Top GPUs Optimized for LLM Inference Workloads

The H100 SXM and H200 SXM are the top two GPUs for LLM inference in 2024-2025, and that ranking is not close. If you're running production language model workloads and want the best throughput per dollar at scale, these are your options.

The A100 80GB earns its place as the proven budget choice for teams that need 80 GB VRAM without paying H-series prices. GMI Cloud offers on-demand and reserved access to both H100 and H200 SXM nodes, pre-configured for LLM inference with TensorRT-LLM, vLLM, and Triton.

Why LLM Inference Is a Different Problem

You can't evaluate GPUs for LLM inference the same way you'd evaluate them for training or rendering. LLM inference has a specific bottleneck structure that makes some specs matter a lot and others almost irrelevant.

During the decode phase (generating each new token), the GPU must load the entire model's weight matrix from VRAM on every single forward pass. For a 70B parameter model in FP16, that's roughly 140 GB read from memory per token generated.

That's why LLM decode is memory-bandwidth-bound, not compute-bound.

KV-cache pressure adds another layer. Each active request caches its key-value attention states, consuming VRAM proportional to context length, batch size, number of layers, and precision. At long contexts and high concurrency, KV-cache competes directly with model weights for VRAM headroom.

Choosing the right GPU means balancing all three: memory bandwidth, VRAM capacity, and compute for prefill.

Full GPU Spec Comparison

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

#1: H100 SXM

The H100 SXM is the current standard for production LLM inference. Its 3.35 TB/s memory bandwidth and 80 GB HBM3 support models up to 70B in FP16 on a single GPU, and its native FP8 support (1,979 TFLOPS) makes it the go-to for quantized inference at scale.

NVLink 4.0 delivers 900 GB/s bidirectional aggregate per GPU on HGX/DGX platforms, making 8-GPU tensor-parallel configurations highly efficient for models that don't fit in single-GPU VRAM.

The H100 also supports MIG (Multi-Instance GPU) with up to 7 instances, useful for multiplexing smaller model inference across many isolated tenants.

The H100's main constraint is its 80 GB VRAM ceiling. Models larger than 70B in FP16 require multi-GPU tensor parallelism, and at very long contexts (32K+) with large batches, KV-cache can push memory limits. That's where the H200 takes over as the better choice.

#2: H200 SXM

The H200 SXM improves on the H100 in two meaningful ways: 141 GB HBM3e (nearly 2x the VRAM) and 4.8 TB/s memory bandwidth (43% more throughput). The compute specs are identical, FP8 is 1,979 TFLOPS, FP16 is 989 TFLOPS. Everything extra is in the memory subsystem.

NVIDIA's own benchmarks show the H200 delivering up to 1.9x inference speedup over the H100 on Llama 2 70B (NVIDIA H200 Tensor Core GPU Product Brief, 2024, TensorRT-LLM, FP8, batch 64, 128/2048 tokens). That speedup comes entirely from the higher bandwidth allowing faster weight reads during decode.

You'll see the H200 truly shine in long-context workloads (32K+ tokens) and high-concurrency scenarios where KV-cache would exhaust an H100's 80 GB. It also lets you run larger models (up to 105B+ in FP16) on a single GPU without tensor parallelism, reducing inter-GPU communication overhead.

#3: A100 80GB

The A100 80GB is the proven workhorse for teams that need large VRAM capacity without paying H100/H200 prices. It ships with 80 GB HBM2e and 2.0 TB/s memory bandwidth. That's slower than the H100, but enough to handle 70B models in FP8 and smaller models in FP16.

One important caveat: the A100 has no native FP8 support. Its quantized inference uses INT8 (624 TOPS), which is significantly lower than the H100/H200's FP8 capability. For workloads that rely heavily on FP8 precision, you'll give up throughput compared to newer hardware.

The A100's NVLink 3.0 runs at 600 GB/s, lower than the H100/H200's 900 GB/s. For tensor-parallel jobs that are inter-GPU communication-heavy, that gap matters. Still, for moderate batch sizes with well-optimized FP8 or INT8 quantization, the A100 offers solid price-to-performance for budget-conscious teams.

