2026 GPU Showdown: H100 / H200 vs AMD MI300X vs GB200 – Complete Benchmark, Pricing & ROI Comparison

The MI300X wins convincingly in three specific scenarios:

1. Long-context inference (≥32K tokens): The 5.3 TB/s memory bandwidth processes KV-cache reads proportionally faster than H100’s 3.35 TB/s. At 128K context length, the MI300X’s throughput advantage expands to ~2.1× versus the H100 (per MLPerf Inference v4.1, long-context track). This is the single workload where MI300X outperforms every other single-GPU option by the widest margin.
2. Models fitting in a single 192 GB card: Running Llama-3.1 70B, Mixtral 8×22B, or Qwen2-72B in FP16 on one MI300X eliminates multi-GPU tensor parallel latency entirely. H100 requires 2-card TP for 70B FP16, doubling NVLink traffic. H200 at 141 GB fits 70B FP16 weights but leaves minimal KV-cache headroom compared to MI300X’s 192 GB, making MI300X the better choice for long-context 70B deployments.
3. Cost-sensitive batch inference: The MI300X’s memory capacity and bandwidth make it strong on cost per million output tokens for latency-tolerant pipelines, though we cannot put a figure on that here: none of the providers in this comparison publishes an MI300X rental rate, so there is no list price to divide by. AMD ecosystem lock-in is real, and teams should factor ROCm migration costs before committing to MI300X widely. Compare MI300X vs H100 pricing on GYGO →

The H100 retakes the lead when your workload is CUDA-kernel-heavy (custom sparse attention, Triton extensions), when you need the absolute lowest p99 latency for real-time inference, or when your team cannot dedicate engineer time to ROCm porting. The full CUDA ecosystem advantage is not theoretical — it translates to measurable iteration speed. Not worth switching to MI300X if your primary workload is custom CUDA kernel development — the ROCm porting cost measured in engineer-weeks typically exceeds the rental savings for teams with active kernel codebases.

The spread between cloud on-demand and GYGO colocation TCO is not a marketing estimate — it reflects the fundamental economics of amortized hardware versus pay-as-you-go infrastructure. Cloud providers build in margin, power cost markup, staff, and return on their own capex. When you colocate owned hardware, you bypass every layer of that markup.

At RunPod’s $5.29/hr against the H100 SXM at $3.99, the H200 rental premium only pays off above 70% GPU utilization on memory-bandwidth-bound workloads. For teams running mixed or moderate utilization, the H100 delivers better TCO despite lower raw throughput. Not worth the H200 premium for batch sizes under 16 — the memory bandwidth advantage disappears at small batch, erasing the performance justification for the higher hourly rate.

The MI300X is the one card here we cannot make a cost argument about in either direction. No provider in this comparison publishes an hourly rate for it, and no named reseller publishes a purchase price — every channel we checked quotes rather than lists. The most recent traceable figure is an analyst estimate from early 2024 that AMD declined to confirm, which is too old and too thin to build a 2026 amortization model on. Treat the MI300X as a technical choice you validate on your own workload, and get a quote before you assume it is cheaper. The buying case rests on its 192 GB of memory, not on a discount anyone has published. See full ROI model on the Invest page →

For published list rates across the providers compared here, use GYGO’s rental rate comparison and request quotes through one form.

GPU selection is inseparable from facility selection. The wrong GPU in the wrong data center can negate every theoretical performance advantage through thermal throttling, power-cap enforcement, or network congestion. This section gives you the numbers you need before signing a colocation contract.

kW per Rack – 2026 Reality Check

DLC vs Air Cooling: The Cost Delta

Direct Liquid Cooling (DLC) is no longer exotic — it is a prerequisite for any GB200 deployment and strongly recommended for dense H100/MI300X racks above 20 kW. The GB200 NVL72 draws ~120 kW per rack vs 8× H100’s ~10.5 kW, an 11× jump in power density that makes air cooling physically impossible. The cooling economics fall out of PUE. At the 1.1–1.2 a DLC facility runs, a 40 kW rack carries 4–8 kW of cooling overhead, against 20–24 kW at the 1.5–1.6 typical of CRAC-unit air cooling, so liquid cuts cooling energy by 60–80%. Over a 730-hour month at $0.07/kWh that is $204–$409/month on liquid against $1,022–$1,226/month on air. Avoid air-cooled facilities for GB200. The 120 kW rack draw is incompatible with any air-cooling solution.

