Best GPU for LLM Inference in 2026: H100, MI300X, L40S, and B200 Compared

Choosing a GPU for LLM inference is a fundamentally different problem than choosing one for training. Training is compute-bound — you want maximum FP16/BF16 TFLOPS and fast interconnects for gradient synchronization. Inference is memory-bound — the model weights need to live in GPU memory, and every token generated requires reading those weights from HBM. The GPU that wins at training often does not win at inference, and vice versa.

This guide is for ML engineers and infrastructure teams deploying language models at scale in 2026. We will cover the four GPU categories you should be evaluating, the key metrics that actually predict inference performance, and a workload-based decision framework.

Why Memory Capacity Dominates Inference GPU Selection

The single most important constraint in LLM inference is VRAM capacity. Here is why: a model's weights must fit in GPU memory. If they do not fit, you either need to quantize aggressively (losing accuracy), split the model across multiple GPUs (adding latency), or use CPU offloading (dramatically slower).

At FP16 precision, the rule of thumb is 2GB of VRAM per billion parameters:

• 7B model → ~14GB
• 13B model → ~26GB
• 70B model → ~140GB
• 405B model → ~810GB

With INT8 quantization, halve those numbers. With INT4, halve again. But quantization has diminishing returns — below INT4, quality degradation becomes noticeable on reasoning tasks.

This is why the AMD MI300X (192GB HBM3e) and NVIDIA H200 (141GB HBM3e) are compelling inference GPUs despite not being the highest TFLOPS options. Memory capacity lets you run larger models at higher precision on fewer GPUs.

The Contenders: GPU-by-GPU Analysis

NVIDIA H100 SXM5 (80GB HBM3)

The H100 remains the most widely deployed inference GPU in 2026, primarily because the infrastructure ecosystem around it is unmatched. vLLM, TensorRT-LLM, and every major inference framework have been heavily optimized for H100. In practice, software optimization matters enormously — a well-tuned H100 deployment often outperforms a less-optimized H200 deployment on real-world serving benchmarks.

Best for: Models up to 70B at INT8 (fits in 80GB). Teams running vLLM or TensorRT-LLM who want to leverage years of CUDA kernel optimization. Production deployments where software stability matters more than raw performance.

Weakness: 80GB is tight for 70B models at FP16, which forces INT8 quantization. For 405B models, you need a minimum of 5× H100s with tensor parallelism, which adds latency.

AMD MI300X (192GB HBM3e)

The MI300X is the memory king of inference. 192GB of HBM3e at 5,300 GB/s bandwidth means you can run a 70B model in full FP16 on a single GPU with 52GB to spare for KV cache. This is a significant advantage for long-context inference, where the KV cache grows proportionally with sequence length.

AMD's ROCm software stack has matured considerably. vLLM now has native ROCm support, and performance on MI300X is within 10-15% of equivalent H100 deployments for most transformer workloads. The gap continues to close with each ROCm release.

Best for: Large model inference (70B-405B) where memory capacity is the constraint. Long-context inference (128K+ tokens) where KV cache size matters. Teams willing to invest in ROCm optimization for the memory density benefit.

Weakness: ROCm ecosystem is still less mature than CUDA. Some custom CUDA kernels (Flash Attention variants, specialized attention implementations) require porting effort. Fewer managed inference services support MI300X natively.

NVIDIA H200 SXM (141GB HBM3e)

The H200 hits a sweet spot that the H100 misses: CUDA ecosystem compatibility plus 1.75× the memory capacity. The H200 is architecturally similar to the H100 but with a dramatically improved memory subsystem — 141GB of HBM3e at 4,800 GB/s versus 80GB HBM3 at 3,350 GB/s. This translates directly to higher tokens-per-second for memory-bound inference workloads.

In our testing with vLLM serving a Llama 3 70B model at FP16, the H200 achieved 23% higher throughput than the H100 for batch sizes of 32+. For smaller batch sizes (real-time serving with low latency targets), the gap narrows to 12-15%.

Best for: Teams upgrading from H100 who want a drop-in replacement with better memory capacity. 70B model serving at FP16 where H100's 80GB is too tight. High-throughput batch inference where memory bandwidth is the bottleneck.

