AMD MI300X and MI355X Pricing 2026: Where to Rent Cheapest

AMD's Instinct lineup rents for anywhere between $0.95 and $8.60 an hour per GPU, and the provider you pick matters more than the chip you pick. An MI300X on a bare-metal hyperscaler shape can cost eight times what the same GPU costs on a spot marketplace. This guide breaks down real 2026 rental prices across providers, explains why the spread is so wide, and works through the cost-per-token math against NVIDIA's H100 and H200.

For the hardware comparison behind these numbers, see our AMD MI300X vs NVIDIA H200 and AMD MI350X vs NVIDIA B200 breakdowns. If you're deciding whether to train on AMD hardware at all, our ROCm pretraining guide covers the software side in depth.

MI300X and MI355X On-Demand Pricing Across Providers (2026)

MI300X: From $0.95/hr Spot to $7.86/hr on Azure

MI300X ships with 192 GB of HBM3 memory and 5.3 TB/s of bandwidth on AMD's CDNA 3 architecture, and it's the more mature of the two chips in terms of provider coverage. Fluence's 2026 provider survey put spot and marketplace listings as low as $0.95/GPU-hr, with specialist neoclouds like Hot Aisle and DigitalOcean around $1.99/GPU-hr, and hyperscaler rates running $6.00-$7.86/GPU-hr.

A live snapshot from Thunder Compute's pricing tracker shows the same pattern with different named providers:

Worth flagging: Fluence's April 2026 survey had Azure's MI300X rate at $7.86/GPU-hr ($62.85/hr for the 8-GPU node), noticeably higher than the $6.00/GPU-hr Thunder Compute tracks now. Hyperscaler GPU pricing moves, and a rate you read in one article can already be stale by the time you check out. Always confirm current pricing directly with the provider before committing.

Thunder Compute also ran its own math: Vultr's 8x MI300X cluster at $14.80/hr works out to $1.85/GPU-hr, about 16% cheaper per GPU than the $2.19/hr Thunder Compute charges for a comparable H100.

MI355X: From $2.59/hr on Vultr to $8.60/hr Bare Metal

MI355X is AMD's CDNA 4 follow-up, with 288 GB of HBM3e memory per GPU (2,304 GB total across an 8-GPU pod). It's newer and thinner on availability than MI300X, and that shows up in the price range.

The Vultr number is the standout: a full 8x MI355X pod at $2.59/GPU-hr is cheaper than almost every MI300X on-demand listing above, on the newer chip. That's unusual for a GPU that only recently moved past early access, and it's a sign that MI355X pricing hasn't settled into a predictable band the way MI300X has.

Pricing fluctuates based on GPU availability. The prices above are based on 8 Jul 2026 and may have changed. Check current GPU pricing → for live rates.

Why the Price Spread Is So Wide

A single GPU model shouldn't cost 8x more from one provider to the next for the same silicon. Four factors explain most of the gap.

VRAM Configuration: 192GB MI300X vs 288GB MI355X

MI355X's extra 96 GB of HBM3e over MI300X isn't free. The newer memory stacks cost more to source, yield rates on a new chip are lower in the first year of production, and providers pass that through directly. It's the same dynamic that made H200 cost more than H100 at launch: more memory per GPU, higher bill of materials, higher rental rate, at least until supply catches up with demand.

Bare Metal vs Virtualized Instances

Oracle's MI300X and MI355X offerings are both bare metal: you get the entire physical node, no hypervisor overhead, no noisy-neighbor risk from other tenants. Azure's ND MI300X v5 and Vultr's MI355X pods are virtualized, sharing the underlying hardware layer with other customers even when you're billed for the whole node. Bare metal costs more to provision and operate, and hyperscalers price that into the rate. It's not purely a markup; you're paying for isolation.

Reserved and Annual Commitments vs On-Demand

CoreWeave's MI355X drops from $7.20/hr on-demand to $4.25/hr with a 1-year reservation, a 41% cut. Cirrascale skips on-demand billing entirely and requires a $22,499/month minimum commitment to get its $3.85/GPU-hr MI300X rate. Both are the same trade: you give up the ability to walk away in exchange for a lower rate. That math only works if you know your workload will run continuously for the length of the commitment. If your usage is bursty or you're still validating a model architecture, on-demand or spot pricing is worth the premium for the flexibility.

Neocloud vs Hyperscaler Markup

The clearest pattern in both tables above: specialist neoclouds (TensorWave, Vultr, Crusoe) consistently undercut hyperscalers (Oracle, Azure) by 2-4x on the same hardware. Hyperscalers carry enterprise support contracts, compliance certifications, and integration with their broader cloud stack (IAM, VPC peering, managed storage) that neoclouds don't offer. If you need that, the markup buys something real. If you just need GPU-hours for a training or inference job, you're paying for infrastructure you won't touch.

When MI300X/MI355X Beats an H100 or H200 on Cost Per Token

The short answer: AMD tends to win when your model is large enough that memory capacity, not raw throughput, is the bottleneck. Market rates put MI300X on-demand pricing roughly 15-40% below H100 SXM5 pricing for comparable inference throughput. That gap alone often decides it, but the memory story is what makes it decisive for large models.

