Chips & silicon

NVIDIA

• NVIDIA GB200 NVL72 — 13.5 TB HBM3E across the rack, 576 TB/s aggregate. Rentable by the hour from multiple clouds; purchasable at scale.
• NVIDIA GB300 NVL72 — Higher HBM3E capacity than GB200; per-rack figure not consistently published, Not published per rack. Rentable by the hour from multiple clouds; purchasable at scale.
• NVIDIA B200 — 192 GB HBM3E, 8 TB/s. Rentable by the hour from multiple clouds; purchasable at scale.
• NVIDIA H200 — 141 GB HBM3E, 4.8 TB/s. Rentable by the hour from multiple clouds; purchasable at scale.
• NVIDIA H100 — 80 GB HBM3, 3.35 TB/s. Rentable by the hour from multiple clouds; purchasable at scale.
• NVIDIA RTX PRO 6000 Blackwell — 96 GB GDDR7, 1.8 TB/s. You can purchase this outright.
• NVIDIA DGX Spark — 128 GB unified LPDDR5X, 273 GB/s. You can purchase this outright.
• NVIDIA Vera Rubin VR200 NVL144 — HBM4. NVIDIA quotes 20.7 TB per rack at NVL72 scale; the NVL144 CPX configuration is quoted at 100 TB of fast memory per rack., Around 20 TB/s per package on initial shipments, against an original 22 TB/s target the memory suppliers missed. 1.6-1.7 PB/s aggregate per rack.. Rentable by the hour from multiple clouds; purchasable at scale.

AMD

• AMD Instinct MI355X — 288 GB HBM3E, 8 TB/s. Rentable by the hour from multiple clouds; purchasable at scale.
• AMD Instinct MI350X — 288 GB HBM3E, 8 TB/s. Rentable by the hour from multiple clouds; purchasable at scale.
• AMD Instinct MI300X — 192 GB HBM3, 5.3 TB/s. Rentable by the hour from multiple clouds; purchasable at scale.
• AMD Helios (Instinct MI455X) — 31 TB of HBM4 per rack across 72 MI455X GPUs; 432 GB of HBM4 per GPU., 260 TB/s scale-up per rack; 19.6 TB/s per GPU.. Rentable by the hour from multiple clouds; purchasable at scale.

Amazon

• AWS Trainium 3 — 144 GB HBM3e per chip, 4.9 TB/s per chip. Cannot be bought. Exists only inside one cloud, so choosing it is choosing that cloud.
• AWS Trainium 2 — Not published in directly comparable terms, Not published. Cannot be bought. Exists only inside one cloud, so choosing it is choosing that cloud.
• AWS Inferentia 2 — Not published in directly comparable terms, Not published. Cannot be bought. Exists only inside one cloud, so choosing it is choosing that cloud.

Google

• Google TPU v7 (Ironwood) — 192 GB HBM3E per chip, 7.37 TB/s per chip. Cannot be bought. Exists only inside one cloud, so choosing it is choosing that cloud.
• Google TPU v6e (Trillium) — Not published in directly comparable terms, Not published. Cannot be bought. Exists only inside one cloud, so choosing it is choosing that cloud.

Cerebras

• Cerebras WSE-3 — 44 GB on-chip SRAM, 21 PB/s on-chip. Rentable by the hour from multiple clouds; purchasable at scale.

Groq

• Groq LPU — 230 MB SRAM per chip, no HBM, 80 TB/s on-chip. Rentable by the hour from multiple clouds; purchasable at scale.

Huawei

• Huawei Ascend 910C — Not reliably published outside China, Not reliably published. Export controls or regional restrictions govern access.

Intel

• Intel Gaudi 3 — 128 GB HBM2E, 3.7 TB/s. You can purchase this outright.

Meta

• Meta MTIA v2 — Not published, Not published. Not available to anyone outside the company that built it.

Microsoft

• Microsoft Maia 200 — Not published, Not published. Cannot be bought. Exists only inside one cloud, so choosing it is choosing that cloud.

Qualcomm

• Qualcomm Cloud AI 100 Ultra — 128 GB LPDDR, Below HBM parts. You can purchase this outright.

