The AMD MI300X and NVIDIA H100 are two most widely deployed datacenter GPUs for AI inference, and they disagree on almost everything. The MI300X ships 192GB of HBM3 and costs less per hour at several providers. The H100 delivers better realized throughput, faster multi-GPU networking, and a CUDA software ecosystem built over 15 years.
Neither card is a strict upgrade over the other. The right choice depends on model size, batch profile, and your stack's tolerance for ROCm (AMD's GPU compute framework).
This guide breaks down where each GPU wins, using sourced benchmarks rather than vendor marketing sheets, and pairs each finding with current rental pricing.
Key Takeaways
- Memory is the MI300X's edge: its 192GB VRAM fits a 70B model on one GPU.
- The H100 wins realized compute: about 14% faster BF16 and 22% faster FP8, despite the MI300X's higher marketed TFLOPS.
- Cost-per-token flips by batch size: the MI300X wins at 256+; the H100 wins the 1-128 low end.
- CUDA still leads ROCm: migrating a CUDA-native codebase to the MI300X is not a trivial lift.
- Pricing favors the MI300X on paper: it rents from $2.39/hr versus Thunder Compute's $3.20/hr H100 PCIe.
MI300X vs H100 at a Glance
In short, 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 tells a more useful story than either number alone.
| Spec | AMD MI300X | NVIDIA H100 SXM | NVIDIA H100 PCIe |
|---|---|---|---|
| Architecture | CDNA 3 | Hopper | Hopper |
| VRAM | 192GB HBM3 | 80GB HBM3 | 80GB HBM2e |
| Memory Bandwidth | 5.3 TB/s | 3.35 TB/s | 2.0 TB/s |
| Peak FP161 | 2,610 TFLOPS | 1,979 TFLOPS | 1,513 TFLOPS |
| Realized BF16 GEMM2 | ~620 TFLOP/s | ~720 TFLOP/s | Not tested |
| Peak FP81 | 5,220 TFLOPS | 3,958 TFLOPS | 3,026 TFLOPS |
| Realized FP82 | ~990 TFLOP/s | ~1,280 TFLOP/s | Not tested |
| Multi-GPU Interconnect3 | Infinity Fabric, 128 GB/s per link | NVLink, 900 GB/s | PCIe Gen5, 128 GB/s |
| TDP | 750W | 700W | 350W |
| Software Stack | ROCm | CUDA | CUDA |
| 1 With sparsity, per AMD and NVIDIA datasheets. 2 Realized GEMM figures from SemiAnalysis independent benchmarking on the SXM part. 3 Interconnect bandwidth is bidirectional; H100 SXM NVLink is a switched fabric, MI300X Infinity Fabric is point-to-point per link. |
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The realized numbers matter most for production planning. Despite the MI300X's higher marketed FP16 spec, the H100 runs about 14% faster in real BF16 and about 22% faster in FP8, according to SemiAnalysis's independently run benchmarks. The MI300X's advantage is almost entirely memory, not raw compute.
MI300X vs H100 VRAM: What 192GB vs 80GB Actually Buys You
More VRAM does not make a GPU faster. It changes how many GPUs you need to hold a model, which affects total cost more than throughput.
The H100 SXM's 80GB ceiling is the binding constraint for large-model serving. A Llama 3 70B model in FP16 needs roughly 140GB for weights alone, before KV cache. H100 deployments must run tensor parallelism across two GPUs or quantize to INT8 or FP8 to fit one card. The MI300X's 192GB holds the full FP16 weights plus a meaningful KV cache on a single GPU.
Mixtral 8x7B shows the same pattern. At around 93GB in FP16, it fits on a single MI300X with room for larger batches, but sits too tight on an 80GB H100 once KV cache grows at realistic context lengths.
However, fitting a model on one GPU and serving it fast on one GPU are different claims: the MI300X's memory win does not automatically become a speed win.
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+. RunPod's inference benchmarks on Mixtral 8x7B provide the most widely cited batch-size breakdown. Beyond batch size 128, the MI300X's larger VRAM lets it hold bigger workloads without spilling across GPUs.
The H100's lead holds across the full 1-128 range, which covers most real-time chat and agent endpoints. The MI300X's turn comes once batch sizes grow large enough that its 192GB holds the Mixtral model on a single card, where an equivalent H100 deployment splits across two GPUs and pays the tensor-parallelism overhead.
MI300X vs H100 Cost Per Token
The H100 wins cost-per-token across batch sizes 1-128, the range that covers most real-time chat, API, and agent serving. The MI300X reclaims the lead at 256+, where its 192GB VRAM absorbs larger working sets that would otherwise spill across multiple H100s. A low-QPS single-stream endpoint and a high-throughput batch pipeline therefore land on opposite sides of the cost-per-token comparison.
Latency and Time-to-First-Token
For latency-sensitive workloads, the H100 is the safer default. At small batch sizes, the H100 SXM's lower memory latency, which low-level benchmarks measured at roughly 57% better than the MI300X, produces faster first-token response. At high concurrency, benchmarks comparing 8x H100 SXM against 4x MI300X show the H100 cluster handling about 74% more requests/second and roughly halving time-to-first-token on Llama 3.1 405B. If P99 latency is a hard SLA, the H100 is the right anchor.
MI300X vs H100 Training Performance
The H100 is the stronger training GPU by a clear margin, mainly because of the CUDA ecosystem. SemiAnalysis's GEMM (general matrix multiply) benchmarks show the H100 delivering about 720 TFLOP/s BF16 against the MI300X's 620 TFLOP/s, a 14% gap despite the MI300X's higher headline spec. The FP8 gap is wider: the H100 realizes roughly 1,280 TFLOP/s against the MI300X's 990 TFLOP/s, about 22% faster.
The gap widens on non-standard attention patterns. On workloads like Mistral 7B's sliding-window attention, ROCm's performance falls well below CUDA-optimized kernels. The cause is software maturity, not hardware: a 15-year-old CUDA stack versus an 10-year-old ROCm ecosystem that still shows gaps.
Training on H100s and serving inference on the MI300X is a workflow gaining traction. It uses the H100's compute and CUDA maturity for training, then the MI300X's memory headroom for serving large models without tensor parallelism. CUDA-committed teams can adopt the MI300X for inference without abandoning their training tooling.
ROCm vs CUDA: The Software Reality
The software gap is the MI300X's biggest practical disadvantage. CUDA has a 15-year head start in library support, kernel optimization, and compiler maturity. ROCm 6.x has closed real ground, and most standard PyTorch and JAX workloads run without major issues. Compatibility gaps persist on newer FlashAttention variants, some quantization libraries, and FP8 inference tooling.
The migration cost is real. Teams running CUDA-optimized inference with TensorRT-LLM (NVIDIA's inference optimization library) need validation time and may hit friction on non-standard attention layers. Budget 2-4 weeks of migration validation for a CUDA-native codebase, plus occasional edge-case ROCm fixes afterward. Teams building fresh on PyTorch or JAX have an easier path than heavy TensorRT-LLM users.
Multi-GPU Scaling: NVLink vs Infinity Fabric
The H100's NVLink gives it the multi-node scaling advantage. NVLink (NVIDIA's GPU interconnect) 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. Its seven-to-eight links aggregate to a comparable per-GPU total, but the switched NVLink topology delivers higher effective bandwidth for the all-to-all traffic in large all-reduce operations.
That bandwidth difference compounds in distributed training. At the message sizes that dominate all-reduce (16-256 MB), NVLink's topology speeds up every parameter update, and for multi-node jobs it often outweighs raw compute. Within a single 8-GPU node, the MI300X's shared-memory architecture partly offsets the gap for models that fit on one node.
MI300X vs H100 Live Pricing
The MI300X rents from $2.39/hr, undercutting most H100 rates, while Thunder Compute's H100 PCIe runs $3.20/hr. Rates vary by provider, region, and commitment tier.
| Provider | GPU | On-Demand ($/GPU/hr) | Notes |
|---|---|---|---|
| Thunder Compute | H100 PCIe | $3.20 | In stock, per-minute billing, no commitments |
| RunPod | MI300X | $2.39 | Cheapest available; on-demand, self-serve |
| DigitalOcean | MI300X | $2.59 | On-demand |
| Crusoe Cloud | MI300X | $3.45 | On-demand |
| Vultr | MI300X | $3.99 | On-demand |
| Azure | MI300X (8-GPU node) | $6.00+ | eastus2; ranges $6.00-$7.86/GPU-hr across US regions |
| Oracle Cloud | MI300X (8-GPU node) | $6.00 | BM.GPU.MI300X.8, normalized per GPU |
| TensorWave | MI300X | Quote only | No self-serve; rate depends on a sales quote |
| Last reviewed on September 8, 2026. | |||
How to Choose: MI300X vs H100 Decision Framework
The right GPU depends on three variables: model size, batch profile, and software stack.
Choose the MI300X when:
- You serve a 70B+ parameter model in FP16 and want to skip tensor parallelism on one GPU.
- Your traffic runs at batch size 256+ (high-throughput pipelines), where the MI300X's cost-per-token advantage is real.
- 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 is in the 1-128 batch size range, covering most real-time chat, API serving, and agents
- P99 time-to-first-token latency is a hard constraint
- Your stack is already optimized for CUDA and TensorRT-LLM
If your model is smaller than 70B and does not need the MI300X's memory headroom, check whether the H100 beats it on cost-per-token at your batch size before defaulting to the MI300X for savings.
Running Your Workload on H100 with Thunder Compute
Teams whose workloads land in the H100 column can rent H100 PCIe instances on Thunder Compute 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, plus VS Code and Cursor extensions for development from a local IDE.
Setup takes under two minutes: pick a GPU configuration, connect VS Code or Cursor, and the environment is ready. 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 slots between the H100 and MI300X on memory. It ships with 141GB of HBM3e and 4.8 TB/s of bandwidth, closing much of the memory gap with the MI300X while keeping full CUDA compatibility. At cloud rates of $3.44-$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. It is the right long-term comparison target for the MI300X, though availability is limited and the ROCm maturity question still applies. For a decision today, the MI300X is the relevant AMD option.
Last Thoughts on the MI300X vs H100
The MI300X and H100 suit different workloads. The MI300X delivers real memory and cost advantages for 70B+ models served at batch size 256+, 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, and it wins on both throughput and total cost of ownership for the majority of inference workloads at standard batch sizes.