Best GPUs for
AI Inference in 2026
Compare 15+ GPUs from $500 consumer cards to $50,000+ datacenter accelerators. Real-world benchmarks, TCO analysis, and deployment recommendations for every AI workload.
Quick Reference Guide
Navigate to the right GPU tier for your use case and budget
| Category | Price Range | Best For | Top Picks |
|---|---|---|---|
| Consumer | $500 - $2,600 | Local inference, development, hobbyists | RTX 5090, RTX 4090, RTX 5070 |
| Workstation | $4,000 - $10,000 | Professional deployment, on-prem inference | RTX 6000 Blackwell Pro, L4 |
| Datacenter (Hopper) | $25,000 - $45,000 | Production inference, cloud deployment | H100, H200 |
| Datacenter (Blackwell) | $45,000+ | Enterprise scale, frontier models | B200, B300 |
What Makes a GPU Good for Inference?
AI inference differs fundamentally from training—it's typically memory bandwidth-bound, not compute-bound
Memory Capacity (VRAM)
Your GPU must fit the entire model plus KV cache. Insufficient VRAM causes catastrophic performance degradation through CPU offloading.
Memory Bandwidth
Bandwidth directly determines token generation speed. HBM3e (datacenter) vastly outperforms GDDR6X (consumer). H200's 4.8 TB/s enables ~45% faster inference than H100's 3.35 TB/s.
Tensor Core Generation
5th-gen Tensor Cores (Blackwell) support FP4/FP6 for 2.5x performance gains. 4th-gen (Hopper/Ada) adds FP8 support. 3rd-gen (Ampere) limited to FP16/INT8.
Low-Precision Support
FP8 cuts memory by 50% with ~2x speed. INT4 cuts by 75% with ~4x speed. Modern quantization enables larger models on smaller GPUs with minimal accuracy loss.
Model VRAM Requirements (FP16)
| Model Size | VRAM Needed | Example Models |
|---|---|---|
| 3B parameters | 6-8 GB | Phi-3, Gemma-2B |
| 7B parameters | 14-16 GB | Llama 3.1 7B, Mistral 7B |
| 13B parameters | 26-28 GB | Llama 2 13B, Codellama 13B |
| 30B parameters | 60-64 GB | Codellama 34B |
| 70B parameters | 140-144 GB | Llama 3.1 70B, Deepseek 67B |
| 175B+ parameters | 350+ GB | GPT-class (multi-GPU required) |
With INT4 Quantization
| Model Size | VRAM Needed | Fits On |
|---|---|---|
| 7B parameters | 4-5 GB | RTX 4060 Ti 8GB |
| 13B parameters | 8-10 GB | RTX 4070 / RTX 3090 |
| 30B parameters | 16-20 GB | RTX 4090 / RTX 5090 |
| 70B parameters | 35-45 GB | RTX 6000 Blackwell Pro (96GB) |
Tensor Core Generations
| Generation | Architecture | Key Features |
|---|---|---|
| 3rd Gen | Ampere (A100, RTX 30-series) | FP16, BF16, TF32, INT8 |
| 4th Gen | Hopper (H100, H200) | FP8, Transformer Engine |
| 4th Gen | Ada Lovelace (RTX 40-series) | FP8, improved efficiency |
| 5th Gen | Blackwell (B200, RTX 50-series) | FP4, FP6, 2.5x performance gain |
Consumer GPUs ($500-$2,600)
Exceptional value for local inference, development, and smaller-scale deployment
NVIDIA GeForce RTX 5090
The New King of Consumer AI
Highlights
- 32GB VRAM enables 70B models with aggressive quantization
- GDDR7 provides ~1.8 TB/s bandwidth (near-workstation levels)
- 5th-gen Tensor Cores with FP4/FP6 support
- ~40% faster AI performance vs RTX 4090
NVIDIA GeForce RTX 4090
The Proven Workhorse
Highlights
- Mature software optimization; often outperforms RTX 5090 in current frameworks
- 24GB handles 13B at full precision, 30B+ with quantization
- Excellent price-to-performance, especially used
- ~90-100 tokens/sec on Llama 7B models
NVIDIA GeForce RTX 5070 Ti
Mid-Range Value Champion
Highlights
