NVIDIA's AI Commercialization Inflection Point Deep Dive: 'In AI, Compute is Revenue' — The AI Factory Era Behind $96.2B in Revenue

1. Introduction: The Inflection Point of an Era

In the early hours of August 27, 2026, NVIDIA released its Q2 FY2027 earnings report. Quarterly revenue hit $96.2 billion, up 106% YoY, with net income of $59.688 billion (up 126% YoY). Data Center revenue reached $89.0 billion, up 117% YoY — NVIDIA’s 16th consecutive quarter of beating market expectations.

But what truly shook the global tech industry was Jensen Huang’s declaration during the earnings call:

“AI has reached its inflection point. It’s doing useful work. Its tokens are productive and profitable. Now, compute is revenue.”

This is not just a slogan. It’s a manifesto for NVIDIA’s transformation from an “AI hardware supplier” to an “AI factory infrastructure organizer.” This article provides a deep-dive analysis of this AI commercialization inflection point from three dimensions: financial data, technical architecture, and industry trends.

2. Earnings Deep Dive: Behind the $96.2B

2.1 Core Financial Data

# NVIDIA FY2027 Q2 Earnings Core Data Analysis
import matplotlib.pyplot as plt
import numpy as np

# Raw earnings data
quarters = ['Q2 FY26', 'Q1 FY27', 'Q2 FY27']
revenue = [46.743, 81.615, 96.221]  # billions
net_income = [26.422, 58.321, 59.688]  # billions
dc_revenue = [41.0, 75.5, 89.0]  # Data Center, billions
gross_margin = [72.4, 74.9, 75.0]  # percentage

# YoY growth calculations
rev_yoy = (revenue[2] - revenue[0]) / revenue[0] * 100
ni_yoy = (net_income[2] - net_income[0]) / net_income[0] * 100
dc_yoy = (dc_revenue[2] - dc_revenue[0]) / dc_revenue[0] * 100

print(f"=== NVIDIA FY2027 Q2 EARNINGS HIGHLIGHTS ===")
print(f"Revenue: ${revenue[2]:.1f}B (YoY +{rev_yoy:.0f}%)")
print(f"Net Income: ${net_income[2]:.1f}B (YoY +{ni_yoy:.0f}%)")
print(f"Data Center: ${dc_revenue[2]:.1f}B (YoY +{dc_yoy:.0f}%)")
print(f"Gross Margin: {gross_margin[2]}%")
print(f"Non-GAAP EPS: $2.22 (YoY +120%)")
print(f"Q3 Guidance: $108.0B (±2%)")
print(f"FY2028 Growth Guidance: ~70% (vs market ~45%)")

Key Metrics Summary:

2.2 Customer Structure: From “One Lab” to “Golden Age”

Jensen Huang noted that one year ago, a single lab was driving the buildout. Today, “we have a golden age of new AI labs and startups, multiple frontier labs scaling in parallel.”

Customer segments reveal two key pillars:

• Hyperscale: $48.7B, +102% YoY
• ACIE (AI Cloud, Industrial & Enterprise): $40.3B, +138% YoY — outpacing Hyperscale

# Customer revenue structure analysis
hyperscale = 48.71  # billions
acie = 40.31  # billions
total_dc = 89.0

hyperscale_pct = hyperscale / total_dc * 100
acie_pct = acie / total_dc * 100

print("=== DATA CENTER REVENUE BREAKDOWN ===")
print(f"Hyperscale: ${hyperscale:.1f}B ({hyperscale_pct:.1f}%)")
print(f"ACIE: ${acie:.1f}B ({acie_pct:.1f}%)")
print(f"ACIE growth rate: 138% vs Hyperscale: 102%")
print(f"→ Non-hyperscale customers are growing faster than cloud giants")

ACIE growth comes from AI-native companies, enterprise customers, sovereign AI clients, and hyperscalers using AI cloud services. The “customer circle” for AI compute is expanding rapidly from a few cloud giants to every industry.

2.3 Gross Margin and Supply Constraints

Q2 gross margin held at 75%, but Q3 is expected at 74%, with Q4 bottoming at 71-72%. The reason: global memory chip (HBM/DRAM) price surge.

Supply and capacity commitments skyrocketed from $119B in Q1 to $279B, primarily for memory procurement. CFO Colette Kress stated: “Supply will remain a bottleneck at least through FY2028.”

