B+ models, yes. The 43% bandwidth increase measurably speeds token generation. For diffusion workloads, FLOPS are identical, so H100 is more cost-efficient unless you need the extra VRAM.

Should we buy GPUs or use cloud?

Steady workloads favor on-premises for lower long-term cost. Variable workloads favor cloud to avoid idle hardware. Many teams validate on cloud first, then move on-premises as utilization stabilizes.

When will B200 be production-ready?

Specs are GTC 2024 estimates. Independent benchmarks are expected in 2025. H200 is the best current option for teams that can't wait.

Are consumer GPUs viable for inference?

For development, yes. For production, no. NVIDIA's GeForce EULA restricts data center use. Using consumer GPUs in production creates compliance risk.

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