NVIDIA B200 (192GB HBM3e)

The B200's headline is compute: 4,500 FP8 TFLOPS versus the H100's 1,979. But for inference, the more interesting number is its 192GB HBM3e at 8,000 GB/s — matching MI300X on capacity with significantly higher bandwidth.

The B200 also introduces FP4 precision support, which enables running 405B parameter models on a single GPU at FP4 with quality comparable to FP8 on earlier hardware. This is a genuine step change for frontier model inference.

Best for: Teams building infrastructure for 2026 and beyond who want a single GPU that handles everything from 7B to 405B models efficiently. Next-generation inference engines (TensorRT-LLM 2.x) that can leverage FP4 precision.

Weakness: High cost and limited on-demand availability outside CoreWeave and major cloud providers. Inference-optimized software (TensorRT-LLM FP4 support) is still maturing.

NVIDIA L40S (48GB GDDR6)

The L40S is the inference-optimized workhorse for teams running 7B-13B models at scale. At 48GB of GDDR6 memory, it handles Llama 3 8B at FP16 comfortably and Llama 3 70B at INT4 in a 4-GPU configuration. The L40S uses GDDR6 rather than HBM, which means lower bandwidth (864 GB/s) but dramatically lower cost — approximately $1,400-1,800/month on cloud versus $3,000-4,000/month for H100.

For inference workloads running models under 30B parameters, the L40S delivers outstanding cost-per-token efficiency. We have seen production deployments where L40S nodes serving INT8 quantized 13B models match or beat H100 deployments in tokens-per-dollar.

Best for: Cost-optimized inference for 7B-13B models. Teams with $/token as the primary optimization target. Inference-only deployments that do not need training capability.

Decision Matrix: Which GPU for Your Use Case

Use Case	Best GPU	Why
70B+ at FP16 (single GPU)	MI300X or H200	Memory capacity — H100's 80GB is too small
70B at INT8 (cost-optimized)	H100 SXM5	80GB sufficient, massive software ecosystem
405B+ models	B200 or MI300X ×2	192GB+ required; B200 FP4 enables single-GPU 405B
7B-13B at maximum throughput	L40S ×4	Best tokens/dollar for small model inference
Long-context (128K+ tokens)	MI300X	KV cache needs memory headroom; 192GB wins
Mixed training + inference	H200 or B200	Balanced compute + memory; CUDA ecosystem

Cost-Per-Token: What Actually Matters in Production

Raw throughput numbers (tokens/second) are only half the story. What matters in production is tokens per dollar — and that calculation changes significantly based on whether you are buying hardware or renting cloud instances.

At current cloud pricing (April 2026 on-demand rates):

• H100 @ $4.76/hr (CoreWeave) serving 70B INT8 at 800 tokens/sec → ~$0.00165/1K tokens
• MI300X @ $4.10/hr (CoreWeave) serving 70B FP16 at 650 tokens/sec → ~$0.00175/1K tokens
• H200 @ $5.20/hr (CoreWeave) serving 70B FP16 at 820 tokens/sec → ~$0.00176/1K tokens
• L40S @ $1.50/hr (CoreWeave) serving 13B INT8 at 1,200 tokens/sec → ~$0.000347/1K tokens

The L40S wins on cost-per-token for models that fit comfortably in 48GB. For 70B models, the H100/MI300X/H200 tier is competitive with each other, with the choice driven primarily by whether you need full FP16 quality or can accept INT8.

Our Recommendation

If we were building an inference fleet today, here is our guidance:

1. Under 30B parameters: L40S for cost efficiency. 4× L40S nodes give you 192GB total VRAM at half the cost of H100.
2. 30B-70B parameters: H100 SXM5 at INT8, or H200/MI300X if you need FP16 quality or long-context headroom.
3. 70B-405B parameters: MI300X or H200 for the memory capacity. B200 if budget allows and you want future-proofing.
4. Frontier models (405B+): B200 cluster or MI300X multi-GPU with tensor parallelism.

Not sure which GPU fits your specific model and throughput requirements? Use our AI-Powered GPU Finder to get a personalized recommendation based on your workload, or compare detailed specs side-by-side on our GPU Comparison tool.