DLC requires upfront facility capital: manifold piping, coolant distribution units (CDUs), leak detection, and in a brownfield retrofit the secondary-loop piping, installation labour and commissioning on top. One rack vendor’s 2026 sizing guide (Rax Data & Energy, 13 August 2026) puts CDU hardware at $150–$400 per kW of cooling capacity and the total installed cost at $230–$630 per kW once piping, controls and commissioning are added; treat that as one vendor’s range, not a quote. What the published rate guides say is how it reaches a colocation customer. Inflect (1 April 2026) reports that “The monthly $/kW rate for liquid-cooled and air-cooled racks is generally the same. Where liquid cooling adds cost is in the non-recurring charge (NRC)”, so at a site already fitted for liquid the $/kW is the air-cooled rate and the retrofit shows up as a one-time charge. QuoteColo’s 2026 liquid-cooled guide instead prices liquid-ready 20–40 kW racks at an effective $130–$400/kW/month across the primary markets, a 5–30% premium over air depending on the metro. Neither guide prices 100 kW+ positions separately, so for a GB200 rack treat those figures as the floor and get the facility’s quote. Inflect also notes that long-term contracts (three to five years) often yield 10–20% discounts against month-to-month terms.

For H100 and MI300X 8-way deployments, rear-door heat exchangers (RDHx) are a practical middle path: they bolt onto standard 42U racks, handle 15–25 kW, and require no internal facility plumbing modifications beyond a chilled water loop. Facilities that offer RDHx-ready racks at standard rack rates are the most cost-efficient option for most teams.

The GB200 NVL72 is different. Its 120 kW rack draw is simply incompatible with any air-cooling solution. You need a facility with direct liquid cooling whose water supply can meet the rack’s CDU spec. NVIDIA’s CEO described coolant entering an NVL72 at about 25°C and leaving roughly 20 degrees warmer at GTC 2024 (as reported by The Register), which at common cold-plate design flow of 1.5 L/min per kW puts the secondary loop somewhere around 120–180 L/min per rack. Ask the facility for its own numbers rather than assuming these. Sites that can do it stay scarce: 82% of operators in Uptime Institute’s 2025 survey reported no rack above 30 kW anywhere in their facility. GYGO sources qualified sites for your deployment. Find DLC-ready colocation facilities on GYGO →

DLC vs Air Cooling: Facility-Level Cost Examples

To make the DLC cost delta concrete, consider two representative facility configurations that appear frequently in GYGO placement requests for H100 and MI300X customers.

Scenario A: Air-cooled facility, 10–15 kW/rack cap. A colocation provider in Northern Virginia offering standard air cooling at the published $180–$300/kW/month for a10 kW rack charges $1,800–$3,000/month for an 8-way H100 deployment drawing ~10.5 kW under load. Add power (750 kWh x 30 days x $0.07 = $1,575/month in power draw cost) and a basic cross-connect for InfiniBand at $100–$400/month (QuoteColo’s published 2026 range), and the facility overhead sits at $3,475–$4,975/month. This tier works well for teams whose H100 or MI300X clusters remain within the 15 kW ceiling and who prioritize contract flexibility over cooling efficiency.

Scenario B: DLC-capable facility, 40–120 kW/rack support. A facility in Dallas with liquid-ready racks publishes an effective $160–$280/kW/month including the liquid premium (QuoteColo, 20–40 kW racks), and the cooling power overhead drops from a typical PUE of 1.5–1.6 (air) to 1.1–1.2 (liquid), reducing effective power consumption by 25–30%. For a GB200 NVL72 rack drawing 120 kW, that PUE improvement cuts facility overhead by roughly 48 kWh per hour of operation. At $0.07/kWh that is about $2,450/month in pure power savings. Over the multi-year terms that high-density contracts usually run, the economics favor DLC strongly for any commitment exceeding 18 months with sustained high utilization.

The practical takeaway: if your GPU deployment will run at more than 70% utilization for 18+ months, DLC-capable facilities deliver lower total infrastructure cost despite their higher listed rack rates. Below that utilization threshold or time horizon, standard air-cooled facilities with shorter minimum terms provide better capital efficiency. Ask prospective colo facilities for their actual PUE measurement (not their advertised design PUE), and verify whether DLC rack rates include CDU maintenance or bill it separately.

Interconnect & Network Requirements

For training clusters beyond 8 GPUs, network topology matters as much as GPU choice. H100 and MI300X clusters require InfiniBand HDR (200 Gbps) or NDR (400 Gbps) for full all-reduce performance. Facilities that offer only 25GbE/100GbE Ethernet will bottleneck distributed training by 30–60% regardless of GPU capability. Confirm your prospective colo site’s InfiniBand switch inventory before committing — or use GYGO’s placement filter for InfiniBand-ready facilities.

The buy-vs-rent decision is a pure discounted cash flow problem. The break-even point is the month at which cumulative rental spend exceeds total ownership cost (hardware + power + colo + maintenance). At that inflection, owned hardware begins generating positive ROI versus continued cloud rental.

