AMD MI300X vs NVIDIA H100: Specs, Benchmarks, and Live Pricing (2026)

The AMD MI300X wins on memory capacity and hourly price; the NVIDIA H100 wins on realized compute, networking, and software. Both are widely deployed datacenter GPUs for AI inference.

The MI300X ships 192GB of HBM3 and rents for less per hour at several providers. The H100 pairs higher realized throughput and faster multi-GPU networking with a CUDA software ecosystem built since 2007.

Neither card is a strict upgrade. The right choice depends on model size, batch profile, and your stack's tolerance for ROCm (AMD's GPU compute framework). This comparison relies on independent benchmarks rather than vendor marketing, and pairs each finding with current rental pricing.

MI300X vs H100 at a Glance

The MI300X wins on memory capacity and per-GPU-hour price at some providers. The H100 wins on realized compute throughput, multi-node networking, and low-batch inference latency. The spec table shows both marketed and realized TFLOPS, because the gap between them matters more than either number.

The H100 outperforms the MI300X on realized compute, despite the MI300X's higher marketed FP16 spec. According to SemiAnalysis's independently run benchmarks, the MI300X runs about 14% slower in real BF16 and about 22% slower in FP8. The MI300X's advantage is memory, not raw compute.

CDNA 3 vs Hopper Architecture

The MI300X's CDNA 3 architecture (AMD's datacenter GPU design) adds a 256MB Infinity Cache that the H100's single Hopper die lacks. AMD builds the MI300X from eight compute dies stacked on four I/O dies, all sharing that Infinity Cache. In Chips and Cheese's low-level tests, the MI300X's cache delivered more bandwidth than the H100's at every level, and its raw instruction throughput ran well ahead. Those microbenchmark wins shrink in real workloads, where the software stack decides how much of the MI300X's hardware gets used.

MI300X vs H100 VRAM: What 192GB vs 80GB Actually Buys You

The MI300X's 192GB VRAM reduces how many GPUs a large model needs, but it does not make the MI300X faster than the H100. GPU count can shape total cost more than throughput.

The H100 SXM's 80GB ceiling is the binding constraint for large-model serving. Llama 3 70B in FP16 needs roughly 140GB for weights alone, before KV cache (the memory that stores attention state for each token). H100 deployments must split the model across two GPUs, or quantize to INT8 or FP8. The MI300X's 192GB holds the full FP16 weights plus a meaningful KV cache on one GPU.

Mixtral 8x7B follows the same pattern. At around 93GB in FP16, Mixtral fits on a single MI300X with room for larger batches. On an 80GB H100, Mixtral's FP16 weights do not fit at all. H100 deployments need two GPUs with tensor parallelism, or quantization to FP8 or INT8.

MI300X vs H100 Inference Benchmarks

Throughput by Batch Size

The H100 SXM leads the MI300X on inference throughput at batch sizes 1-128, and the MI300X pulls ahead at 256+. The data comes from RunPod's inference benchmarks on Mixtral 8x7B.

The MI300X gains ground at large batch sizes because its 192GB holds Mixtral on one card. An equivalent H100 deployment splits Mixtral across two GPUs and pays the tensor-parallelism overhead.

MI300X vs H100 Cost Per Token

The MI300X is cheaper per token at batch sizes 1-4 and 256+, while the H100 SXM is cheaper in between. The figures come from RunPod's Mixtral 8x7B tests. At batch size 1, the MI300X cost about $22/1M tokens versus $28/1M tokens for the H100.

GPU count explains why throughput and cost disagree at low batch sizes. Mixtral does not fit on one 80GB H100, so the test split it across two H100s, doubling the hourly bill. The H100 pair produced more tokens/second, but not enough to offset the second card's cost at light traffic.

The H100 is the cheaper option per token for most real-time chat, API, and agent serving, which falls between batch sizes 4 and 256. The MI300X's cost edge applies to high-throughput batch pipelines, and to low-traffic endpoints running models too large for one H100.

Latency and Time-to-First-Token

The H100 is the safer default for latency-sensitive inference. At small batch sizes, the H100 benefits from lower memory latency, which low-level benchmarks measured at roughly 57% better than the MI300X.

The H100 also leads at high concurrency. dstack (an open-source GPU orchestration tool) ran benchmarks comparing 8x H100 SXM against 4x MI300X on Llama 3.1 405B, where the H100 cluster handled about 74% more requests/second and roughly halved time-to-first-token. The MI300X side used half as many GPUs but had more total memory. For hard P99 latency SLAs, the H100 is the right anchor.

MI300X vs H100 Training Performance

The H100 is the stronger training GPU, mainly because of the CUDA ecosystem. SemiAnalysis's GEMM (general matrix multiply) benchmarks show the H100 delivering about 720 TFLOP/s in BF16, versus 620 TFLOP/s for the MI300X. The FP8 gap is wider: roughly 1,280 TFLOP/s for the H100 versus 990 TFLOP/s for the MI300X.

