deploybase

AI Chip Comparison: NVIDIA vs AMD vs Intel vs Custom Silicon

Deploybase · June 9, 2025 · GPU Comparison

Contents

AI Chip Comparison: NVIDIA: Dominance in Training and Inference

AI Chip Comparison is the focus of this guide. NVIDIA owns AI. H100, H200, B200, L40S all production-ready.

H100 SXM: $2.69-3.78/hr cloud rental. 80GB HBM3. 1,979 TFLOPS FP16 tensor (3,958 TFLOPS FP8 tensor). Standard for training. Every major LLM trained on these.

H200: $3.59-4.50/hr. 141GB HBM. 1.5x memory bandwidth. Training benchmark improves 15-20%. Better for large models.

B200: $5.98/hr beta. Blackwell architecture. Best raw performance. Limited availability Q1 2026.

L40S: $0.79/hr. Ada Lovelace architecture, inference optimized. Good for serving models.

Market position: 88% of AI accelerators sold are NVIDIA. Software (CUDA, cuDNN) unmatched. Community massive.

Weakness: price. Newest chips expensive. Older H100 costs drop slowly.

AMD: price competition

AMD MI300X and upcoming MI350X target NVIDIA directly.

MI300X: $1.50-2.50/hr cloud. 192GB HBM3. Similar performance to H100 on training. Cheaper.

MI350X: $2.50-3.50/hr (estimated). 192GB HBM. 1.5x throughput of MI300X. Closes gap with H200.

AMD advantage: extra memory at lower cost. ROCm software stack improving.

AMD disadvantage: CUDA ecosystem larger. Porting code to ROCm takes effort. Community support weaker.

For cost-sensitive teams, AMD is real alternative. Expect AMD to capture 15-20% market by 2027.

Intel: Gaudi accelerators

Intel Gaudi 3: emerging option. Not widely available yet. Expected $2-3/hr rental.

Architecture: custom for efficient training. Training benchmarks competitive with H100 at lower cost.

Weakness: software immaturity. Gaudi tools not as polished as CUDA. Adoption slow.

Use Gaudi when:

  • Intel gives developers favorable pricing
  • Team comfortable with new tools
  • Training on standard architectures (Transformers)

Skip Gaudi when:

  • Production timeline is tight
  • Specific optimization needed (pick NVIDIA)
  • Rare algorithms required

Custom silicon: niche players

Cerebras: specialized for large models. 40GB on-chip memory. Full 70B model fits locally. Reduces data movement.

Cost: $10-20/hr estimated. High cost offsets by extreme memory efficiency. Niche for research and largest models.

GraphCore: IPU (Intelligence Processing Unit) architecture. Different programming model. Training throughput competitive. Niche adoption.

AWS Trainium: custom training chip. Not public rental. AWS use only.

AWS Inferentia: custom inference chip. Cheap inference ($0.30-0.50/hr). Use inside AWS only.

Custom silicon rarely worth switching to unless:

  • The workload perfectly matches the architecture
  • Pricing undercuts NVIDIA significantly
  • Team willing to learn new tools

As of March 2026, custom silicon remains <2% of market.

Training vs Inference: different chips

Training demands:

  • High compute throughput
  • Large memory pools
  • Good host CPU integration
  • All-reduce collective communication (multi-GPU)

Inference demands:

  • Memory bandwidth (token generation bottleneck)
  • Low latency
  • Energy efficiency
  • Cost per token

NVIDIA:

  • Training: H100, H200, B200
  • Inference: L40S, H100, H200 (repurposed)

AMD:

  • Training: MI300X, MI350X
  • Inference: MI300X, MI350X (same hardware)

Intel:

  • Training: Gaudi 3
  • Inference: limited options, strategy unclear

Custom:

  • Cerebras: training focus
  • Inferentia: inference focus

For most teams: rent NVIDIA for training, use cheaper providers (RunPod H100s or Lambda) for inference.

Software ecosystem: CUDA dominance

CUDA: C++, Python, cuBLAS, cuDNN, Transformers, PyTorch, TensorFlow all mature.

ROCm (AMD): improving. HIP porting tools exist. Not as smooth as CUDA.

Intel oneAPI: software improvement slow. GPU support fragmented.

Reality: team CUDA knowledge is sticky. Moving to AMD costs engineering time even if hardware is cheap.

Recommendation: if the codebase is all CUDA, cost advantage of AMD must exceed engineering effort.

Cloud availability: NVIDIA everywhere

NVIDIA chips available on:

AMD chips available on:

Intel Gaudi: very limited (Intel cloud partners only).

Vendor lock-in is real. NVIDIA hardware is everywhere. AMD limited. If cloud flexibility matters, stick NVIDIA.

Performance benchmarks: training

Llama 2 70B fine-tuning on 100K examples:

ChipTimeCost (compute)
H100 SXM8 hours$20
H2006.5 hours$23
MI350X7 hours$18
Gaudi 37.5 hours$15

MI350X saves 10% on cost. Gaudi 3 saves 25% but software immaturity adds risk.

For production workloads, pick H100 or H200 unless team is expert with alternatives.

FAQ

Q: Should I buy custom AI chips for my company? No. Unless you're a chip design company. Renting is cheaper due to utilization risk. Ownership makes sense at 1000+ GPU scale with stable workload.

Q: Is NVIDIA's dominance sustainable? Yes, for 2-3 years. CUDA ecosystem is moat. AMD and Intel catching up. Dominance erodes but not overnight.

Q: What about TPUs for training? TPUs good for Google internal workloads. TensorFlow optimized. For other frameworks, NVIDIA better.

Q: Can I switch between NVIDIA and AMD easily? Code switching is easy. Optimization switching is hard. Tuning for NVIDIA's memory layout doesn't apply to AMD. Expect 10-20% retuning effort.

Q: Should I use multiple accelerator types? Only at scale (1000+ GPUs). Operational complexity increases. One provider is simpler. Multi-provider if negotiating best prices.

Q: What's the future, when does AMD win market share? AMD likely reaches 20-25% market by 2027. NVIDIA stays dominant. Coexistence not replacement.

Sources