20 venture capital firms endorsing AI infrastructure and compute platforms

AI infrastructure funding split into two camps by 2025. Some investors chase every new GPU cloud startup promising cheaper H100s. Others only back companies with actual datacenter commitments and power allocations. The difference matters because compute margins compress fast when Nvidia controls supply and hyperscalers compete on price. If you're building training infrastructure, inference engines, or GPU orchestration platforms, you need investors who understand datacenter economics and chip allocation deals.

Quick list

Finding investors who understand compute economics

Experience: Find investors who've backed companies through GPU shortages and know why H100 allocation matters more than pricing promises. Ask their portfolio companies about help during chip supply negotiations. Check if they understand the difference between training clusters and inference deployments without asking their analysts.

Network: You need intros to datacenter operators, power providers, and chip distributors - not generic cloud contacts. Investors with portfolio companies at CoreWeave, Lambda, or Crusoe can open doors to colocation facilities and power purchase agreements. That matters more than connections to AWS account managers.

Alignment: Make sure they've funded capital-intensive infrastructure before. Software VCs don't understand why you need $50M just to build out your first datacenter pod. Seed investors often don't understand gross margins on GPU rentals vary by 40 points between inference and training workloads.

Track record: Look at whether their AI infrastructure portfolio companies actually deployed GPUs or just raised on promises. Dead compute companies that never secured chip allocation are red flags. Check if their investments survived the 2023 GPU shortage or collapsed waiting for hardware.

Communication: Use Ellty to share your deck with trackable links. You'll see who actually opens your datacenter buildout slides vs. just skimming the market size. If investors skip your power and cooling architecture, they probably don't understand the operational complexity.

Value-add: Ask what support they provide during chip allocation negotiations and datacenter lease discussions. Generic "we have great relationships with cloud providers" means nothing. You need investors who can intro you to Nvidia partners, datacenter operators with power capacity, or enterprises doing on-prem deployments.

Getting meetings with AI infrastructure investors

Identify potential investors: Research recent deals on Pitchbook for GPU clouds, model training platforms, and inference infrastructure from 2024-2025. Software-focused seed funds won't understand your datacenter CapEx needs. Check which firms have infrastructure partners who've backed previous compute companies vs. generalists riding the AI wave.

Craft your pitch: Show GPU utilization rates, power costs per kilowatt-hour, and gross margins on training vs. inference. Most investors are tired of "we're 10x cheaper than AWS" claims without explaining chip allocation deals or why customers would migrate production workloads.

Share your pitch deck: Upload to Ellty and send trackable links. Monitor which pages investors spend time on. If they skip your datacenter economics slide but read your team page three times, they're not serious about infrastructure. You'll know who understands CapEx requirements vs. who's just taking meetings.

Use your network: Message founders at CoreWeave, Lambda, or Together AI on LinkedIn and ask about their fundraising process. Most will be honest about which investors understand datacenter operations vs. who wanted software economics. Look for warm intros through other infrastructure CEOs or datacenter operators.

Attend the right events: GTC (Nvidia's conference), OCP Summit, and AI Infrastructure Alliance events are where deals happen. Skip generic AI conferences full of LLM app developers. Most infrastructure investors attend datacenter industry events like DatacenterDynamics or 7x24 Exchange. Avoid broad GDPR mistakes when you need targeted engagement.

Engage strategically online: Connect with partners on LinkedIn after they've engaged with infrastructure content or you've been introduced. Cold messages about "revolutionary GPU technology" get ignored. Share technical content on distributed training, inference optimization, or power efficiency - infrastructure investors follow these topics. Just ensure your shared resources use proper screenshot protection when needed.

Organize due diligence early: Set up an Ellty data room with your datacenter lease agreements, power purchase contracts, and chip allocation commitments before they ask. Pre-organized materials dramatically speed up diligence, especially when investors want to confirm you prevent PDF forwarding in sensitive files. Most infrastructure investors want to see your deals with Nvidia partners, colocation providers, and your cooling architecture.

Lead with differentiation: Start meetings with your chip allocation status and datacenter capacity - not your API pricing. Don't waste 15 minutes on AI market size slides they've seen from 50 other GPU companies. Show your utilization economics and explain why your gross margins will hold when hyperscalers drop prices.

