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: At $1.50–3/hr spot versus $2–4/hr for H100, the MI300X delivers roughly 40–50% lower cost per million output tokens for latency-tolerant pipelines. MI300X spot pricing at $1.52/hr on Vast.ai undercuts both H100 and H200 options, but AMD ecosystem lock-in is real — teams should factor ROCm migration costs before committing to MI300X at scale. 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 $3.10–3.29/hr on-demand vs H100’s $2.06–2.49/hr, 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 savings widen further for MI300X because the street price discount ($18,000–28,000 vs H100’s $25,000–35,000) accelerates the hardware amortization curve. A fully-loaded MI300X colo deployment can reach positive ROI in 9–12 months versus 14–18 months for H100 at comparable utilization. MI300X spot pricing at $1.52/hr on Vast.ai undercuts both NVIDIA H100 and H200 options — but AMD ecosystem lock-in is real and the secondary-market resale value for MI300X hardware is lower than comparable NVIDIA hardware, which affects long-term amortization calculations. See full ROI model on the Invest page →

For live pricing across all available providers, use GYGO’s real-time rental comparison for the latest GPU pricing comparisons.

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 ~80 kW per rack vs 8× H100’s ~10.5 kW — a 7.6× increase in power density that makes air cooling physically impossible. The economics are counter-intuitive: DLC reduces cooling energy consumption by 30–40% compared to CRAC-unit air cooling because liquid heat exchangers operate at much higher thermal efficiency. A 40 kW DLC rack can be cooled for roughly $180–220/month in power vs $280–350/month for equivalent air cooling. Avoid air-cooled facilities for GB200 — the 80 kW rack draw is incompatible with any air-cooling solution.

However, DLC requires upfront facility capital: manifold piping, coolant distribution units (CDUs), and leak-detection systems add $15,000–40,000 per rack bay in one-time facility cost. For colocation customers, this means paying a DLC-capable facility’s premium rack rates (typically $1,800–3,500/kW/month for Tier-3 DLC-ready space vs $800–1,500/kW/month for standard air). Longer contract terms (24–36 months) typically unlock DLC-enabled colocation rates that make the premium worthwhile for sustained workloads.

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 80 kW rack draw is simply incompatible with any air-cooling solution. You need a DLC-certified facility with a water supply capable of handling the CDU spec (typically 27°C inlet temp, 350 L/min flow rate per rack). Less than ~200 facilities in North America and Europe met this spec as of early 2026. GYGO’s placement team maintains a current list of GB200-certified sites. 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 mid-tier colocation provider in Northern Virginia offering standard air cooling at $900/kW/month for a 10 kW rack delivers an effective power cost of $9,000/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, and the facility overhead sits at roughly $10,800–11,500/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, 40–80 kW/rack support. A Tier-3 hyperscale-adjacent facility in Dallas or Chicago with full DLC infrastructure charges $2,200–2,800/kW/month for DLC-ready space, but 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 80 kW, that PUE improvement saves roughly 16 kWh per hour of operation — at $0.07/kWh, approximately $840/month in pure power savings. Combined with the 5–year contract terms that DLC-capable facilities typically require, 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.

Break-Even Analysis – 8× GPU Cluster

Assumptions: 75% utilization, $0.06/kWh blended power, standard colo rack rates, 0% financing. Results are illustrative; use GYGO’s ROI calculator for custom inputs.

Worked Example: 8× MI300X — 12 Months Cloud vs Colocation

Abstract break-even tables can obscure the real magnitude of the savings. Here is a concrete, month-by-month example for a team running an 8-card MI300X cluster at 80% average utilization over 12 months, comparing cloud rental against owned hardware in colocation.

Cloud rental path (Lambda Labs, on-demand MI300X): At $1.99/hr per card, 8 cards drawing 80% utilization across a 30-day month consume: 8 × $1.99 × 24 × 30 × 0.80 = $9,148/month. Over 12 months, that totals $109,776 in GPU rental cost alone, before accounting for egress fees, storage, or support. There is no residual asset at month 12 — the compute simply stops when the billing stops.

Owned hardware, colocation path: Eight MI300X OAM modules in a purpose-built server cost approximately $180,000 at February 2026 market rates for a complete system. 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). Over 12 months, the facility cost totals roughly $63,000. Combined with the $180,000 hardware purchase, the all-in 12-month expenditure is $243,000.

At first glance, $243,000 versus $109,776 appears to favor cloud by a wide margin. But the comparison changes fundamentally after month 10 — the MI300X break-even point. In months 11 and 12, the owned cluster continues operating at only $5,250/month in facility cost versus $9,148/month in cloud rental. By month 18, the cumulative cloud spend reaches $164,664 while cumulative owned costs plateau at roughly $243,000 + ($5,250 × 6 more months) = $274,500. The owned cluster is still slightly behind. By month 24, the crossover is clear: cloud total is $219,552 while colo total is $306,000 — but the owned hardware carries a residual secondary-market value estimated at $80,000–100,000 for MI300X at 24-month age, bringing the effective ownership cost to $206,000–226,000 versus $219,552 in rental. At month 24, owning is equivalent to or cheaper than renting, with full asset control going forward.

The lesson: cloud rental wins for commitments under 12–14 months. Owned colocation wins for commitments of 18+ months, particularly when 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. Not worth purchasing H200 for investment programs — the hardware premium ($280,000 vs H100’s $240,000 for an 8-card cluster) extends the break-even timeline to ~13 months versus ~14 months for H100, a difference that does not justify the higher capital outlay unless your workload is specifically 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 GYGO-vetted operator who deploys it in their facility, manages the software stack, and pays you a monthly yield based on utilization.

Current managed-leasing yields on H100 hardware run approximately:

• H100 SXM5 (8-card server): $3,200–4,800/month gross yield at 80% utilization, equivalent to a 16–24% annualized return on $240K hardware investment before operator fees.
• MI300X (8-card system): $2,800–4,200/month gross at 80% utilization, or 19–28% annualized on $180K hardware — the higher percentage reflecting the lower entry price.

Net yields after operator management fees (typically 20–30% of gross) range from 11–20% annualized, comparing favorably to most fixed-income alternatives in a high-AI-demand environment. Risks include hardware depreciation (next-generation GPU releases compress yields), utilization volatility, and operator creditworthiness.

DePIN (Decentralized Physical Infrastructure) protocols such as Akash Network, Render Network, and io.net offer an alternative yield mechanism for consumer-grade hardware (RTX 4090 class) with on-chain yield transparency but higher utilization variability.

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 get on the GB200 waitlist 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 $0.10/hr effective cost delivers comparable throughput to 2 H100 cards at $4.50/hr — at roughly 80% lower cost. 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 unmatched 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. If the software answer is “both CUDA and ROCm work,” the MI300X’s 15–25% rental discount and faster break-even make it the financially superior choice for sustained workloads. MI300X at $1.52–1.99/hr spot vs H100 at $1.85–2.49/hr delivers comparable or better performance per dollar for memory-bound tasks (per MLPerf Training v3.1, December 2025 submission data).
• Reserve GB200 if you are planning frontier scale. Waitlist lead times are 3–6 months. 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 80 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 vetted resellers — all without talking to a sales rep until you are ready.

Already leaning toward owning your hardware? Use our Colocation vs Cloud ROI Calculator to model your exact break-even month, monthly savings, and total cost of ownership for H100, MI300X, and A100 clusters.