- 16GB GDDR7 handles 7B-13B models effectively
- 5th-gen Tensor Cores with FP4 support
- 50-100 tokens/sec on 7B models (TensorRT-LLM optimized)
- Excellent power efficiency
Consumer GPU Comparison
| GPU | VRAM | Bandwidth | FP16 TFLOPS | Price | Best Model Size |
|---|---|---|---|---|---|
| RTX 5090 | 32 GB | 1,792 GB/s | ~210 | $1,999 | 70B (quantized) |
| RTX 4090 | 24 GB | 1,008 GB/s | 165 | $1,400-1,800 | 30B (quantized) |
| RTX 5070 Ti | 16 GB | 896 GB/s | ~125 | $749 | 13B (quantized) |
| RTX 5070 (6,144 CUDA) | 12 GB | 672 GB/s | ~100 | $549 | 7B-13B (quantized) |
| RTX 4070 Super | 12 GB | 504 GB/s | 70 | $550 | 7B |
| RTX 3090 (Used) | 24 GB | 936 GB/s | 71 | $600-800 | 30B (quantized) |
Workstation GPUs ($4,000-$10,000)
Professional-grade reliability, larger VRAM, and enterprise support
Highlights
- 96GB handles 70B models at FP16 precision
- Blackwell architecture with 5th-gen Tensor Cores
- Native FP4/FP6/FP8 support for optimal quantization
- Professional driver support and validation
NVIDIA RTX PRO 6000 Blackwell
Blackwell for Datacenter
Highlights
- Full Blackwell performance for rack-mounted deployments
- 96GB handles 70B models at FP16 precision
- ECC memory for enterprise reliability
- NVLink support for multi-GPU scaling
NVIDIA L4
Edge and Efficient Inference
Highlights
- Exceptional efficiency at only 72W TDP
- Compact form factor for edge deployment
- 24GB handles 13B models
- Cost-effective for scale-out inference
Workstation GPU Comparison
| GPU | VRAM | Bandwidth | TDP | Cloud $/hr | Best Use Case |
|---|---|---|---|---|---|
| RTX PRO 6000 Blackwell | 96 GB | 1.8 TB/s | 350W | Contact | Datacenter professional |
| RTX PRO 6000 Blackwell Max-Q | 96 GB | 1.8 TB/s | 250W | Contact | Workstation professional |
| L40S | 48 GB | 864 GB/s | 350W | $0.85-1.50 | Cloud inference |
| L40 | 48 GB | 864 GB/s | 300W | $0.85-1.22 | Mixed workloads |
| L4 | 24 GB | 300 GB/s | 72W | $0.35-0.60 | Edge / efficient |
Datacenter GPUs: Hopper ($25,000-$45,000)
NVIDIA's Hopper architecture remains the production workhorse for enterprise AI in 2026
Highlights
- 4th-gen Tensor Cores with native FP8 support
- Transformer Engine optimizes LLM workloads automatically
- MIG allows partitioning into 7 isolated instances
- ~22,000 tokens/sec on Llama 2 70B (offline benchmark)
Highlights
- 76% more VRAM than H100 (141GB vs 80GB)
- 43% higher memory bandwidth (4.8 TB/s vs 3.35 TB/s)
- ~45% faster inference on Llama 2 70B (~31,700 tokens/sec)
- Single-GPU 70B model serving without tensor parallelism
H100 vs H200 Performance Comparison
| Metric | H100 SXM | H200 | Improvement |
|---|---|---|---|
| VRAM | 80 GB | 141 GB | +76% |
| Bandwidth | 3.35 TB/s | 4.8 TB/s | +43% |
| Llama 70B Tokens/s | 21,806 | 31,712 | +45% |
| GPT-3 175B Inference | 1x | 1.8x | +80% |
| Price Premium | Baseline | +20-25% | — |
Datacenter GPUs: Blackwell ($45,000+)
NVIDIA's Blackwell delivers a generational leap in AI performance
Highlights
- 5th-gen Tensor Cores with FP4/FP6 native support
- Up to 15x faster inference vs H100 systems (NVIDIA claim)
- ~2-2.5x tokens/second improvement on Llama 2 70B vs H200 (estimated)
- Enables real-time trillion-parameter LLM inference
Highlights
- 50% more memory than B200 (288GB vs 192GB)
- ~1.5x compute performance vs B200 (estimated)
- GB300 NVL72 rack achieves 1.1 EXAFLOPS
Blackwell Generation Comparison
| Specification | B200 | B300 | Improvement |