We run this analysis on the H100 alone. A break-even figure needs a purchase price and a rental rate, and the H100 is the only card in this guide where both are published: an 8-GPU server listed at $289,000 on 13 October 2026, against six providers publishing hourly rates. For MI300X and GB200 neither half exists in public — no reseller lists a price and no provider in this comparison publishes an hourly rate — so any break-even month we printed for them would be arithmetic performed on guesses.

Worked Example: 8× H100 — 12 Months Cloud vs Colocation

Abstract break-even tables can obscure the real magnitude of the savings. Here is a concrete example for a team running an 8-card H100 cluster at 80% average utilization over 12 months, comparing cloud rental against owned hardware in colocation.

Cloud rental path (8× H100 80GB SXM, on-demand list): At RunPod Secure Cloud’s $3.99/hr per card, 8 cards at 80% utilization across a 30-day month consume 8 × $3.99 × 24 × 30 × 0.80 = $18,386/month. At the $6.52 median list rate across the six providers, the same cluster costs $30,044/month. Over 12 months that is $220,631 or $360,530 depending on where you buy. There is no residual asset at month 12 — the compute stops when the billing stops.

Owned hardware, colocation path: A complete 8-GPU H100 SXM5 server — a Supermicro SYS-821GE-TNHR — was listed at $289,000 by the reseller Vipera on 13 October 2026, with RackmountNet and Corgitech listing the same build within 4% the same day. Colocation for a ~10–11 kW rack in a Tier-3 air-cooled facility runs $5,000–5,500/month fully loaded (rack fee, power draw, cross-connect, and remote hands allowance). Call it $5,250. Over 12 months the facility cost totals $63,000, so the all-in 12-month expenditure is $352,000.

Where the crossover lands depends entirely on which rate you rent at, and the gap is wide enough to flip the decision. Owning costs $289,000 up front plus $5,250/month, so the break-even month solves as $289,000 divided by the monthly rental saving. Against the $6.52 median that saving is $30,044 − $5,250 = $24,794/month, and ownership pays back in about 12 months. Against RunPod’s $3.99 the saving falls to $18,386 − $5,250 = $13,136/month, pushing break-even out to about 22 months. Residual secondary-market value pulls both numbers in further, in the buyer’s favour: a three-year-old H100 still resells for roughly 60% of its original price.

The lesson: shop the rate before you shop the model. Renting at the median list rate makes ownership pay back inside a year; renting at the cheapest published rate roughly doubles that. Owned colocation wins comfortably for commitments of 24+ months either way, particularly once secondary-market hardware value is factored in. Teams with 18-month or longer GPU horizons should evaluate the purchase path seriously, not just for cost but for operational independence. On the H200 question, the hardware premium is smaller than it looks: Supermicro’s starting price for the same chassis with 8x H200 is $323,603 against the $289,000 H100 server, a 12% step up on the system. That is not enough of a gap to decide on price alone, so decide it on whether your workload is memory-bandwidth-bound at sustained high utilization.

Staking & Leasing Yields

For investors who want GPU exposure without operating a cluster, hardware leasing programs provide a middle path. Under a typical managed-leasing arrangement, you purchase the GPU hardware through a partner operator who deploys it in their facility, manages the software stack, and pays you a monthly yield based on utilization.

We are not going to quote you a managed-leasing yield, because no operator publishes one you could check. The figures that circulate for this arrangement are quoted gross, at utilisation rates nobody discloses, before the management fee comes out. Build the number yourself from the two halves you can verify: the hourly rate the hardware earns, which is in the pricing table above, and the purchase price in the spec table. Then subtract the operator’s fee, power, and the 30–40% the card loses in its first year.

The risks are the same whoever runs the hardware: depreciation, since each new generation compresses what the previous one earns, utilisation volatility, and the creditworthiness of the operator holding your asset.

DePIN protocols such as Akash Network, Render Network and io.net offer an alternative for consumer-grade hardware, though the transparency there is thinner than the marketing suggests. Render publishes what clients pay but not what operators earn, io.net publishes no APY at all, and Akash’s staking yield is funded by token emission rather than compute revenue. The GPU staking returns guide works through what each of them actually discloses.

Explore GPU investment programs and managed-leasing options on GYGO →

Reading the matrix from left to right reveals a consistent pattern: the H100 is the safest generalist choice, winning or tying in five of eight scenarios and falling short only in memory-intensive workloads where its 80 GB limit becomes a hard constraint. The MI300X is the specialists’ champion — it leads in four scenarios and ties in two, making it the statistically superior choice if your workload spans multiple rows of the table. The GB200 dominates where it appears but is largely absent from the matrix because its reservation complexity and rack-scale minimum make it irrelevant for most teams evaluating this guide today. Avoid GB200 for any workload that does not require the full NVL72 rack — the minimum deployable unit of 72 GPUs makes it worse than H100 on a per-GPU cost basis for teams that cannot saturate the full system. The RTX 4090’s strong showing in the cost-sensitive rows is a reminder that consumer-grade hardware remains a legitimate production option for teams willing to accept its 24 GB VRAM ceiling and consumer-grade reliability characteristics. Every team will find their primary use case in two or three rows of this table — start there rather than trying to optimize for the entire matrix simultaneously.

If you need to pick one GPU today without deep analysis: teams running mixed training-and-inference workloads on ROCm-compatible frameworks should choose the MI300X for its memory advantage and lower total cost. Teams needing maximum CUDA ecosystem compatibility or the lowest possible p99 inference latency should choose the H100 or H200. Teams planning frontier-model training at the 400B+ parameter scale should start the GB200 reservation conversation now. Avoid H100 if your primary use case is long-context inference at 64K+ tokens — the MI300X outperforms H100 by up to 2.1× at 128K context length and costs 20–40% less per hour on spot markets (per MLPerf Inference v4.1 long-context results).

The RTX 4090 delivers 165 FP8 TFLOPS vs H100’s 989 — roughly 1/6th the raw compute at 1/20th the rental cost. This ratio makes it compelling for tiled workloads where per-unit cost matters more than throughput per card (per NVIDIA Ada Lovelace architecture whitepaper). The RTX 4090 deserves more credit than it receives in enterprise discussions. For teams running 7B-13B fine-tuning pipelines at high volume, a 16-card RTX 4090 cluster at $0.34/hr list delivers comparable throughput to 2 H100 cards at $3.99/hr each — $5.44 against $7.98, so roughly 32% lower cost at the cheapest published H100 rate, and far wider against the $6.52 median. The caveats are VRAM fragmentation risk for models above 24B parameters and the reliability delta of consumer vs enterprise hardware.

Browse GPU purchase options across resellers on GYGO →

Every number in this guide required pulling data from eight provider APIs, three hardware spec sheets, two benchmark databases, and multiple colocation operator rate cards. GYGO automates that entire process and presents the result in one interface. Here is exactly what you get:

GYGO does not manufacture GPUs, operate data centers, or resell cloud compute directly. We are a neutral marketplace layer that aggregates supply across the entire GPU infrastructure market and matches demand through a single interface. That structural neutrality means our comparison data has no incentive to favor any single provider — a significant advantage when every provider you might compare us against has a conflict of interest in their own data.

Browse all of our GPU comparison guides and tools on the resources page.

The 2026 GPU market has reached a rare moment of genuine optionality. For the first time since NVIDIA dominated the AI compute market from 2019–2023, buyers have three meaningfully different options at the top of the performance stack: the H100 with the deepest CUDA software maturity, the MI300X with superior memory economics, and the GB200 with a generational leap in throughput for teams who can commit to rack-scale infrastructure.

The decision framework is simpler than the spec sheet comparison suggests:

• Start with software. If your production stack runs on custom CUDA kernels with no porting budget, choose H100 or H200. End of analysis. Avoid MI300X if your framework stack is CUDA-only — ROCm migration cost often exceeds hardware savings for teams with more than a few custom kernels.
• Then consider memory. If your models fit within 80 GB with comfortable KV-cache headroom, H100 is fine — not worth the MI300X or H200 premium for sub-30B models at short context lengths. If you are running 70B+ models in FP16 or require long contexts, pay for the MI300X’s 192 GB. The H200’s 141 GB HBM3e fits 70B FP16 weights but leaves minimal headroom vs MI300X’s 192 GB for long-context KV-cache.
• Then optimize cost, and expect to do it by quotation. If the software answer is “both CUDA and ROCm work,” the cost comparison between H100 and MI300X cannot be made from public data. No provider in this guide publishes an MI300X rental rate and no named reseller publishes a purchase price, so get a quote for both before you assume either is cheaper. What the MI300X does have on record is comparable or better performance per dollar on memory-bound tasks, per MLPerf Training v3.1, December 2025 submission data — which is a performance argument, not a price one.
• Reserve GB200 if you are planning frontier scale. Reservation lead times are set per contract. Start the conversation now even if your training run is 9–12 months away. Not worth the GB200 commitment for inference-only workloads — the NVL72 rack-scale minimum and 120 kW power requirement deliver no advantage over H200 or MI300X for inference serving at realistic batch sizes.

Whatever you choose, GYGO’s marketplace keeps your options open: compare live H100 vs MI300X rental quotes across eight providers, filter colocation facilities by your exact power density and cooling requirements, and access GPU hardware from partner resellers — all without talking to a sales rep until you are ready.

Already leaning toward owning your hardware? Our Colocation vs Cloud ROI breakdown works through break-even month, monthly savings, and total cost of ownership for an H100 cluster.