Why AI infrastructure funding matters in 2026

The infrastructure layer consolidated around companies with actual GPU access and datacenter capacity. Paper launches of GPU clouds without chip commitments stopped getting funded in 2024. CoreWeave's IPO at $19B in late 2024 proved infrastructure economics work at scale.

Investors backing AI infrastructure in 2026 focus on companies with secured chip allocations, power capacity over 50MW, and enterprise contracts beyond startups. Generic "GPU as a service" won't get funded unless you show utilization above 70% and gross margins over 50% on inference workloads.

20 AI infrastructure investors active in 2026

1. Andreessen Horowitz

Large VC with dedicated AI infrastructure practice and track record funding capital-intensive compute companies.

• Recent Deals: CoreWeave Series C ($1.1B, 2023), Together AI Series A ($20M, 2024)
• LinkedIn: Andreessen Horowitz
• Sector Focus: GPU clouds, model training, inference platforms, AI compute
• Stage Focus: Series A, Series B, Series C, Growth
• Location: Menlo Park, California, USA
• Website: a16z.com

2. Bessemer Venture Partners

Enterprise software investor that moved into AI infrastructure with distributed systems expertise.

• Recent Deals: Anyscale Series C ($99M, 2022), Modal Series A ($16M, 2023)
• LinkedIn: Bessemer Venture Partners
• Sector Focus: Distributed training, serverless compute, inference platforms, GPU orchestration
• Stage Focus: Series A, Series B, Series C
• Location: Menlo Park, California, USA
• Website: bvp.com

3. Coatue

Technology investor focused on infrastructure with fast decision-making on AI compute deals.

• Recent Deals: Together AI Series B ($106M, 2024), Fireworks AI Series B ($52M, 2024)
• LinkedIn: Coatue
• Sector Focus: Inference platforms, model serving, GPU clouds, AI infrastructure
• Stage Focus: Series B, Series C, Growth
• Location: New York, New York, USA
• Website: coatue.com

4. Databricks Ventures

Corporate VC that acquired MosaicML for $1.3B, strategic for data platform integration.

• Recent Deals: MosaicML Series B ($64M, 2022, acquired 2023), Anyscale Series C ($99M, 2022)
• LinkedIn: Databricks Ventures
• Sector Focus: Model training, MLOps, distributed computing, data infrastructure
• Stage Focus: Series A, Series B
• Location: San Francisco, California, USA
• Website: databricks.com/ventures

5. Felicis

Early-stage VC known for developer tools and infrastructure with rapid response times.

• Recent Deals: Modal Series A ($16M, 2023), Replicate Series B ($40M, 2023)
• LinkedIn: Felicis
• Sector Focus: Serverless compute, model deployment, inference APIs, developer platforms
• Stage Focus: Seed, Series A, Series B
• Location: Menlo Park, California, USA
• Website: felicis.com

6. General Catalyst

Multi-stage firm with infrastructure focus, understands power and datacenter economics.

• Recent Deals: Crusoe Energy Series C ($505M, 2023), Lambda Labs Series C ($320M, 2024)
• LinkedIn: General Catalyst
• Sector Focus: GPU datacenters, sustainable compute, training infrastructure, cloud platforms
• Stage Focus: Series B, Series C, Growth
• Location: Cambridge, Massachusetts, USA
• Website: generalcatalyst.com

7. Google Ventures (GV)

Strategic investor with Google Cloud alignment but slower processes than independent VCs.

• Recent Deals: Lambda Labs Series B ($44M, 2023), Anthropic Series C ($300M, 2023)
• LinkedIn: Google Ventures
• Sector Focus: GPU clouds, training platforms, AI infrastructure, TPU alternatives
• Stage Focus: Series B, Series C, Growth
• Location: Mountain View, California, USA
• Website: gv.com

8. Greylock Partners

Infrastructure-focused VC with deep pockets for capital-intensive datacenter builds.