#4: L4

The L4 is a PCIe inference card designed for efficiency, not raw throughput. At 72W TDP and 24 GB GDDR6, it's the lowest-power GPU on this list. Its 300 GB/s memory bandwidth and 242 FP8 TOPS make it appropriate only for small models (7B and below) at modest concurrency levels.

The L4 has no NVLink, so multi-GPU tensor parallelism is limited to slower PCIe bandwidth. That makes it impractical for any model that doesn't fit in 24 GB. Plus, the low bandwidth means decode throughput drops quickly as batch size grows.

Where the L4 genuinely earns its place: edge deployments, cost-sensitive API endpoints serving small quantized models, and environments where power consumption is a hard constraint. Don't use it for anything above 13B parameters.

#5: B200 (est.)

The B200 is NVIDIA's next-generation flagship, and if the announced specs hold, it will redefine inference efficiency.

Estimated at 192 GB HBM3e (est.) VRAM, 8.0 TB/s memory bandwidth (est.), and ~4,500 FP8 TFLOPS (est.), it would handle models the H200 can't fit on a single GPU and deliver roughly 1.7x H200's bandwidth.

All B200 specifications are estimates based on GTC 2024 disclosures. Confirmed production specs and third-party benchmarks are not yet widely available. NVLink 5.0 at 1,800 GB/s (est.) would double H200's inter-GPU bandwidth, opening up new possibilities for multi-GPU inference configurations.

For production planning in 2025, stick with H100 and H200 SXM. B200 belongs in your roadmap, not your current deployment.

Decision Table: Match Your Workload to the Right GPU

Model Size	Context Length	Recommended GPU	Reason
Up to 13B (FP16)	Up to 32K	H100 SXM	Fits in 80 GB, high bandwidth, FP8 support
13B-70B (FP16)	Up to 16K	H100 SXM	Model fits, KV-cache manageable
70B+ (FP16) or any at 32K+	Long context	H200 SXM	141 GB VRAM, 4.8 TB/s bandwidth
Up to 70B (FP8/INT8)	Moderate	A100 80GB	Budget option, lower bandwidth
Up to 7B (quantized)	Short	L4	Ultra-low power, cost-efficient
100B+ models	Any	B200 (est.)	Future planning only, specs unconfirmed

Infrastructure for Production LLM Inference

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 jobs.

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

Pricing is approximately $2.00/GPU-hour for H100 SXM and $2.60/GPU-hour for H200 SXM. Always check gmicloud.ai/pricing for current rates, since on-demand and reserved pricing differ.

Frequently Asked Questions

Q: Is the H200 always better than the H100 for LLM inference? A: Not always. For models under 70B at moderate batch sizes and context lengths, the H100 often delivers better $/token.

The H200 wins decisively when your model is large, your context is long, or your batch size is high enough that bandwidth becomes the primary bottleneck.

Q: Can I run Llama 2 70B on a single H100? A: In FP8 quantized form, yes. In FP16, you'll need approximately 140 GB of VRAM, which means a multi-GPU H100 setup or a single H200 (141 GB). Check your KV-cache budget at your target batch size and context length before committing.

Q: Does MIG help with LLM inference costs? A: MIG (Multi-Instance GPU) lets you partition one H100 or H200 into up to 7 isolated GPU instances. It's useful for running multiple smaller models on a single physical GPU, or for multi-tenant environments.

It won't improve throughput for a single large model.

Q: What software stack should I use for LLM inference on H100/H200? A: TensorRT-LLM from NVIDIA is the highest-performance option and supports FP8 natively. vLLM is excellent for production deployment with dynamic batching and PagedAttention. Triton Inference Server works well as a serving layer.

Most production teams use a combination of these.

Q: How much does inter-node InfiniBand bandwidth matter for LLM inference? A: For single-node (8 GPU) tensor-parallel inference, NVLink handles all inter-GPU communication and InfiniBand is irrelevant.

InfiniBand matters when your job spans multiple nodes, which happens with very large models (200B+) that require more than 8 GPUs.