Which GPUs Offer the Best Price-to-Performance Ratio for AI Inference in Cloud Environments?

The short answer: the H100 SXM wins best price-to-performance for most inference workloads, and the H200 SXM takes over when your model exceeds 80 GB VRAM or decode throughput is the bottleneck.

If you're running production LLM inference and still agonizing over GPU selection, you're probably measuring the wrong thing — raw TFLOPS don't tell you what a token actually costs.

GMI Cloud gives you on-demand access to both H100 and H200 SXM nodes, so you can benchmark real $/token before committing to reserved capacity.

Why Raw TFLOPS Don't Define Inference Efficiency

Here's the thing: inference pricing is about $/output-token, not peak compute. A GPU that delivers 2,000 TFLOPS but bottlenecks on memory bandwidth will cost more per token than a lower-TFLOPS card that feeds the model weights at wire speed.

LLM inference has two distinct phases. The prefill phase (processing your prompt) is compute-bound. The decode phase (generating each new token) is memory-bandwidth-bound, because the model must read all its weights from VRAM on every forward pass.

So the right metric is: how many tokens per second can this GPU deliver at a given batch size, and what does that cost per hour? That's how you compare GPUs honestly.

Full GPU Spec Comparison

GPU	VRAM	Memory BW	FP16 TFLOPS	FP8 TFLOPS	TDP	NVLink
H200 SXM	141 GB HBM3e	4.8 TB/s	989	1,979	700W	900 GB/s bidir. agg./GPU (HGX/DGX)
H100 SXM	80 GB HBM3	3.35 TB/s	989	1,979	700W	900 GB/s bidir. agg./GPU (HGX/DGX)
A100 80GB	80 GB HBM2e	2.0 TB/s	312	N/A	400W	600 GB/s
L4	24 GB GDDR6	300 GB/s	121	242	72W	None (PCIe)
B200 (est.)	192 GB HBM3e (est.)	8.0 TB/s (est.)	N/A	~4,500 (est.)	~1,000W (est.)	1,800 GB/s (est.)

Sources: NVIDIA H100 Tensor Core GPU Datasheet (2023); NVIDIA H200 Tensor Core GPU Product Brief (2024); NVIDIA A100 Tensor Core GPU Datasheet; NVIDIA L4 Tensor Core GPU Datasheet. B200 specs are estimates based on GTC 2024 disclosures.

You'll notice the H100 and H200 share identical FP8 and FP16 TFLOPS. The difference is entirely in memory subsystem. That distinction drives everything you'll see in the $/token analysis below.

$/TFLOPS and Estimated $/Token by Workload

Compute cost per TFLOPS tells you how efficiently you're buying raw math. But for inference, the more useful number is estimated $/token at a realistic batch size.

The table below uses approximate cloud rates. Always check gmicloud.ai/pricing for current rates, since spot and reserved pricing changes.

GPU	Approx. $/GPU-hr	FP8 TFLOPS	$/TFLOPS-hr	Est. tokens/sec (Llama 2 70B, batch 32)	Est. $/1M tokens
H100 SXM	~$2.00	1,979	~$0.00101	~2,800	~$0.20
H200 SXM	~$2.60	1,979	~$0.00131	~4,200	~$0.17
A100 80GB	~$1.60	N/A	N/A	~1,500	~$0.30
L4	~$0.55	242	~$0.00227	~400	~$0.38

Throughput estimates are approximations for decode phase at batch 32, FP8/FP16 precision. Benchmark reference: NVIDIA TensorRT-LLM internal benchmarks; H200 inference speedup per NVIDIA H200 Product Brief (2024), TensorRT-LLM, FP8, batch 64.

The H200 costs more per hour but delivers more tokens per second, pushing its effective $/token below the H100 for decode-heavy workloads. If your traffic is batch-heavy and latency-tolerant, that spread widens further.

KV-Cache Budgeting: Will Your Model Fit?

Before you can optimize $/token, you need to know whether the model fits in VRAM at your target context length. KV-cache is the silent VRAM killer.

The formula is:

KV cache per request ≈ 2 × num_layers × num_kv_heads × head_dim × seq_len × bytes_per_element

For Llama 2 70B (80 layers, 8 KV heads, 128 head_dim), at 4K context, FP16:

2 × 80 × 8 × 128 × 4,096 × 2 bytes ≈ 0.4 GB per request

At batch 32, that's ~12.8 GB just for KV-cache, on top of ~140 GB for model weights in FP16. That exceeds an H100's 80 GB on its own. You'll need 8× H100 with tensor parallelism, or a single H200 node for KV headroom at large contexts.