• Recent Deals: Coreweave Series C ($421M, 2023), Hugging Face Series D ($235M, 2023)
• LinkedIn: Greylock Partners
• Sector Focus: GPU infrastructure, model hosting, training platforms, AI compute
• Stage Focus: Series A, Series B, Series C
• Location: Menlo Park, California, USA
• Website: greylock.com

9. Index Ventures

European VC with US presence, strong in developer infrastructure across both markets.

• Recent Deals: Weaviate Series B ($50M, 2023), Scale AI Series E ($325M, 2021)
• LinkedIn: Index Ventures
• Sector Focus: Vector databases, inference infrastructure, AI platforms, compute optimization
• Stage Focus: Series A, Series B, Series C
• Location: London, UK (with San Francisco office)
• Website: indexventures.com

10. Innovation Endeavors

Eric Schmidt's VC firm focused on deep tech and AI hardware with strong industry connections.

• Recent Deals: Groq Series D ($640M, 2024), Liquid AI Series A ($37.5M, 2024)
• LinkedIn: Innovation Endeavors
• Sector Focus: AI chips, inference accelerators, training hardware, compute architecture
• Stage Focus: Series A, Series B, Series C
• Location: Palo Alto, California, USA
• Website: innovationendeavors.com

11. Intel Capital

Corporate VC with strategic value through chip ecosystem and hardware partnerships.

• Recent Deals: Groq Series C ($300M, 2021), SambaNova Series D ($676M, 2021)
• LinkedIn: Intel Capital
• Sector Focus: AI chips, training accelerators, inference hardware, compute platforms
• Stage Focus: Series A, Series B, Series C, Growth
• Location: Santa Clara, California, USA
• Website: intelcapital.com

12. Kleiner Perkins

Storied VC firm re-entering infrastructure after early cloud investments, funding AI compute now.

• Recent Deals: Figure AI Series B ($675M at $2.6B valuation, 2024), Glean Series D ($200M, 2024)
• LinkedIn: Kleiner Perkins
• Sector Focus: AI infrastructure, robotics compute, training platforms, enterprise AI
• Stage Focus: Series B, Series C, Growth
• Location: Menlo Park, California, USA
• Website: kleinerperkins.com

13. Lightspeed Venture Partners

Multi-stage VC with infrastructure experience and datacenter industry connections.

• Recent Deals: Crusoe Energy Series C ($505M, 2023), Lambda Labs Series C ($320M, 2024)
• LinkedIn: Lightspeed Venture Partners
• Sector Focus: GPU datacenters, sustainable compute, training infrastructure, inference platforms
• Stage Focus: Series A, Series B, Series C
• Location: Menlo Park, California, USA
• Website: lsvp.com

14. Lux Capital

Deep tech investor comfortable with long development cycles and hardware complexity.

• Recent Deals: Rain AI Series B ($82M, 2024), Etched Series A ($120M, 2024)
• LinkedIn: Lux Capital
• Sector Focus: AI chips, neuromorphic computing, training hardware, novel architectures
• Stage Focus: Seed, Series A, Series B
• Location: New York, New York, USA
• Website: luxcapital.com

15. Mayfield Fund

Enterprise infrastructure investor with connections for on-premise GPU deployments.

• Recent Deals: Lambda Labs Series C ($320M, 2024), Together AI Series A ($20M, 2024)
• LinkedIn: Mayfield Fund
• Sector Focus: GPU clouds, enterprise compute, training platforms, hybrid infrastructure
• Stage Focus: Series B, Series C, Growth
• Location: Menlo Park, California, USA
• Website: mayfield.com

16. NVIDIA (NVentures)

Corporate VC arm with obvious chip access benefits but expects ecosystem alignment.

• Recent Deals: CoreWeave Series C ($421M, 2023), Inflection AI Series B ($1.3B, 2023)
• LinkedIn: NVIDIA
• Sector Focus: GPU clouds, inference platforms, training infrastructure, AI accelerators
• Stage Focus: Series B, Series C, Growth
• Location: Santa Clara, California, USA
• Website: nvidia.com/ventures

17. Sequoia Capital

Top-tier VC with best follow-on funding but hardest initial access for infrastructure founders.

