The AMD MI300X's VRAM cost at best available pricing — 55% cheaper per GB of GPU memory than H100 ($0.0218/GB). Same transformer attention math. More memory per dollar, fewer GPUs required for large models.
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.
| Provider | MI300X 192GB | Pricing Type | Status | Notes |
|---|---|---|---|---|
| Thunder Compute | $1.85/hr | On-demand | VERIFIED | Cheapest available; bare-metal MI300X |
| CoreWeave | $2.50/hr | On-demand | ESTIMATE | HPC-grade networking; enterprise SLAs |
| RunPod | $3.49/hr | On-demand | VERIFIED | Flexible billing; serverless option available |
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.
MI300X availability is expanding fast. Following Meta's February 2026 announcement of 6 gigawatts of AMD Instinct GPU deployments, cloud providers are rushing to add MI300X capacity. GridStackHub tracks new providers as they list MI300X — set a price alert to be notified when availability opens at new providers.
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:
| Spec | AMD MI300X | NVIDIA H100 SXM5 | Winner |
|---|---|---|---|
| GPU Memory | 192 GB HBM3 | 80 GB HBM3 | MI300X +2.4× |
| Memory Bandwidth | 5.3 TB/s | 3.35 TB/s | MI300X +58% |
| FP8 Throughput | 2,610 TFLOPS | 3,958 TFLOPS | H100 +52% |
| BF16 Throughput | 1,307 TFLOPS | 1,979 TFLOPS | H100 +51% |
| Min Cloud Price | $1.85/hr | $1.74/hr | H100 (marginally) |
| Cost per GB VRAM | $0.0096/GB | $0.0218/GB | MI300X −55% |
| Models > 130B params (single GPU) | Yes (BF16/FP8) | No (requires multi-GPU) | MI300X |
| Tensor Parallelism Needed (>70B) | Often not required | Usually required | MI300X |
| Software Ecosystem | ROCm (maturing) | CUDA (dominant) | H100 |
| Cloud Availability | Growing (3+ providers) | Broad (10+ providers) | H100 |
| TDP (Power) | 750W | 700W | H100 (marginally) |
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.
The real MI300X opportunity: Most teams running 70B+ models today are paying for 2–4× H100s with tensor parallelism and accepting the latency overhead. A single MI300X eliminates that complexity at a lower total cost. If you have a model above 70B parameters, run the numbers — it's often a slam dunk.
Run the MI300X numbers for your workload
Compare MI300X against H100, A100, and 26+ other providers in the GridStackHub calculator.
Open Calculator →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:
| Model | Parameters | BF16 (2 bytes/param) | 4-bit (0.5 bytes/param) | Fits on MI300X? |
|---|---|---|---|---|
| Llama 3.2 3B | 3B | ~6 GB | ~1.5 GB | Yes — massive headroom |
| Llama 3.1 8B | 8B | ~16 GB | ~4 GB | Yes — run multiple replicas |
| Llama 3.1 70B | 70B | ~140 GB | ~35 GB | Yes — BF16 fits with KV cache room |
| Falcon 180B | 180B | ~360 GB (needs 2×) | ~90 GB | 4-bit: Yes — BF16 needs 2 GPUs |
| Llama 3.1 405B | 405B | ~810 GB (needs 5×) | ~202 GB | 4-bit: marginal (needs careful KV cache tuning) |
| Mixtral 8×22B (MoE) | 141B active | ~282 GB (needs 2×) | ~71 GB | 4-bit: Yes — BF16 needs 2 GPUs |
*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:
Get MI300X price alerts
We'll notify you when MI300X prices drop, new providers list capacity, or a better deal appears. Free — no credit card required.
Compare MI300X vs H100 vs A100 for your workload
Set your model size, hours per month, and precision — see exact monthly cost for every provider in 60 seconds.
Compare Grid Stacks →