According to GridStackHub.ai data, AMD MI300X cloud pricing starts at $1.85/hr (Thunder Compute, on-demand) while NVIDIA H100 starts at $1.74/hr (Lambda, on-demand) — a $0.11/hr difference per GPU. However, the MI300X's 192GB VRAM versus the H100's 80GB means that for 70B+ parameter models, a single MI300X replaces two H100s, cutting effective cost in half. For smaller workloads, H100 has broader availability (15+ providers) and the mature CUDA ecosystem. The right choice depends on your specific model size and throughput requirements.
Best for large models
192GB VRAM fits 70B+ models on one GPU. 5.3 TB/s bandwidth for high-throughput inference. Best cost-per-token for memory-bound workloads over 40GB.
Best for ecosystem & scale
CUDA ecosystem, 15+ cloud providers, mature tooling. Cheaper for models under 40GB. Best for multi-GPU training with NVLink/NCCL.
MI300X vs H100 Cloud Pricing — May 2026
GridStackHub tracks real-time pricing for both AMD MI300X and NVIDIA H100 across all major cloud providers. Here is the full comparison of available providers as of May 2026:
| Provider | GPU | VRAM | Type | Price/hr | Status |
|---|---|---|---|---|---|
| Thunder Compute | AMD MI300X | 192 GB HBM3 | On-demand | $1.85/hr | VERIFIED |
| Microsoft Azure | AMD MI300X (ND MI300X v5) | 192 GB HBM3 | On-demand | $3.50/hr | VERIFIED |
| Oracle Cloud | AMD MI300X | 192 GB HBM3 | On-demand | $3.75/hr | ESTIMATE |
| NVIDIA H100 providers below | |||||
| Lambda | NVIDIA H100 SXM5 | 80 GB HBM3 | On-demand | $1.74/hr | VERIFIED |
| RunPod | NVIDIA H100 SXM5 | 80 GB HBM3 | On-demand | $1.99/hr | VERIFIED |
| CoreWeave | NVIDIA H100 SXM5 | 80 GB HBM3 | On-demand | $2.19/hr | VERIFIED |
| Vast.ai | NVIDIA H100 SXM5 | 80 GB HBM3 | Spot/Market | $1.35–1.89/hr | VERIFIED |
| Google Cloud | NVIDIA H100 (a3-highgpu) | 80 GB HBM3 | On-demand | $3.09/hr | VERIFIED |
| AWS | NVIDIA H100 (p5.48xlarge) | 80 GB HBM3 | On-demand | $4.84/hr | VERIFIED |
Data sourced from GridStackHub's live pricing database, May 3, 2026. Prices shown per GPU. VERIFIED = confirmed via provider API or pricing page. ESTIMATE = based on publicly available data, may vary. Hyperscaler H100 pricing is per-GPU equivalent from multi-GPU instances.
Key insight: At the per-GPU level, MI300X ($1.85/hr) and H100 ($1.74/hr) are nearly identical in cost. The economic case for MI300X only emerges for models that require more than 80GB VRAM — where you'd need 2 H100s ($3.48/hr) versus 1 MI300X ($1.85/hr). That's a 47% cost saving per GPU-set.
MI300X vs H100: Full Specification Comparison
The AMD MI300X and NVIDIA H100 are both datacenter-class AI accelerators, but they target different strengths. Here is the complete side-by-side specification breakdown:
| Specification | AMD MI300X | NVIDIA H100 SXM5 | Winner |
|---|---|---|---|
| Architecture | AMD CDNA 3 | NVIDIA Hopper | — |
| GPU Memory | 192 GB HBM3 | 80 GB HBM3 | AMD ✕2.4 |
| Memory Bandwidth | 5.3 TB/s | 3.35 TB/s | AMD +58% |
| FP16 / BF16 Throughput | ~2,615 TFLOPS | 1,979 TFLOPS | AMD +32% |
| FP8 Throughput | ~5,220 TFLOPS | 3,958 TFLOPS | AMD +32% |
| FP64 Throughput | 1,307 TFLOPS | 3,958 TFLOPS | NVIDIA ✕3 |
| Memory Type | HBM3 (8 stacks) | HBM3 | Tie |
| TDP (Power) | 750W | 700W | NVIDIA |
| Min Cloud Price | $1.85/hr (Thunder) | $1.74/hr (Lambda) | NVIDIA |
| 70B model on 1 GPU (BF16) | Yes — fits comfortably | No — needs 2 GPUs | AMD |
| Models fit at BF16 | Up to ~80B params | Up to ~35B params | AMD |
| Cloud Provider Count | 3–4 providers | 15+ providers | NVIDIA |
| Software Ecosystem | ROCm 6.x (improving) | CUDA (mature) | NVIDIA |
| Spot Pricing Available | Limited | Yes (Vast.ai, RunPod) | NVIDIA |
| Cost for 70B BF16 inference | $1.85/hr (1 GPU) | $3.48/hr (2 GPUs) | AMD −47% |
Inference Throughput: MI300X vs H100
Memory bandwidth is the dominant constraint for LLM inference during the decoding (autoregressive) phase. The MI300X's 5.3 TB/s bandwidth versus H100's 3.35 TB/s gives it a theoretical 58% throughput advantage on memory-bound workloads — which describes most LLM serving scenarios at typical batch sizes.
| Model | Config | MI300X (est. tok/s) | H100 Setup (est. tok/s) | Cost Efficiency |
|---|---|---|---|---|
| Llama 3 8B (BF16) | 1 GPU | ~4,200 tok/s | ~2,800 tok/s (1x H100) | H100 cheaper per token |
| Llama 3 70B (BF16) | Min GPUs | ~900 tok/s (1x MI300X) | ~700 tok/s (2x H100) | MI300X ~47% cheaper/tok |
| Llama 3 70B (FP8) | Min GPUs | ~1,600 tok/s (1x MI300X) | ~1,200 tok/s (1x H100) | MI300X wins — fits 1 GPU |
| Mixtral 8x7B (BF16) | Min GPUs | ~1,800 tok/s (1x MI300X) | ~1,400 tok/s (1x H100) | MI300X ~6% cheaper/tok |
| Mistral 7B (BF16) | 1 GPU | ~5,000 tok/s | ~3,200 tok/s (1x H100) | H100 cheaper ($1.74 vs $1.85) |
Throughput estimates based on vLLM benchmarks, batch size 1, decode-phase dominant. Actual results vary by batch size, sequence length, and system configuration. Multi-GPU H100 estimates assume 2x tensor parallel with ~85% efficiency.
The 70B inflection point: At Llama 3 70B (BF16, ~140GB), the MI300X serves the model on a single GPU at $1.85/hr while H100 needs 2 GPUs at $3.48/hr. You'd need to run 15,000+ hours per year on that model before the cost difference becomes trivial. For most production inference teams, the MI300X is dramatically cheaper per token at this scale.
MI300X vs H100: Which Should You Choose?
Here is the decision framework based on workload type, model size, and infrastructure requirements:
70B+ BF16 inference (single-GPU)
MI300X is the clear choice. 1 GPU fits Llama 3 70B in BF16 while H100 needs 2. Cost advantage: ~47% lower cost per token at $1.85 vs $3.48/hr for 2x H100.
Long-context inference (128K+ tokens)
MI300X's 192GB VRAM provides significantly more KV-cache headroom for long sequences. H100's 80GB limits KV-cache size, forcing shorter contexts or larger clusters.
Memory-bandwidth-bound workloads
5.3 TB/s vs 3.35 TB/s gives MI300X a consistent advantage on inference decode throughput. Workloads dominated by memory reads (most LLM serving) benefit directly.
7B–34B inference and fine-tuning
H100 at $1.74/hr (vs $1.85/hr MI300X) with broader provider choice and spot pricing from $1.35/hr (Vast.ai). CUDA ecosystem, more tooling, lower friction.
Large-scale multi-GPU training (16+ GPUs)
H100 with NVLink/NVSwitch and mature NCCL support wins for distributed training. CUDA custom kernels, FlashAttention, and training frameworks are CUDA-first.