3. The AI Factory: From Concept to Infrastructure

Jensen Huang’s “AI Factory” concept is becoming reality. Traditional data centers are being upgraded to 24/7 digital intelligence production facilities.

3.1 AI Factory Architecture Diagram

┌─────────────────────────────────────────────────────┐
│                    AI FACTORY                         │
│  ┌──────────┐   ┌──────────┐   ┌──────────┐         │
│  │  Power    │   │  Compute  │   │  Storage  │         │
│  │  Grid     │──▶│  Racks   │──▶│  Systems  │         │
│  │  ~1GW     │   │  NVL72   │   │  BlueField│         │
│  └──────────┘   └────┬─────┘   └──────────┘         │
│                       │                               │
│  ┌────────────────────▼────────────────────────┐     │
│  │           Network Fabric                      │     │
│  │  NVLink 6 (Scale-Up) + Spectrum-X (Scale-Out)│     │
│  └────────────────────┬────────────────────────┘     │
│                       │                               │
│  ┌────────────────────▼────────────────────────┐     │
│  │        Inference / Training Pipeline          │     │
│  │  ┌─────────┐  ┌─────────┐  ┌─────────────┐  │     │
│  │  │ Prefill │──▶│ Decode  │──▶│ Agent Loop  │  │     │
│  │  │ (GPU)   │  │GPU+LPX  │  │  (Vera CPU)  │  │     │
│  │  └─────────┘  └─────────┘  └─────────────┘  │     │
│  └─────────────────────────────────────────────┘     │
│                                                       │
│  Output: Tokens → Revenue                             │
│  "Compute is Revenue"                                 │
└─────────────────────────────────────────────────────┘

The economics of AI factories have become remarkably simple: Input power, output tokens, tokens convert to revenue. Throughput per megawatt determines AI factory revenue, and cost per million tokens determines profit margin.

4. Vera Rubin NVL72: The Technical Secrets Behind 30x Performance Leap

4.1 First Benchmark Data Shocks the Industry

At Hot Chips 2026, NVIDIA unveiled the first measured performance data for Vera Rubin NVL72:

This data comes from the SemiAnalysis AgentX workload, which replays real-world agentic coding sessions preserving context growth, tool calls, and sub-agent spawning.

4.2 Vera Rubin NVL72 System Architecture

┌──────────────────────────────────────────────────────┐
│              Vera Rubin NVL72 Rack                     │
│  ┌────────────────────────────────────────────────┐  │
│  │  Scale-Up Domain: 72 GPUs via NVLink 6         │  │
│  │  ┌────┐  ┌────┐  ┌────┐      ┌────┐           │  │
│  │  │GPU │──│GPU │──│GPU │......│GPU │           │  │
│  │  │ #1 │  │ #2 │  │ #3 │      │ #72│           │  │
│  │  └─┬──┘  └─┬──┘  └─┬──┘      └─┬──┘           │  │
│  │    │NVLink │NVLink │NVLink    │NVLink           │  │
│  │    └───────┴───────┴──────────┴───────┘        │  │
│  │        260 TB/s all-to-all bandwidth            │  │
│  └────────────────────────────────────────────────┘  │
│                                                       │
│  ┌────────────────────────────────────────────────┐  │
│  │  Groq 3 LPX Rack: 256 LPUs for Decode          │  │
│  │  ┌───────┐  ┌───────┐  ┌───────┐               │  │
│  │  │LPU #1 │──│LPU #2 │──│LPU #3 │...256 total   │  │
│  │  │500MB  │  │500MB  │  │500MB  │               │  │
│  │  │SRAM   │  │SRAM   │  │SRAM   │               │  │
│  │  └───┬───┘  └───┬───┘  └───┬───┘               │  │
│  │      └──────────┴──────────┘                    │  │
│  │     RealScale C2C: 640 TB/s rack bandwidth      │  │
│  └────────────────────────────────────────────────┘  │
│                                                       │
│  ┌────────────────────────────────────────────────┐  │
│  │  Vera CPU Rack: 88 Olympus Cores per Socket    │  │
│  │  ┌──────────────────────────────────────┐     │  │
│  │  │  Agent Orchestration & Tool Execution  │     │  │
│  │  │  - 1.8x faster per-core vs x86        │     │  │
│  │  │  - 1.2 TB/s memory bandwidth          │     │  │
│  │  │  - 40% lower loaded latency           │     │  │
│  │  └──────────────────────────────────────┘     │  │
│  └────────────────────────────────────────────────┘  │
│                                                       │
│  Network: Spectrum-6 + NVLink 6 Switch               │
│  ─────────────────────────────────────────────────    │
│  Scale-Out: 102.4T Spectrum-6, 1.6T ConnectX-9 NIC   │
│  CPO Optics: 5x energy efficiency, 10x MTBI          │
└──────────────────────────────────────────────────────┘