The MI300X falls further behind on non-standard attention patterns. On workloads like Mistral 7B's sliding-window attention, ROCm performance falls well below CUDA-optimized kernels. The cause is software maturity, not hardware: a CUDA stack dating to 2007 versus a ROCm ecosystem launched in 2016 that still shows gaps.

Training on NVIDIA GPUs and serving inference on the MI300X has a high-profile precedent. Microsoft serves GPT-3.5 and GPT-4 on MI300X for Azure OpenAI Service, while the models themselves were trained on NVIDIA hardware. The split lets CUDA-committed teams use the MI300X's memory headroom for serving without abandoning their training tooling.

ROCm vs CUDA: The Software Reality

The software gap is the MI300X's biggest practical disadvantage. CUDA has a nine-year head start in library support, kernel optimization, and compiler maturity. ROCm 7 has closed real ground, and most standard PyTorch and JAX (Google's ML framework) workloads run without major issues. Compatibility gaps can still appear in:

• Newer FlashAttention variants
• Some quantization libraries
• FP8 inference tooling

Migrating a CUDA-native codebase to the MI300X takes real validation time. Teams running TensorRT-LLM (NVIDIA's inference optimization library) may hit friction on non-standard attention layers, plus occasional edge-case ROCm fixes afterward. Teams building fresh on PyTorch or JAX have an easier path.

Multi-GPU Scaling: NVLink vs Infinity Fabric

The H100's NVLink (NVIDIA's GPU interconnect) gives it the multi-node scaling advantage over the MI300X. NVLink provides 900 GB/s of bidirectional bandwidth per GPU through a switched fabric, so any GPU reaches any other at full bandwidth. The MI300X's Infinity Fabric (AMD's GPU interconnect) provides 128 GB/s per bidirectional point-to-point link. The MI300X's seven scale-up links add up to a comparable per-GPU total, but NVLink's switched topology delivers higher effective bandwidth for large all-reduce operations.

NVLink's bandwidth advantage compounds in distributed training. In all-reduce-heavy workloads, NVLink speeds up every parameter update, and for multi-node jobs it often outweighs raw compute.

MI300X vs H100 Live Pricing

The MI300X rents from $2.39/hr, undercutting most H100 rates, while Thunder Compute's H100 runs $3.20/hr. Rates vary by provider, region, and commitment tier.

How to Choose: MI300X vs H100 Decision Framework

The right choice between the MI300X and H100 depends on model size, batch profile, and software stack.

Choose the MI300X when:

• You serve a 70B+ model in FP16 and want to avoid tensor parallelism.
• Your traffic runs at batch size 256+, or you serve a model too large for one H100 at very low traffic.
• Your codebase has limited TensorRT-LLM dependency.

Choose the H100 when:

• You are training, not just serving.
• Your workload spans multiple nodes and depends on fast all-reduce operations.
• Your traffic sits between batch sizes 4 and 256, covering most real-time chat, API serving, and agents.
• P99 time-to-first-token is a hard constraint.
• Your stack is already optimized for CUDA and TensorRT-LLM.

Running Your Workload on H100 with Thunder Compute

Thunder Compute rents H100 instances at $3.20/hr on-demand, billed by the minute with no minimum commitment. Instances come in 1x, 2x, 4x, and 8x configurations with persistent storage. VS Code and Cursor extensions let you develop from a local IDE.

Setup takes under two minutes: pick a GPU configuration, connect VS Code or Cursor, and start working. There is no cluster management overhead and no long-term contract. Per-minute billing makes it cheap to test the H100 on a real workload before committing to a longer run.

H200 and MI355X: Where the Newer Generation Fits

The H200 closes much of the memory gap with the MI300X while keeping full CUDA compatibility. The H200 ships with 141GB of HBM3e and 4.8 TB/s of bandwidth, placing it between the H100 and MI300X on memory. At on-demand cloud rates of $3.99-$10.60/hr, the H200 suits teams that need more than the H100's 80GB but want to stay on CUDA.

The MI355X is AMD's next-generation CDNA 4 card, with 288GB HBM3e, now appearing at a handful of providers. The MI355X is the right long-term comparison target for the MI300X, but availability is limited and the ROCm maturity question still applies.

Last Thoughts on AMD MI300X vs NVIDIA H100

The MI300X and H100 suit different workloads. The MI300X delivers real memory and cost advantages for 70B+ models at very low or very high (256+) batch sizes, if your team can handle ROCm. The H100 remains the more reliable choice for training, multi-node scaling, latency-sensitive inference, and CUDA-committed stacks. The H100 also wins on throughput and cost per token for most inference workloads at standard batch sizes.

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