AMD MI300X Cloud Pricing 2026: Compare Providers & Cost vs H100

The AMD MI300X is AMD's highest-performing GPU built specifically for AI inference workloads. Launched in late 2023 and ramping through 2024–2025, the MI300X packs 192GB of HBM3 memory onto a single card — 2.4× what the NVIDIA H100 SXM5 offers. This memory capacity is the MI300X's defining advantage: models up to approximately 130–140 billion parameters fit in full BF16 precision on a single MI300X, something that requires multi-GPU tensor parallelism on an H100. For teams running Llama 3 70B, Mistral 8×22B, or any 70–405B class model, a single MI300X can replace what would otherwise require two to four H100s — at a lower total hourly cost and with less operational complexity. This page tracks every live MI300X cloud rental available in 2026, from verified on-demand prices to provider availability. Whether you're evaluating MI300X vs H100 for your inference stack, calculating cost-per-token for a 405B model, or building multi-GPU clusters for a large MoE deployment, GridStackHub's MI300X pricing database has the data you need to make the call.

AMD MI300X Technical Specifications

AMD's Instinct MI300X is built on a 5nm CDNA 3 architecture with 304 compute units, 192GB HBM3 memory, and a 5.3 TB/s memory bandwidth. Here is the full spec sheet:

AMD MI300X: Best For — Workload Guide

The MI300X's 192GB of HBM3 and 5.3 TB/s bandwidth are not neutral advantages — they are decisive for specific workload types. Here is a direct guide to what the MI300X is built for:

AMD MI300X Cloud Pricing — Live Table (April 2026)

Three cloud providers currently list AMD MI300X (192GB HBM3) capacity in GridStackHub's database. Availability is growing rapidly as AMD scales Instinct production following Meta's 6GW deployment announcement in February 2026.

Data sourced from GridStackHub's live pricing database, April 20, 2026. VERIFIED = confirmed via live provider API. ESTIMATE = sourced from public pricing pages; verify before committing. More providers being tracked as MI300X availability expands.

AMD MI300X vs NVIDIA H100: Full Comparison

The MI300X and H100 are both frontier GPUs, but they are built for different jobs. Here is a complete side-by-side on the specs that matter for AI workloads:

The story in one sentence: H100 wins on raw compute throughput and software maturity; MI300X wins on memory capacity and memory bandwidth. Which GPU you choose depends entirely on whether your workload is compute-bound or memory-bound.

When MI300X Beats H100: Use Case Guide

The decision rule is straightforward: if your model fits in 80GB, start with H100. If it doesn't — or if memory bandwidth is your primary bottleneck — the MI300X is likely the better and cheaper choice.

Choose MI300X when:

• You're serving models above 70B parameters at BF16 precision. Llama 3 70B requires ~140GB at BF16, fitting comfortably on a single MI300X. On H100, you'd need two GPUs with tensor parallelism, doubling cost and adding ~15–25% latency overhead from cross-GPU communication.
• You're running 405B or 180B models in 4-bit quantization. Llama 3.1 405B in AWQ/GPTQ fits on one MI300X (192GB). On H100, you'd need 3–4 GPUs — a 3–4× cost multiplier.
• Memory bandwidth is your inference bottleneck. For memory-bound workloads (autoregressive decoding, long-context inference), the MI300X's 5.3 TB/s bandwidth can deliver faster token generation than H100's 3.35 TB/s — even if raw TFLOPS favor H100.
• You want the lowest cost-per-GB of working memory. At $0.0096/GB vs $0.0218/GB, MI300X is 55% cheaper per GB of GPU memory. For context-heavy inference workloads, this difference compounds significantly at scale.
• You're building with ROCm-compatible frameworks. PyTorch, vLLM, and llama.cpp all have ROCm support. If your stack is already tested with ROCm, MI300X runs with minimal changes.

Choose H100 when:

• You're training or fine-tuning models at scale. H100's higher FP8/BF16 throughput and more mature NVLink fabric make it the faster, more cost-effective choice for multi-GPU training runs.
• Your stack is CUDA-dependent. If you're using CUDA-specific libraries, custom kernels, or NVIDIA-only tools (TensorRT, Triton, cuDNN), switching to MI300X adds migration overhead that may outweigh the cost advantage.
• Your model is under 70B parameters and compute-bound. Smaller models that fit in 80GB of VRAM run faster on H100 due to higher raw TFLOPS. The MI300X's memory advantage doesn't help workloads that aren't memory-constrained.
• You need broad provider choice and guaranteed availability. H100 is available from 10+ providers with established SLAs. MI300X has fewer options today, though availability is growing fast.
• Enterprise compliance or specific cloud integrations are required. AWS, GCP, and Azure offer H100 with enterprise SLAs, compliance certifications, and ecosystem integrations that MI300X providers don't yet match.

What Models Fit on a Single AMD MI300X?

192GB of HBM3 is a significant amount of GPU memory. Here is a practical reference for what fits at various precision levels:

*Memory estimates include model weights only. Production inference also requires KV cache (scales with batch size and context length). Estimate 20–40% additional memory overhead for KV cache in typical serving configurations.

Frequently Asked Questions

Track MI300X Prices and Get Alerts

MI300X availability and pricing are moving fast in 2026. As more providers add capacity and AMD scales production, prices will shift. GridStackHub tracks every change — here is how to stay ahead of it: