MI355X vs GB200
Inference Comparison
AMD's highest-VRAM single GPU (288GB) vs NVIDIA's Blackwell architecture. Full specs, cost per token analysis, and availability reality check.
Important: What is GB200?
GB200 NVL72 is an NVIDIA rack-level system: 36 Grace CPUs + 72 B200 GPUs in a single rack. The GPU inside GB200 is the B200 (192GB HBM3e). This page compares MI355X as a single GPU against the B200 GPU (which powers GB200) and the GB200 system at rack scale.
Single GPU Specs: MI355X vs B200
GPU Architecture
CDNA4
Blackwell
VRAM (single GPU)
288GB HBM3e
192GB HBM3e
Memory Bandwidth
8.0 TB/s
8.0 TB/s
FP8 TFLOPS
4,610
4,500 (9,000 w/ sparsity)
FP16 TFLOPS
2,305
2,250 (4,500 w/ sparsity)
TDP
1,400W
1,000W
Interconnect
xGMI 896 GB/s
NVLink 5.0, 1,800 GB/s
Cloud On-Demand
~$5.50–6.50/hr
~$6.99–8.00/hr
Cloud Availability
Very limited
Limited (CoreWeave)
Ecosystem
ROCm 6.x / PyTorch
CUDA / TensorRT-LLM
Inference Cost per Million Tokens
Estimated cloud pricing. vLLM throughput at batch 16.
| Model / Workload | MI355X Config | MI355X $/1M | B200 Config | B200 $/1M | Winner |
|---|---|---|---|---|---|
| DeepSeek R1 671B FP8 | 3× MI355X (864GB) | $3.27 | 4× B200 (768GB) | $2.59 | B200 ✓ |
| Llama 3.1 405B FP8 | 2× MI355X (576GB) | $1.91 | 3× B200 (576GB) | $1.82 | B200 ✓ |
| Llama 3.1 70B FP16 | 1× MI355X (288GB) | $0.51 | 1× B200 (192GB) | $0.46 | B200 ✓ |
| Qwen 72B FP16 | 1× MI355X | $0.53 | 1× B200 | $0.47 | B200 ✓ |
GB200 NVL72 at Rack Scale
GPUs per rack
72× B200
Total VRAM
13.5 TB HBM3e
Total FP8 TFLOPS
648,000
NVLink across all GPUs
1.8 TB/s each
Rack TDP
~72 kW
Est. cloud price
~$500–700/hr rack
GB200 NVL72 is not a GPU you provision individually — it's a full rack system. At this scale, the 13.5TB of unified memory can serve multiple 400B+ parameter models simultaneously. It's designed for hyperscaler AI training and inference at the largest scales, not typical startup or enterprise inference workloads.
When to Choose Each
Choose MI355X when:
- →You need > 200GB VRAM per GPU (MI355X's 288GB is unique)
- →Your team uses ROCm / JAX (AMD ecosystem)
- →Cost is the priority and B200 availability is limited
- →Running models that benefit from single-GPU VRAM density
Choose B200 (or GB200 system) when:
- →Maximum throughput is required (TensorRT-LLM optimizations)
- →Team is on CUDA with custom kernels
- →You need NVLink 5.0 (1,800 GB/s) for multi-GPU communication
- →Running at GB200 NVL72 rack scale for hyperscaler workloads
FAQs
What is the difference between MI355X and GB200?
MI355X is a single AMD GPU with 288GB HBM3e. GB200 NVL72 is an NVIDIA rack-level system containing 36 Grace CPUs and 72 B200 GPUs. The individual GPU inside GB200 is the B200 (192GB). MI355X has more VRAM per GPU (288GB vs 192GB) but the B200 has better per-GPU compute and the GB200 NVL72 system offers far more total compute at rack scale.
MI355X vs B200 — which has more VRAM?
AMD MI355X has more VRAM per GPU: 288GB HBM3e vs NVIDIA B200's 192GB HBM3e. This means MI355X can serve larger models (like 200B+ parameter models) on a single GPU without quantization, while B200 requires 2 GPUs or heavy quantization for the same model.
Is MI355X or B200 better for LLM inference?
For cost per token, MI355X is cheaper at ~$5.50/hr vs B200 at ~$6.99/hr. MI355X delivers slightly better $/token for 70B–200B models when running on single GPU (288GB VRAM). B200 wins on raw throughput (4,500 vs 4,610 FP8 TFLOPS is similar, but B200's TensorRT-LLM optimizations often add 20–30% more real-world throughput). For teams already on ROCm, MI355X makes sense. For CUDA shops, B200 is the better choice.
Is the GB200 NVL72 available in the cloud?
GB200 NVL72 rack systems are in extremely limited cloud availability as of May 2026. CoreWeave and a few select hyperscalers have GB200 NVL72 available through reservation, but public on-demand availability is very limited. The individual B200 GPU is more widely available on CoreWeave. Most teams needing high-end inference should plan for H100 or B200 single GPUs rather than full GB200 NVL72 racks.