TL;DR: The MI300X wins on memory (192GB vs 80GB) and bandwidth (5.3 TB/s vs 3.35 TB/s), making it excellent for very large models and high-batch inference. The H100 wins on software maturity (CUDA ecosystem) and lower latency for real-time applications. Price per inference varies by workload—neither is universally cheaper.
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The Specs That Matter
| Specification | AMD MI300X | NVIDIA H100 SXM |
|---|---|---|
| HBM Memory | 192 GB HBM3 | 80 GB HBM2e |
| Memory Bandwidth | 5.3 TB/s | 3.35 TB/s |
| FP16 Performance | 1,307 TFLOPS | 1,979 TFLOPS |
| FP8 Performance | 2,614 TFLOPS | 3,958 TFLOPS |
| TDP | 750W | 700W |
| Interconnect | Infinity Fabric | NVLink 4.0 |
The headline numbers favor AMD on memory and NVIDIA on compute. But real-world performance depends on workload characteristics.
Benchmark Reality Check
LLM Inference Performance
Based on published benchmarks from RunPod and dstack:
Small Batch Sizes (1-4):
- MI300X shows 40% latency advantage for LLaMA2-70B
- Larger memory eliminates need for model sharding
- Cost per million tokens: MI300X $22-11 vs H100 $28-14
Medium Batch Sizes (8-128):
- H100 generally performs better
- CUDA optimization provides consistent advantage
- Cost advantage shifts to H100
Large Batch Sizes (256+):
- MI300X regains advantage
- 192GB memory prevents OOM at high concurrency
- Better throughput per dollar
The Mixtral Example
The Mixtral 8x7B model is a clear illustration of the memory advantage:
- H100 80GB: Requires 2 GPUs with tensor parallelism
- MI300X 192GB: Fits on single GPU
This halves infrastructure complexity and potentially cost for large MoE models.
Training Performance
According to SemiAnalysis benchmarks:
- H100 maintains 15-20% advantage in training throughput
- CUDA libraries (cuDNN, cuBLAS) more optimized
- MI300X competitive but requires more tuning
The Software Reality
This is where the comparison gets uncomfortable for AMD advocates.
CUDA Ecosystem
- 20+ years of optimization
- Every major ML framework optimized for CUDA
- Extensive documentation and community support
- Works out of the box
ROCm Ecosystem
- Improving rapidly but still behind
- Some libraries require manual optimization
- PyTorch support good, but edge cases exist
- "Considerable patience and elbow grease" required for some workloads
The honest assessment: if you're running standard LLM inference with vLLM or TGI, ROCm works well. If you're doing custom CUDA kernels or specialized training loops, expect friction.
When to Choose MI300X
Strong Use Cases
- Very large models (70B+): 192GB eliminates multi-GPU complexity
- High-batch inference: Better throughput at batch sizes 256+
- Memory-bound workloads: 5.3 TB/s bandwidth shines
- Vendor diversification: Reduce NVIDIA dependency
- Specific cost scenarios: When cloud pricing favors AMD (varies by provider)
Weak Use Cases
- Real-time applications: H100 latency often better at medium batch
- Custom training code: CUDA optimization still ahead
- Teams without GPU experience: ROCm learning curve is real
When to Choose H100
Strong Use Cases
- Training workloads: CUDA optimization matters most here
- Low-latency inference: Better for real-time applications
- Existing CUDA codebase: Zero migration effort
- Enterprise support: NVIDIA ecosystem is more mature
- Multi-GPU training: NVLink optimization is excellent
Weak Use Cases
- Memory-constrained large models: 80GB can be limiting
- Very high batch sizes: MI300X often wins here
- Cost-sensitive high-throughput: Depends on specific workload
Pricing Reality
Hardware Costs
Direct comparison is difficult because:
- MI300X systems often require "contact for quote"
- H100 systems range from $29,000 (single PCIe card) to $375,000+ (8-GPU servers)
- Availability varies significantly by vendor
Cloud Costs (December 2025)
| Provider | H100 SXM | MI300X | Notes |
|---|---|---|---|
| RunPod | $4.69/hr | $4.89/hr | Similar pricing |
| Lambda Labs | $2.49-3.29/hr | Limited | H100 more available |
| CoreWeave | $4.76/hr | Contact | Enterprise agreements |
Cloud pricing often makes the hardware comparison moot—it comes down to workload fit.
The OpenAI Factor
In December 2025, reports emerged of OpenAI securing a major AMD GPU deal for 6 gigawatts of compute capacity. This signals:
- AMD's credibility for production AI workloads
- ROCm maturity reaching enterprise acceptance
- Market diversification away from NVIDIA monopoly
However, OpenAI's engineering resources are exceptional. What works for a 1,000-person AI lab may not translate directly to smaller organizations.
Practical Recommendations
For New AI Teams
Start with H100. The ecosystem maturity reduces time-to-production. You can always migrate later when ROCm matures further.
Recommended products:
- PNY NVIDIA H100 NVL 94GB - $28,999 for single-card deployments
- H100 GPU Servers - Multi-GPU configurations
For Large-Scale Inference
Evaluate MI300X seriously. The memory advantage at 192GB is significant for 70B+ models, and vLLM/TGI support is mature.
Note: MI300X systems typically require direct vendor quotes. Contact AMD partners or cloud providers for availability.
For Training-Heavy Workloads
H100 remains the safer choice. Training requires the deepest software optimization, where CUDA still leads.
For Vendor Diversification
If reducing NVIDIA dependency is strategic, MI300X is the most viable alternative. Budget for additional engineering time during initial deployment.
What About H200 and B200?
The H200 (141GB HBM3e) and B200 (next-gen Blackwell) shift the comparison:
| GPU | Memory | Bandwidth | Status |
|---|---|---|---|
| H100 | 80GB | 3.35 TB/s | Widely available |
| H200 | 141GB | 4.8 TB/s | Ramping production |
| MI300X | 192GB | 5.3 TB/s | Available |
| B200 | 192GB | 8.0 TB/s | Limited availability |
The H200 closes the memory gap significantly. For buyers considering MI300X primarily for memory, H200 may be a simpler path.
The Bottom Line
Neither GPU is universally better. The choice depends on:
- Model size: >100GB models favor MI300X memory
- Batch size: High batch favors MI300X, medium batch favors H100
- Workload type: Training favors H100, large-model inference is competitive
- Team experience: CUDA experience reduces H100 friction significantly
- Strategic goals: Vendor diversification may justify AMD despite friction
For most organizations buying their first AI accelerators, the H100 ecosystem advantage outweighs MI300X's memory advantage. For organizations already running at scale and optimizing for specific workloads, MI300X deserves serious evaluation.
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Browse H100 Products:
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Benchmark data sourced from RunPod, dstack, and SemiAnalysis. Prices reflect December 2025 market conditions. MI300X availability varies by region and vendor.
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