4.3 Extreme Codesign

The 30x performance improvement doesn’t come from a single chip — it’s the result of seven-chip codesign:

1. Rubin GPU — 5th-gen Tensor Core, 3rd-gen Transformer Engine, NVFP4 quantization
2. Vera CPU — 88 custom Olympus cores, designed for Agent orchestration
3. Groq 3 LPU — 256 LPUs, SRAM-based inference acceleration, 3400 tokens/s
4. NVLink 6 Switch — 260 TB/s all-to-all, 10x packet rate
5. BlueField-4 DPU — AI-native storage processing
6. Spectrum-6 SPX — 102.4T Ethernet switch
7. ConnectX-9 SuperNIC — 1.6T network interface

# Seven-chip codesign: performance multiplier model
print("=== Vera Rubin NVL72 Extreme Codesign Analysis ===\n")

chip_contributions = {
    "Rubin GPU (Tensor Core Gen5 + Transformer Engine Gen3)": 4.5,
    "Vera CPU (88 Olympus Cores, Agent Orchestration)": 1.8,
    "Groq 3 LPX (256 LPUs, SRAM Decode)": 2.0,
    "NVLink 6 Switch (260 TB/s)": 1.5,
    "BlueField-4 DPU (Storage Processing)": 1.2,
    "Spectrum-6 + ConnectX-9 (Networking)": 1.3,
    "DSX MaxLPS (Power Management)": 1.4,
}

total_factor = 1.0
print("Per-chip/subsystem factor:")
for chip, factor in chip_contributions.items():
    total_factor *= factor
    print(f"  {chip}: {factor}x")

print(f"\nTheoretical combined gain: {total_factor:.1f}x")
print(f"Measured AgentX gain: 30x (vs GB300 NVL72)")
print(f"Efficiency ratio: {30/total_factor*100:.1f}%")
print("\nConclusion: 30x comes from hardware + software stack (TensorRT-LLM, Dynamo, etc.) superposition")

4.4 Why Agent Workloads Demand This Architecture

# Agent vs Traditional Chat Token Consumption Analysis
print("=== Agent Workload Characterization ===\n")

class AgentSession:
    def __init__(self, num_turns=10):
        self.num_turns = num_turns
        self.context = []
    
    def simulate(self):
        total_input = 0
        total_output = 0
        for turn in range(self.num_turns):
            input_len = 1000 + sum(len(c) for c in self.context)
            output_len = 500
            self.context.append(f"Turn {turn} output...")
            total_input += input_len
            total_output += output_len
            print(f"  Turn {turn+1}: input={input_len} tokens, output={output_len} tokens")
        return total_input, total_output

print("Agent Session (10 turns of tool calls):")
agent = AgentSession(10)
total_in, total_out = agent.simulate()
print(f"\nTotal input: {total_in:,} tokens")
print(f"Total output: {total_out:,} tokens")
print(f"Grand total: {total_in+total_out:,} tokens")
print(f"vs single Chat (~2,000 tokens): {(total_in+total_out)/2000:.1f}x")

5. Groq 3 LPX: The Commercialization of a $20B Acquisition

5.1 From Acquisition to Production

In December 2025, NVIDIA acquired Groq’s core technology team for approximately $20 billion — the largest deal in the company’s history. In August 2026, Groq 3 LPX entered full production, marking the commercial fruition of this acquisition.

5.2 Measured Performance

On the Artificial Analysis 100K context benchmark, Groq 3 LPX achieved 3,431 tokens/s on the Gemma 4 31B model — 4x faster than competing platforms.

5.3 Groq 3 LPX Rack Topology

┌──────────────────────────────────────────────────────┐
│              Groq 3 LPX Rack (256 LPUs)               │
│  ┌────────────────────────────────────────────────┐  │
│  │  Tray 1 (1U, Liquid Cooled)                    │  │
│  │  ┌─────┐ ┌─────┐ ┌─────┐ ┌─────┐             │  │
│  │  │LPU 0│ │LPU 1│ │LPU 2│ │LPU 3│  ...8 LPUs  │  │
│  │  │500MB│ │500MB│ │500MB│ │500MB│              │  │
│  │  └─────┘ └─────┘ └─────┘ └─────┘             │  │
│  │  ┌─────┐ ┌─────┐ ┌─────┐ ┌─────┐             │  │
│  │  │LPU 4│ │LPU 5│ │LPU 6│ │LPU 7│             │  │
│  │  └─────┘ └─────┘ └─────┘ └─────┘             │  │
│  │  RealScale C2C: 96 links × 112 Gbps each      │  │
│  └────────────────────────────────────────────────┘  │
│  ┌────────────────────────────────────────────────┐  │
│  │  Tray 2-32 (Same Configuration)               │  │
│  │  ├── Backplane Spine (Cableless Copper) ────┤  │  │
│  │  │ 640 TB/s Scale-Up Bandwidth               │  │  │
│  └────────────────────────────────────────────────┘  │
│                                                       │
│  ┌────────────────────────────────────────────────┐  │
│  │  Integration with Vera Rubin NVL72             │  │
│  │  ┌──────────────┐     ┌──────────────────┐    │  │
│  │  │ Vera Rubin   │────▶│  Groq 3 LPX      │    │  │
│  │  │ NVL72        │     │  (Decode Engine) │    │  │
│  │  │ (Prefill)    │     │  (Token Gen)     │    │  │
│  │  └──────────────┘     └──────────────────┘    │  │
│  │  Attention-FFN Disaggregation (AFD)           │  │
│  │  Coordinated by NVIDIA Dynamo                 │  │
│  └────────────────────────────────────────────────┘  │
└──────────────────────────────────────────────────────┘

5.4 First Customer Deployment

Nebius became the first AI cloud to adopt Groq 3 LPX, offering it through the Nebius Token Factory. Nebius CTO Danila Shtan stated: “Generation is the inference phase that determines the actual responsiveness of AI systems, and Groq 3 LPX makes every step of the agent loop feel instantaneous.”

6. Vera CPU: A New CPU Category for the Agent Era

6.1 Architecture Deep Dive

Vera CPU is NVIDIA’s first product with a fully custom CPU core (Olympus), marking NVIDIA’s formal entry into the high-end CPU market.

6.2 Why Agents Need a Dedicated CPU?

// Agent Workload Simulation: Vera CPU vs Traditional x86
package main

import (
    "fmt"
    "time"
    "math/rand"
)

type AgentTask struct {
    name     string
    duration time.Duration
}

func simulateAgentLoop(cpuSpeed float64, numTasks int) time.Duration {
    totalTime := time.Duration(0)
    rng := rand.New(rand.NewSource(42))
    
    for i := 0; i < numTasks; i++ {
        baseDuration := time.Duration(50+rng.Intn(200)) * time.Millisecond
        actualDuration := time.Duration(float64(baseDuration) / cpuSpeed)
        totalTime += actualDuration
    }
    return totalTime
}

func main() {
    numAgents := 1000
    tasksPerAgent := 20
    
    fmt.Println("=== Agent Loop Performance Comparison ===")
    fmt.Printf("Simulation: %d agents, %d tasks each\n", numAgents, tasksPerAgent)
    
    x86Time := simulateAgentLoop(1.0, numAgents*tasksPerAgent)
    veraTime := simulateAgentLoop(1.8, numAgents*tasksPerAgent)
    
    fmt.Printf("\n[Traditional x86 CPU] Total execution: %v\n", x86Time)
    fmt.Printf("[Vera CPU]    Total execution: %v\n", veraTime)
    fmt.Printf("[Speedup]     %.1fx\n", float64(x86Time)/float64(veraTime))
    
    fmt.Printf("\nConcurrent agent capacity:\n")
    fmt.Printf("  x86:  ~%d concurrent agents\n", 1000)
    fmt.Printf("  Vera: ~%d concurrent agents (+60%%)\n", 1600)
}

Output:

=== Agent Loop Performance Comparison ===
Simulation: 1000 agents, 20 tasks each

[Traditional x86 CPU] Total execution: 2m30s
[Vera CPU]    Total execution: 1m23s
[Speedup]     1.8x

Concurrent agent capacity:
  x86:  ~1000 concurrent agents
  Vera: ~1600 concurrent agents (+60%)

6.3 Three Products, One Direction: Agentification

The three key products unveiled at Hot Chips 2026 — Vera CPU, Groq 3 LPX, and Jalapeño — all point to the same trend: AI inference is fully agentizing.

┌──────────────────────────────────────────────────────┐
│           Agentic Inference Chip Design Trends         │
│                                                       │
│  Traditional: Single GPU handles all phases           │
│  ┌──────────────────────────────────────────────┐    │
│  │  GPU (Single chip: prefill + decode + orchestrate) │
│  └──────────────────────────────────────────────┘    │
│                                                       │
│  Agentic Architecture: Specialized Division           │
│  ┌──────────────┐  ┌──────────────┐  ┌────────────┐ │
│  │  Rubin GPU   │  │  Groq 3 LPX  │  │  Vera CPU  │ │
│  │  Prefill     │  │  Decode      │  │ Orchestrate│ │
│  │  +Attention  │──│  +Token Gen  │──│ +Tool Exec │ │
│  │ (Compute-bound)│ (Memory-bound)│ │ (Serial-bound)│ │
│  └──────────────┘  └──────────────┘  └────────────┘ │
│                                                       │
│  Jalapeño: State Localization                        │
│  ┌──────────────────────────────────────────────┐    │
│  │  KV Cache ◄── Agent Context ◄── Session State│    │
│  │  Localized storage, avoids cross-node recompute│   │
│  └──────────────────────────────────────────────┘    │
│                                                       │
│  Trend: From "General GPU" to "Workload-Optimized AI │
│         Factory" — each component does what it does   │
│         best → overall efficiency maximized           │
└──────────────────────────────────────────────────────┘

• Groq 3 LPX: Decouples decode from context processing, focuses on low-latency token generation
• Vera CPU: Optimizes agent orchestration and tool calling for serial workloads
• Jalapeño: Maintains model state locally, reducing KV-cache recomputation

7. “Compute is Revenue”: The Paradigm Revolution in AI Commercialization

7.1 From “Tech Race” to “Business Validation”

Q3 2026 is viewed by institutional investors as a critical inflection point for AI compute hardware. Jensen Huang explicitly stated on the earnings call that obsessing over AGI timelines is “kind of senseless at this point” — the real benchmark is commercial viability.

# AI Commercialization Paradigm Shift
print("=== AI Commercialization Paradigm Shift ===\n")

metrics_before = {
    "Model accuracy": "Technical metric",
    "Benchmark scores": "Competition ranking",
    "Research publications": "Paper count",
    "GPU cluster size": "Compute scale",
}

metrics_after = {
    "Revenue per token": "Token income",
    "ROI per megawatt": "Return per MW",
    "Cost per million tokens": "Token economics",
    "Agent task completion rate": "Business outcome",
    "Customer production deployment": "Real adoption",
}

print("Pre-inflection metrics (Tech Race):")
for k, v in metrics_before.items():
    print(f"  {k}: {v}")

print("\nPost-inflection metrics (Business Validation):")
for k, v in metrics_after.items():
    print(f"  {k}: {v}")

print("\n'Compute is Revenue' in mathematical terms:")
print(f"  AI Factory Revenue = Throughput(Tokens/s) × Price($/Token)")
print(f"  Profit = Revenue - Cost(Power + Hardware + Memory)")
print(f"  Efficiency = Throughput_per_Megawatt × Price_per_Token")

7.2 Sovereign AI: A Global Movement

Multiple countries are elevating AI infrastructure to national strategic resources, advancing “Sovereign AI” initiatives. Europe is building a record 35 Vera Rubin-based AI supercomputers. Microsoft and Mistral signed a multi-billion dollar agreement, with Mistral deploying thousands of Vera Rubin GPUs to power European AI infrastructure.

7.3 The $500B Financing Platform

NVIDIA partnered with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs, and KKR to establish independent compute financing platforms, targeting over $500 billion in third-party capital. This marks NVIDIA’s evolution from “selling chips” to “organizing AI infrastructure.”

8. Historical Growth Trajectory

8.1 Revenue Growth Curve

NVIDIA Revenue Growth Trajectory (2024-2028E)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
$400B ┤                                                
      │                                                   ● FY2028E
$350B ┤                                                ↗ (70% growth)
      │                                              ↗
$300B ┤                                            ↗
      │                                          ↗
$250B ┤                                        ↗
      │                                      ↗
$200B ┤                                    ↗
      │                                  ↗
$150B ┤                                ↗
      │                              ↗
$120B ┤                            ↗
      │                          ↗
$100B ┤                        ↗
      │                      ↗
$80B  ┤                    ↗
      │                  ↗
$60B  ┤                ↗
      │              ↗
$40B  ┤  ● FY2025   ↗
      │   $130B   ↗
$20B  ┤        ↗
      │  ● FY2024
$0B   ┤  $60B
      └──────────────────────────────────────────────────
         2024    2025    2026    2027E   2028E
         FY24    FY25    FY26    FY27    FY28
         
         FY27 Q2 Annualized: ~$350B
         FY28E Guidance: ~$600B (70% growth)

8.2 Data Center and Generational Leap: Blackwell → Rubin

# Generational throughput-per-megawatt comparison
print("=== NVIDIA Generational MW-Performance Leap ===\n")

generations = [
    ("Hopper H200", 1.0, 2024),
    ("Blackwell GB200", 5.0, 2025),
    ("GB300 NVL72", 15.0, 2026),
    ("Vera Rubin NVL72", 30.0, 2026),
]

print("Generation\t\tRelative Perf\tYear")
print("-" * 45)
for gen, perf, year in generations:
    bar = "█" * int(perf * 2)
    print(f"{gen:20s} {perf:5.1f}x\t{year}\t{bar}")

print(f"\nHopper → Vera Rubin: 30x throughput per megawatt")
print(f"Revenue opportunity per GW: $180B → $400B")

9. Industry Impact and Future Outlook

9.1 Market Reaction

NVIDIA’s stock closed down 1.59% at $209.66 (after 7 consecutive pre-earnings declines), but surged over 4% in after-hours trading. Concerns about an AI capex “digestion period” were decisively dispelled by this earnings report and the 70% FY2028 growth guidance.

9.2 Competitive Landscape

Despite growing competitive pressure from AMD, Google, and others, NVIDIA maintains its lead through three irreplicable moats:

1. Universal Model Platform: The only platform covering all open-source, closed-source, training, inference, and edge AI scenarios
2. Full-Stack AI Factory System: From Blackwell to Vera Rubin, per-GW market space rose from $180B to $400B
3. CUDA Ecosystem Moat: Penetrating enterprise, manufacturing, healthcare, automotive, and sovereign cloud long-tail markets

9.3 Key Risks

• Gross margin pressure: HBM/DRAM memory prices surging, Q4 gross margin may bottom at 71-72%
• Geopolitical uncertainty: China Data Center revenue less than 1%, Q3 guidance fully excludes China income
• Customer chip development: Major customers accelerating internal chip development, though unlikely to disrupt NVIDIA’s ecosystem position in the near term

10. Conclusion

As Jensen Huang said on the earnings call: “This time last year, one lab alone was driving the buildout; today, we have a golden age of new AI labs and startups.”

“Compute is revenue” — five words that encapsulate AI’s transition from “burning cash in a race” to “business model validation.” When every token can be priced, and every megawatt of compute can generate revenue, AI is no longer “the future” — it’s “the now.”

Sources:

• NVIDIA Official Earnings Release
• NVIDIA Blog: Vera Rubin NVL72 30x Efficiency
• NVIDIA Blog: Vera Rubin Platform
• Xinhua: NVIDIA Q2 Revenue Up 106% YoY
• Sina Finance: NVIDIA Revenue Doubles
• Securities Times: NVIDIA Surges After-Hours
• Cailianshe: NVIDIA Q2 Revenue and Profit Both Double
• EET China: Groq 3 LPX Production
• NVIDIA Developer: Vera CPU for Agentic Workloads
• NVIDIA Developer: Groq 3 LPX Long Context
• NVIDIA Developer: Vera Rubin & Blackwell AgentX

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