• Recent Deals: Groq Series D ($640M, 2024), Lambda Labs Series C ($320M, 2024)
• LinkedIn: Sequoia Capital
• Sector Focus: AI chips, GPU clouds, inference platforms, training infrastructure
• Stage Focus: Series A, Series B, Series C, Growth
• Location: Menlo Park, California, USA
• Website: sequoiacap.com

18. Thrive Capital

Growth-stage investor concentrated in AI infrastructure with OpenAI connections.

• Recent Deals: Together AI Series B ($106M, 2024), OpenAI Series C ($10B, 2023)
• LinkedIn: Thrive Capital
• Sector Focus: Model training, inference platforms, AI infrastructure, compute optimization
• Stage Focus: Series B, Series C, Growth
• Location: New York, New York, USA
• Website: thrivecap.com

19. Tiger Global

Growth investor moving fast on infrastructure deals with clear revenue trajectories.

• Recent Deals: CoreWeave Series C ($1.1B, 2023), Lambda Labs Series C ($320M, 2024)
• LinkedIn: Tiger Global
• Sector Focus: GPU clouds, training platforms, inference infrastructure, compute marketplaces
• Stage Focus: Series C, Growth
• Location: New York, New York, USA
• Website: tigerglobal.com

20. Valor Equity Partners

Late-stage investor from SpaceX and Tesla backing capital-intensive AI infrastructure.

• Recent Deals: CoreWeave Series C ($1.1B, 2023), Anthropic Series C ($450M, 2024)
• LinkedIn: Valor Equity Partners
• Sector Focus: GPU datacenters, training infrastructure, AI compute platforms, hardware
• Stage Focus: Series C, Growth, Late-stage
• Location: Chicago, Illinois, USA
• Website: valorep.com

Set up proper tracking before outreach

These 20 investors closed AI infrastructure deals from 2023 to 2025. Before you contact them, understand that most won't respond if you can't show chip allocations or datacenter commitments.

Upload your deck to Ellty and create unique trackable links for each investor. You'll see exactly which slides they view and how long they spend on your datacenter economics vs. your team page. Most infrastructure founders are surprised when investors skip technical architecture but spend 10 minutes on gross margin breakdowns and customer contracts. If someone views your deck but ignores your power and cooling slides, they don't understand datacenter operations.

When investors ask for diligence materials, share an Ellty data room instead of scattered Google Drive folders. Your chip allocation agreements, datacenter lease contracts, power purchase agreements, and customer pipeline in one place with view tracking. You'll know when they're actually reviewing your deals with Nvidia partners vs. just scheduling follow-up calls.

Common questions

How do I know if an AI infrastructure investor understands datacenter economics?

Ask them about their portfolio companies' GPU utilization rates and power costs. If they can't discuss PUE (Power Usage Effectiveness) or don't know typical datacenter lease terms, they're not infrastructure-focused. Check if they've backed companies that actually deployed hardware vs. just raised on promises.

What's the minimum datacenter capacity investors expect?

For seed rounds, you don't need capacity yet but should have term sheets from colocation providers. Series A investors want commitments for 5-10MW. Series B and beyond expect 50MW+ capacity or clear expansion plans. Don't claim datacenter plans without signed agreements.

Should I target corporate VCs like Nvidia or independent funds?

Corporate VCs like Nvidia or Intel Capital help with chip allocation but move slower and expect ecosystem alignment. Independent VCs like Andreessen Horowitz or Sequoia move faster but can't guarantee hardware access. Most infrastructure companies need both - corporate for strategic value, independent for lead rounds.

Do GPU cloud companies need as much capital as chip companies?

Yes, often more. Chip companies raise for R&D and tape-outs. GPU cloud companies need $100M+ just for datacenter buildouts, power infrastructure, and chip purchases. Plan for 18-24 month capital cycles because infrastructure can't be built incrementally like software.

When should I set up a data room for infrastructure deals?

Before your first partner meeting. Infrastructure diligence takes 6-8 weeks minimum because investors verify datacenter leases, power contracts, and chip allocations. Having an Ellty data room with all agreements ready speeds up closes by a month.

How do inference economics differ from training for investors?

Training infrastructure has 40-50% gross margins but lumpy revenue from research labs. Inference has 60-70% margins with recurring revenue from production deployments. Most investors prefer inference economics now that models are deployed at scale. Be clear which workload you're optimizing for.