SambaNova

• SambaNova SN40L — 520 MiB SRAM + 64 GiB HBM + up to 1.5 TiB DDR per socket, Over 1 TB/s DDR-to-HBM model streaming. Rentable by the hour from multiple clouds; purchasable at scale.

Tenstorrent

• Tenstorrent Blackhole — GDDR6 rather than HBM, Below HBM parts by design. You can purchase this outright.

Head to head

24 pairs, authored rather than generated. These 26 chips would produce 325 combinations, and that many near-identical spec tables is how a site earns a thin-content judgement. Each of these is a comparison somebody actually makes.

• NVIDIA B200 vs AMD Instinct MI355X — The memory argument, stated cleanly: 192 GB of CUDA against 288 GB of ROCm.
• Google TPU v7 (Ironwood) vs AWS Trainium 3 — Two chips you cannot buy. This is a cloud commitment dressed as a hardware comparison.
• NVIDIA GB200 NVL72 vs Google TPU v7 (Ironwood) — Rack-scale coherent memory against a 9,216-chip inference pod. Different shapes of the same bet.
• NVIDIA H100 vs NVIDIA H200 — The same architecture with 61 GB and 1.45 TB/s more. Whether that matters is decided entirely by your model size.
• AMD Instinct MI300X vs NVIDIA H100 — The generation where AMD first became a real answer for memory-bound inference.
• Intel Gaudi 3 vs NVIDIA H100 — Standard Ethernet and a lower price against the deepest software ecosystem in computing.
• Cerebras WSE-3 vs Groq LPU — Speed against cost. Cerebras is 3-8x faster; Groq is cheaper per million tokens.
• Groq LPU vs NVIDIA H100 — Specialist inference silicon against the general-purpose default.
• AWS Trainium 3 vs AMD Instinct MI355X — The two most credible cost-per-token challengers, one locked to a cloud, one not.
• NVIDIA GB300 NVL72 vs NVIDIA GB200 NVL72 — One generation, and 15-80 kW more per rack. The question is whether your facility can energise it.
• Huawei Ascend 910C vs NVIDIA H100 — A jurisdiction comparison, not a performance one.
• NVIDIA RTX PRO 6000 Blackwell vs NVIDIA DGX Spark — Both buyable, both local. Bandwidth against unified memory capacity.
• SambaNova SN40L vs Groq LPU — Many models on one system against one model served cheaply.
• Qualcomm Cloud AI 100 Ultra vs NVIDIA H200 — 150 W against 700 W. Tokens per watt is a different contest from tokens per second.
• Tenstorrent Blackhole vs NVIDIA RTX PRO 6000 Blackwell — The only fully open stack against the one everything is written for.
• AMD Instinct MI350X vs AMD Instinct MI355X — Same 288 GB, same generation. The question is whether the flagship's extra compute changes your cost per token at all.
• Google TPU v6e (Trillium) vs Google TPU v7 (Ironwood) — One generation apart, and Google publishes far more detail about the newer one — which is itself a signal about how they want them compared.
• AWS Trainium 2 vs AWS Trainium 3 — Region availability against raw capability. On AWS the older part is often the one you can actually get.
• AWS Inferentia 2 vs AWS Trainium 3 — Inference-only and cheap against dense-training-capable. Model support decides this, not FLOPS.
• Microsoft Maia 200 vs Meta MTIA v2 — Two chips nobody outside their owners can touch. They matter as the GPU orders that never get placed.
• Meta MTIA v2 vs NVIDIA B200 — The custom-silicon question in one line: every MTIA Meta deploys is a B200 NVIDIA does not sell.
• NVIDIA Vera Rubin VR200 NVL144 vs AMD Helios (Instinct MI455X) — The 2026 rack-scale fight. Both bet everything on HBM4, and HBM4 is sold out through the year.
• NVIDIA Vera Rubin VR200 NVL144 vs NVIDIA GB300 NVL72 — One generation apart. The real question is not which is faster, it is whether you can get either.
• AMD Helios (Instinct MI455X) vs NVIDIA GB200 NVL72 — 432 GB per GPU of ROCm against the CUDA rack everyone already runs. The memory argument at rack scale.