H200's 141 GB HBM3e and 4.8 TB/s bandwidth mean you can serve longer contexts and larger batches before spilling to multi-GPU. That's where its $/token advantage becomes decisive.

Decision Tree: Which GPU for Your Workload?

You don't need a spreadsheet. Use this decision path:

Step 1: Does your model fit on a single 80 GB GPU at your target batch size and context length? - Yes: H100 SXM is likely your best $/token option. - No (model exceeds 80 GB, or KV-cache pushes you over): Move to H200 SXM.

Step 2: Is your workload decode-bound (long generations, high concurrency) or prefill-bound (short outputs, batch processing)? - Decode-bound: H200 SXM wins on $/token (bandwidth advantage compounds at scale).

- Prefill-bound or batch-processing: H100 SXM competes well (compute, not bandwidth, is the limit).

Step 3: Is cost per hour your primary constraint and latency requirements are loose? - Yes: A100 80GB covers models up to 70B in FP16 with proven reliability and lower hourly rates. - No: Don't sacrifice throughput for a lower sticker price.

Step 4: Are you serving small models (7B-13B) with very high request volume and tight per-unit cost targets? - L4 is worth evaluating for quantized small models, but NVLink is absent, so multi-GPU scaling is slow.

Model quality and throughput come first. Don't pick a cheaper GPU and then compensate with aggressive quantization that degrades output quality.

Why Memory Bandwidth Is the Real Price-to-Performance Lever

You might be wondering why the A100, with its lower price point, doesn't win more categories. It's simple: the A100's 2.0 TB/s memory bandwidth is the bottleneck for large models at scale.

When you're decoding tokens with a 70B model, the GPU needs to sweep through all ~140 GB of model weights per forward pass. At 2.0 TB/s, that sweep takes about 70ms. At 4.8 TB/s (H200), it takes about 29ms. That difference, multiplied by millions of tokens per day, is your actual cost difference.

This is why you'll see published benchmarks show H200 delivering up to 1.9x inference speedup over H100 on Llama 2 70B (NVIDIA official benchmark, TensorRT-LLM, FP8, batch 64, 128/2048 tokens). The extra bandwidth is doing real work.

GMI Cloud: Infrastructure Matched to These Workloads

GMI Cloud runs H100 SXM and H200 SXM GPU instances on 8-GPU nodes with NVLink 4.0 (900 GB/s bidirectional aggregate per GPU, HGX/DGX platforms) and 3.2 Tbps InfiniBand for inter-node communication.

Nodes come pre-configured with CUDA 12.x, cuDNN, NCCL, TensorRT-LLM, vLLM, and Triton Inference Server.

Current pricing runs approximately $2.00/GPU-hour for H100 SXM and $2.60/GPU-hour for H200 SXM. Check gmicloud.ai/pricing for current rates, as on-demand and reserved pricing differ.

Frequently Asked Questions

Q: Is $/TFLOPS a reliable way to compare GPUs for inference? A: Not on its own. FP8 TFLOPS matter most for prefill. For decode, memory bandwidth per dollar is more predictive of actual $/token. Use both metrics together.

Q: Should I always prefer H200 over H100 for inference? A: Only when your workload is decode-bound, model size pushes against 80 GB, or you're targeting long contexts at high concurrency. For smaller models in FP8 at moderate batch sizes, H100 SXM often wins on pure $/token.

Q: Does the L4 make sense for production LLM inference? A: For small quantized models (7B and below) with moderate traffic, yes. It's power-efficient and cheap per hour. But it has no NVLink, only 24 GB VRAM, and tops out at 300 GB/s bandwidth. Don't use it for anything above 13B.

Q: How do I account for KV-cache when planning GPU allocation? A: Use the formula above. Calculate model weight VRAM + peak KV-cache at your max batch size and context length. Add 10-15% overhead for activations and framework buffers. Then choose the GPU that fits that budget.

Q: Where can I check current GPU pricing for GMI Cloud? A: Check gmicloud.ai/pricing for current rates, both on-demand and reserved.

Q: What about B200 for inference? A: B200 specifications are estimates based on GTC 2024 disclosures. If the projected 8.0 TB/s memory bandwidth and ~4,500 FP8 TFLOPS (est.) are confirmed, it will significantly shift the $/token picture for large-model inference.

But availability is limited and pricing is unclear. Stick with H100/H200 for production planning today.