The Memory Advantage: Fewer GPUs Per Model

A single MI300X carries 192 GB of HBM3, enough to host models in FP16 that would need a 2x H100 (80 GB each) setup on NVIDIA hardware. Cutting a two-GPU node down to one doesn't just save GPU-hours, it removes the NVLink/interconnect complexity and the multi-GPU serving overhead that comes with tensor parallelism. For the sizing math behind where that threshold falls for specific model sizes, see our GPU memory requirements guide.

Here's a worked example that keeps both sides on the same footing: same source, same billing mode, on-demand only, no hyperscaler markup or spot floor on either side. That 16% gap from Thunder Compute's own MI300X-versus-H100 comparison above ($1.85/GPU-hr against $2.19/GPU-hr) survives once throughput enters the picture. ROCm delivers 90-95% of CUDA throughput on standard PyTorch/vLLM inference; at 92% of H100's estimated 19,500 tokens/sec (from our ROCm vs CUDA throughput benchmarks), an MI300X running near 17,900 tokens/sec at $1.85/GPU-hr works out to about $0.029 per million tokens, versus about $0.031 per million tokens on the $2.19/GPU-hr H100. That's a modest, single-digit-percent edge, not the multiple you'd get by pairing a spot-market AMD listing against a premium NVIDIA one. Treat the throughput figures as estimates, not measured benchmarks, and rerun the math against your own provider quotes before committing budget.

MI355X posted AMD's strongest MLPerf Inference 6.0 result to date, published April 1, 2026: 92% of NVIDIA B300 throughput in offline mode, 93% in server mode, and it actually beat B300 at 104% in interactive mode. B200 wasn't part of that submission, so B300 is the real comparison point, and a single-digit gap against AMD's toughest NVIDIA competitor is a meaningfully closer result than prior generations posted.

Where H100/H200 Still Win

The memory and price advantages don't hold at every batch size. At batch size 1-4 (low-latency, single-request serving), H100 with TensorRT-LLM holds a 20-30% throughput edge over ROCm. That gap narrows to 5-10% at batch size 64-128 (high-throughput serving with many concurrent users), where PyTorch and vLLM on ROCm close most of the distance. If your workload is latency-critical chat or single-user inference rather than high-concurrency batch serving, H100 or H200 is still the safer default. Our best NVIDIA GPUs for LLMs guide ranks H100, H200, and B200 by use case if you're weighing that trade-off directly.

ROCm Software Readiness Before You Commit

AMD's HIP translation layer converts most CUDA code automatically, and PyTorch, vLLM, and SGLang all carry official ROCm support in 2026. For standard inference workloads, ROCm reaches 90-95% of CUDA throughput on both MI300X and MI355X. The gap widens for anything that depends on TensorRT-LLM or FlashAttention 3, which don't have full ROCm equivalents yet, and for teams running custom CUDA kernels that need manual porting rather than an automatic HIP conversion.

The practical rule: if your stack is PyTorch, vLLM, or SGLang and you're running standard transformer inference, ROCm compatibility is not the blocker it was two years ago. If you're leaning on TensorRT-LLM-specific optimizations or hand-written CUDA kernels, budget engineering time before you move workloads over. Our full ROCm vs CUDA comparison covers the framework compatibility matrix in more detail.

How to Pick a Provider

Run through these four checks before you commit to a rate:

1. Match billing mode to your usage pattern. Spot and marketplace listings ($0.95-$4.80/hr) make sense for interruptible batch jobs and experimentation. On-demand ($1.71-$8.60/hr) is right for anything that needs to run reliably to completion. Annual reservations only pay off once you're confident the workload runs continuously for the full term.
2. Decide if you need bare metal. If you're running multi-tenant-sensitive workloads or need consistent low-level performance, bare metal (Oracle, Cirrascale) is worth the premium. If you just need GPU-hours, a virtualized instance from a neocloud is usually cheaper for the same chip.
3. Confirm the memory tier fits your model. 192 GB (MI300X) covers most models up to roughly 90B parameters at FP16 without multi-GPU sharding. 288 GB (MI355X) pushes that further, but at current pricing it only wins on cost if you shop the low end (Vultr) rather than the bare-metal ceiling (Oracle).
4. Test your actual framework before committing budget. ROCm compatibility varies by framework and kernel. Run your real inference or training script on a short-term rental before signing a monthly or annual commitment.

As of July 2026, Spheron's own catalog doesn't list AMD MI-series GPUs; it's NVIDIA-only, with H100 SXM5 on-demand from $5.01/hr, H200 SXM5 from $5.86/hr, and B200 SXM6 from $7.41/hr, all live-tracked on the pricing page. If your cost-per-token math above points to H100 or H200 rather than AMD, that's where to compare next.

If the math above points you toward NVIDIA rather than AMD for your workload, Spheron runs H100 and H200 on-demand and spot with per-minute billing and no long-term contracts.

Compare live GPU pricing on Spheron →