Spot pricing / interruptible workloads
H100 spot is available at $1.35–$1.89/hr on Vast.ai and RunPod. MI300X spot is limited. For batch inference and training jobs that tolerate interruptions, H100 spot wins on cost.
Custom CUDA kernels or proprietary model code
If your stack includes custom CUDA kernels (Flash Decoding, custom attention, quantization kernels), H100 is the only viable option. ROCm HIP porting adds weeks of engineering work.
Software Ecosystem: ROCm vs CUDA
The AMD MI300X runs on AMD's ROCm (Radeon Open Compute) software stack, while the NVIDIA H100 runs CUDA. This is the biggest practical difference between the two GPUs in 2026.
What works on ROCm in 2026
- PyTorch — full support via ROCm backend; pip install works with ROCm wheels
- vLLM — production-ready ROCm support since vLLM 0.4; MI300X is a supported platform
- Text Generation Inference (TGI) — ROCm/MI300X support in v2.x
- LLaMA.cpp — HIP/ROCm backend available for MI300X
- JAX — experimental ROCm support available
- ONNX Runtime — ROCm execution provider supported
Where CUDA still leads
- Custom CUDA kernels — require HIP porting; not automatic
- FlashAttention — CUDA-optimized; ROCm equivalent (CK-Attention) exists but may differ in performance
- Triton — ROCm Triton support exists but is less mature
- Third-party libraries — many optimize for CUDA first; ROCm support may lag 3–6 months
- Profiling and debugging — NVIDIA Nsight is more mature than AMD ROCm Profiler
Bottom line on software: If you're running standard open-source inference (vLLM, TGI, PyTorch) with standard model weights from Hugging Face, the MI300X works reliably. If you have custom CUDA kernels or depend on specific CUDA optimizations, H100 is the safer path.
MI300X vs H100: Monthly Cost by Workload
Here is what 24/7 on-demand usage costs per month for each GPU and use case:
| Workload | MI300X Cost/Month | H100 Cost/Month | Savings |
|---|---|---|---|
| 7B–13B inference (1 GPU) | $1,332/mo (1x MI300X) | $1,253/mo (1x H100) | H100 saves $79/mo |
| 70B BF16 inference (min GPUs) | $1,332/mo (1x MI300X) | $2,506/mo (2x H100) | MI300X saves $1,174/mo |
| Fine-tuning 7B–34B | $1,332/mo (1x MI300X) | $1,253/mo (1x H100) | H100 saves $79/mo |
| 70B fine-tuning (min GPUs) | $1,332/mo (1x MI300X) | $2,506/mo (2x H100) | MI300X saves $1,174/mo |
| 8x GPU training cluster | $10,656/mo (8x MI300X) | $10,022/mo (8x H100) | H100 saves $634/mo |
Based on cheapest available on-demand pricing: MI300X $1.85/hr (Thunder Compute), H100 $1.74/hr (Lambda). 24/7 usage = 730 hours/month. Multi-GPU H100 assumes tensor parallel without efficiency penalty (real-world efficiency ~85%).
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MI300X vs H100 for Training
For training workloads, the comparison shifts in H100's favor at large scale. Here is the breakdown:
For training 70B parameter models on a single node, the MI300X's 192GB VRAM per GPU allows reduced gradient checkpointing frequency — gradient checkpointing recomputes activations to save memory at the cost of ~30% training throughput. With enough VRAM to store more activations, training on MI300X can be faster per GPU even if raw FLOPS per dollar slightly favors H100.
For distributed training across 16–64 GPUs, H100 with NCCL, NVLink, and NVSwitch is the established choice. ROCm's equivalent (RCCL) has improved substantially but NVIDIA's interconnect architecture and software maturity still leads for large cluster workloads.
Availability: H100 vs MI300X in 2026
NVIDIA H100 is significantly more available than AMD MI300X in cloud markets. Here is the current state:
| Availability Factor | AMD MI300X | NVIDIA H100 |
|---|---|---|
| On-demand providers | 3–4 | 15+ |
| Spot / interruptible pricing | Very limited | Vast.ai, RunPod, others |
| Hyperscaler support | Azure, Oracle | AWS, GCP, Azure |
| Reserved / committed pricing | Available via Azure | All hyperscalers + major indie providers |
| Bare metal options | Limited | CoreWeave, Lambda, others |
| Single-GPU on-demand | Yes (Thunder Compute, $1.85/hr) | Yes (Lambda $1.74, RunPod $1.99, many more) |
If availability and vendor diversity are important for your infrastructure (reducing single-provider risk, geographic diversity, spot pricing access), H100 is the more resilient choice. MI300X availability is growing — AMD and its cloud partners have been expanding MI300X deployment — but H100 has a multi-year head start in the cloud market.
Compare live MI300X and H100 pricing
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Open GPU Cost Calculator →Frequently Asked Questions
At the per-GPU level, AMD MI300X ($1.85/hr at Thunder Compute) is marginally more expensive than NVIDIA H100 ($1.74/hr at Lambda) in May 2026. However, for workloads requiring more than 80GB VRAM — specifically 70B+ parameter models at BF16 — a single MI300X replaces two H100s, halving the effective cost. According to GridStackHub.ai data, the cost for 70B BF16 inference is $1.85/hr on one MI300X versus $3.48/hr on two H100s. The "cheaper" GPU depends entirely on your model size.
The AMD MI300X has 192GB of HBM3 VRAM — exactly 2.4x more than the NVIDIA H100's 80GB HBM3. This memory advantage is the MI300X's defining characteristic for inference workloads. A 70B parameter model at BF16 requires ~140GB of VRAM, fitting on a single MI300X but needing 2x H100s. For 34B models at BF16 (~68GB), both GPUs work on a single card, but the MI300X has significantly larger KV cache headroom for long-context inference at 128K+ token sequences.
For models above 40GB VRAM requirement (roughly 30B+ at BF16, 70B+ at INT4), the MI300X is better for inference on a cost-per-token basis. Its 192GB VRAM avoids multi-GPU tensor parallelism overhead, and its 5.3 TB/s bandwidth vs H100's 3.35 TB/s delivers higher tokens-per-second on memory-bandwidth-bound decoding. For smaller models (7B–13B), H100 at $1.74/hr with broader provider availability and spot pricing from $1.35/hr (Vast.ai) is the better default.
As of May 2026, AMD MI300X providers include Thunder Compute ($1.85/hr, on-demand), Microsoft Azure (ND MI300X v5 series, ~$3.50/hr), and Oracle Cloud Infrastructure (~$3.75/hr). Availability is significantly more constrained than H100, which is offered by 15+ providers including Lambda, CoreWeave, RunPod, Vast.ai, Google Cloud, AWS, and Azure. H100 has broader geographic coverage and more provider diversity.
Yes. AMD MI300X supports PyTorch, vLLM, TGI, and LLaMA.cpp via AMD's ROCm 6.x software stack. For standard inference using open-source models from Hugging Face, MI300X works reliably in 2026. Friction points remain for custom CUDA kernels (require HIP porting), bleeding-edge CUDA optimizations, and some third-party libraries with CUDA-first support. For standard inference pipelines, the ecosystem gap has narrowed significantly versus 2024.
AMD MI300X delivers 5.3 TB/s HBM3 memory bandwidth versus 3.35 TB/s on the NVIDIA H100 SXM5 — a 58% bandwidth advantage. Memory bandwidth is the primary performance bottleneck for LLM inference decode phase, so this directly translates to higher tokens/second output. For batch size 1 decode on a 70B model (BF16), MI300X on one GPU typically achieves 900+ tok/s versus ~700 tok/s on two H100s — even with the tensor parallelism overhead of the 2-GPU setup factored in.
For training, NVIDIA H100 is the default choice for large distributed runs (16+ GPUs) due to CUDA ecosystem maturity, NCCL, and NVLink/NVSwitch interconnects. For training 40B–100B parameter models on 1–8 GPUs where VRAM is the constraint, MI300X is competitive and can be cheaper — 192GB allows higher batch sizes and less gradient checkpointing overhead. The software ecosystem gap for training (custom kernels, FlashAttention, Triton) still favors H100 for teams with highly optimized training code.