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2026 H2 Top AI Chip Selection Guide: From H100 to Rubin, MI400, TPU 8t, TPU 8i

· 8 min read
AI Compute Cards Wiki Editorial
Industry Research Team

2026 H2 is the richest era for the AI compute market: NVIDIA Rubin R200, AMD MI400, Trainium 3, TPU 8t/8i, Ascend 920, and Groq 3 LPX are all in place. This article provides a complete selection tree to help you choose the most suitable product based on model size, training/inference, latency requirements, budget, and region.

Selection Decision Tree​

Start
├─ Task Type?
│   ├─ Training ──────────── [Training Selection]
│   └─ Inference ────────── [Inference Selection]
└─ Region?
    ├─ North America / Europe ──── Full product selection
    ├─ China ────────────── Huawei Ascend series
    └─ AWS Cloud ───────── Trainium / Inferentia

Training Selection​

100B+ LLM Training​

PrioritySolutionPer-Rack Compute100B Model Training Time
1NVIDIA Rubin NVL723.6 EF FP4~1-2 days (300B tokens)
2AWS Trn3 UltraServer (2+)104 PF FP8~3-5 days
3AMD Helios2.88 EF FP4 dense~1-2 days
4Google TPU 8t pod (large pod)590+ EF FP8 dense~several hours (Google internal)

Recommendation:

  • Commercial cloud: NVIDIA Rubin NVL72
  • Cost-sensitive: AWS Trn3 UltraServer
  • Open ecosystem: AMD Helios
  • Google Cloud: TPU 8t pod

70B LLM Training​

SolutionConfigurationPriceRecommended Scenario
NVIDIA H2008-card H200~$264KMainstream
NVIDIA B2008-card B200~$400KHigh-end
NVIDIA B300 Ultra8-card B300~$500KLatest
AMD MI300X8-card MI300X~$120KBest value
AMD MI325X8-card MI325X~$160KHigh memory
Trainium 2trn2.48xlarge × 4~$32/hrAWS customers
Trainium 3trn3 UltraServer~$5MHyperscale

Recommendation:

  • Commercial mainstream: NVIDIA H200 8-card
  • Performance-first: NVIDIA B300 Ultra 8-card
  • Best value: AMD MI300X 8-card
  • AWS Cloud: Trainium 3 UltraServer

7B-13B LLM Training​

SolutionConfigurationPriceRecommended
NVIDIA A100 80GB8-card A100~$160KMainstream
NVIDIA H1008-card H100~$240KHigh-end
NVIDIA RTX 6000 Ada4-8 cards~$27KWorkstation
AMD MI300X8-card MI300X~$120KBest value
Intel Gaudi 38-card Gaudi 3~$80KBudget-sensitive

Recommendation:

  • Commercial mainstream: NVIDIA A100 80GB
  • High-end: NVIDIA H100
  • Workstation: NVIDIA RTX 6000 Ada
  • Best value: AMD MI300X
  • Budget-sensitive: Intel Gaudi 3

1B-3B LLM Training​

SolutionConfigurationRecommended
NVIDIA RTX 4090Single cardLocal
NVIDIA RTX 5090Single cardLocal high-end
NVIDIA A100 40GB4 cardsCommercial
Intel Gaudi 28 cardsBudget
Apple M3 UltraSingle workstationLocal LLM

Inference Selection​

70B+ LLM Inference (Single Card)​

SolutionFP16 70B Fits?ComputeRecommended
NVIDIA B300 Ultra (288 GB)✅ Fits 17 PF FP8Top choice
Google TPU 8i (288 GB HBM)✅ Fits 111 PF FP8Google Cloud
AMD MI400 (432 GB HBM4)✅ Fits 120 PF FP8 dense2026
NVIDIA H200 (141 GB)❌ Needs TP21.0 PF FP8Previous gen
AMD MI325X (256 GB)✅ Fits 12.6 PF FP8Previous gen
NVIDIA Groq 3 LPX (128 GB SRAM/rack)✅ Fits 15.5 PF (rack)Ultra-low latency

Recommendation:

  • Commercial cloud: NVIDIA B300 Ultra or TPU 8i
  • Large memory: AMD MI400 / TPU 8i
  • Ultra-low latency: Groq 3 LPX
  • Best value: AMD MI325X

7B-30B LLM Inference​

SolutionMemoryComputePriceRecommended
NVIDIA L40S48 GB733 TF FP8~$8KGeneral purpose
NVIDIA A100 80GB80 GB624 TOPS INT8~$15KLarge models
NVIDIA H10080 GB4 PF FP8~$30KHigh performance
Google TPU 8i288 GB11 PF FP8Cloud onlyGoogle Cloud
AWS Inferentia 232 GB190 TOPSInf2 instancesAWS
Apple M3 Ultra192 GB80-core GPU~$5KLocal

Recommendation:

  • Commercial cloud: NVIDIA L40S / A100
  • AWS Cloud: Inferentia 2
  • Google Cloud: TPU 8i
  • Local: Apple M3 Ultra

Ultra-Low Latency Inference (Agentic AI)​

SolutionTTFTTPOTPriceRecommended
Groq 3 LPX<20ms<5ms$8-10M/rackTop choice
Groq LPU v1~50ms~10ms$1.8M/rackAlternative
TPU 8i~100ms~15msCloudGoogle Cloud
NVIDIA H200~200ms~30ms$30KGeneral purpose
AWS Inferentia 2~200ms~30msAWS instancesAWS

Recommendation:

  • Agentic AI (1000+ calls/sec): Groq 3 LPX (only choice)
  • Real-time Code Gen: Groq 3 LPX
  • Medium latency needs: TPU 8i / H200

Model Size Quick Reference​

Model SizeSingle Card Fits (FP16)Recommended TrainingRecommended Inference
1B-3BAny 8GB+ GPURTX 4090 / A100RTX 4090 / L4
7B24 GBA100 40GB × 4L4 / L40S
13B32 GBA100 40GB × 4L4 / L40S
30B64 GBA100 80GB × 4L40S / H100
70B141 GBH200 × 8B300 Ultra single card / TPU 8i
405B800 GBNVL72B300 Ultra × 4 / Rubin R200
1T+2 TBRubin NVL576Rubin R200 × multi-card / LPX coordinated

Budget Quick Reference​

Monthly BudgetRecommended Training ConfigRecommended Inference Config
<$5KRTX 4090 / clusterL4 / T4
$5K-20K8× A100 80GBL40S / H100 single card
$20K-100K8× H100 / MI300XH200 / B200
$100K-500K8× B200 / NVL72B300 Ultra / TPU 8i
$500K-5MRubin NVL72 / HeliosRubin NVL72 / Helios
$5M-50MRubin NVL576 (8+)Groq 3 LPX rack
$50M+Multi-datacenterHybrid solutions

Region Quick Reference​

China Market (Domestic Required)​

ScenarioRecommendationReason
Government/TelecomHuawei Ascend 920Strongest domestic
Internet LLMHuawei Ascend 920 + CloudMatrix 384 UltraSystem-level
Edge AIHuawei Ascend 310Domestic
National-level AIHuawei CloudMatrix 384 UltraSingle system 345 PFLOPS

North America / Europe (Free Choice)​

PriorityVendorReason
1NVIDIAMature ecosystem, strongest performance
2AMDBest value, open ecosystem
3AWSAWS Cloud only
4GoogleGoogle Cloud only

AWS Cloud (AWS Ecosystem Only)​

ScenarioRecommendation
TrainingTrainium 3 UltraServer (3nm, 4.4×)
InferenceInferentia 2 (affordable)
General purposeNVIDIA H100 (p5.48xlarge)

Google Cloud (Google Ecosystem Only)​

ScenarioRecommendation
TrainingTPU 8t pod (9,216 chip)
InferenceTPU 8i (288GB HBM)
General purposeNVIDIA H100 / A100

Latency Quick Reference​

Latency RequirementTrainingInference
>1sAny solutionAny solution
100ms-1sAny solutionNVIDIA H200 / TPU 8i
50-100ms—TPU 8i / H200 NVL
20-50ms—Groq 3 LPX
<20ms—Groq 3 LPX rack

2026 H2 Selection Quick Reference​

NeedRecommended SolutionAlternative
Trillion-parameter LLM trainingNVIDIA Rubin NVL72AMD Helios
700B LLM trainingAMD Helios (open) or NVIDIA Rubin NVL72Trainium 3
70B LLM inference (single card)NVIDIA B300 UltraTPU 8i / MI400
70B LLM trainingNVIDIA H200 / B200AMD MI300X / MI325X
7B-13B LLM trainingNVIDIA A100 / H100AMD MI300X / Gaudi 3
Local 7B LLMNVIDIA RTX 4090 / 5090Apple M3 Ultra
Ultra-low latency LLM inferenceGroq 3 LPXTPU 8i
Agentic AIGroq 3 LPX rackOnly choice
China marketHuawei Ascend 920Ascend 910C
AWS CloudTrainium 3NVIDIA H100
Google CloudTPU 8t (training) + 8i (inference)NVIDIA H100
Robotics / Physical AIJetson AGX Thor T5000Jetson Orin
Industrial edgeJetson AGX Orin 64GBHailo-15
Best value deep learningAMD MI300XIntel Gaudi 3
Intel ecosystem retentionIntel Jaguar Shores (2027-2028)Gaudi 3
Ultra-low latency AIGroq 3 LPX (256 LPU)Only

Detailed Product Page Index​

Training GPUs​

Training ASICs​

Inference GPUs​

Inference ASICs​

Wafer-Scale​

Others​

Summary​

2026 H2 selection core principles:

  1. Training + Inference = Same chip? In most scenarios, use NVIDIA B300 Ultra / H200 to handle both.
  2. Ultra-low latency inference? Choose Groq 3 LPX, no alternative.
  3. AWS Cloud? Choose Trainium 3, 2-3× performance per dollar.
  4. Google Cloud? Choose TPU 8t (training) + TPU 8i (inference).
  5. China market? Huawei Ascend 920 + CloudMatrix 384 Ultra.
  6. Open ecosystem? AMD Helios (UALoF open interconnect).
  7. Budget-sensitive? AMD MI300X or Intel Gaudi 3.
  8. Local LLM? Apple M3 Ultra (192GB UMA).

There is no best, only the most suitable. Consider your model size, latency requirements, budget, and region, and refer to the selection tree and quick reference tables in this article.