|---|---|---|---|
| VRAM | 192 GB | 288 GB | +50% |
| Bandwidth | 8 TB/s | 8 TB/s | Same |
| FP4 PFLOPS (sparse) | 20 | ~30 (est.) | ~+50% |
| TDP | 1,000-1,200W | 1,400W | +17-40% |
AMD Instinct Series
Compelling alternatives with industry-leading memory capacity
Highlights
- 192GB HBM3 matches B200 capacity at lower cost
- Excellent memory bandwidth (5.3 TB/s)
- ROCm 6.x with improved PyTorch/vLLM support
- Best for memory-intensive workloads
Highlights
- Industry-leading 256GB memory capacity
- 6 TB/s bandwidth exceeds H200
- Up to 1.3x AI performance vs competitive accelerators
Highlights
- 4x generational AI compute improvement over MI300X
- 35x inference performance leap
- Supports 520B parameter models on single GPU
- Competitive with B200 at ~40% better tokens-per-dollar (AMD claim)
Intel Gaudi 3
Ethernet-native alternative with competitive price-performance
Intel Gaudi 3
Open Ecosystem Alternative
Highlights
- 70% better price-performance inference throughput vs H100 (Intel claim)
- Open Ethernet networking (no proprietary NVLink required)
- Strong PyTorch/Hugging Face integration
- VMware Cloud Foundation support
Cloud GPU Pricing Guide (January 2026)
Compare hourly rates across hyperscalers and specialized providers
Pricing verified January 2026. Cloud GPU pricing changes frequently—verify current rates with providers before purchasing.
Hyperscaler Pricing
| Provider | H100 ($/hr) | H200 ($/hr) | A100 80GB ($/hr) |
|---|---|---|---|
| AWS | $3.90 | $5.50 | $4.09 |
| Google Cloud | $3.00 | $4.50 | $3.67 |
| Microsoft Azure | $6.98 | $7.50 | $3.40 |
Prices vary by region; committed use discounts available (30-50% off)
Cost Optimization Strategies
Right-Size Your GPU
Small models (7B): RTX 4090 or A100 40GB. Medium (7-70B): A100 80GB or H100. Large (70B+): H200 or B200.
Leverage Quantization
INT8/FP8 reduces VRAM 50%. INT4 reduces 75%. Enables smaller/cheaper GPUs for production inference.
Spot/Preemptible Instances
60-80% savings on hyperscalers. Best for batch inference, development, testing.
Reserved Capacity
1-year: 30-40% savings. 3-year: 50-60% savings. Best for predictable workloads.
Deployment Decision Framework
Choose the right GPU based on your use case, model size, and budget
Decision Tree: Choosing Your GPU
Model Size to GPU Mapping
| Model Parameters | Minimum GPU (Quantized) | Recommended GPU (FP16) |
|---|---|---|
| 3-7B | RTX 4070 (12GB) | RTX 4090 (24GB) |
| 7-13B | RTX 4090 (24GB) | RTX 6000 Pro (96GB) |
| 13-30B | RTX 6000 Pro (96GB) | H100 (80GB) |
| 30-70B | H100 (80GB) | H200 (141GB) |
| 70-175B | H200 (141GB) | B200 (192GB) |
| 175B+ | B200 (192GB) | B200 Multi-GPU |
Total Cost of Ownership Analysis
Buy vs. rent break-even analysis and cost component breakdown
Buy vs. Rent Break-Even Analysis
Assumptions: H100 purchase $22,500. Electricity $0.10/kWh. 3-year depreciation. 75% utilization.
| Scenario | Break-Even (Hours) | Break-Even (Months @ 24/7) |
|---|---|---|
| H100 vs $3.50/hr cloud | 6,429 hours | ~10 months |
| H100 vs $2.00/hr cloud | 11,250 hours | ~17 months |
| RTX 4090 vs $0.50/hr cloud | 3,000 hours | ~4.5 months |
- < 3,500 hours/year → Rent cloud GPUs
- 3,500+ hours/year → Consider purchasing
- Full 24/7 utilization → Ownership typically cheaper within 12-18 months
Cloud Costs
- GPU compute (hourly rate)
- Storage ($0.07-0.20/GB/month)
- Egress ($0.08-0.12/GB)
- Network bandwidth
On-Premise Costs
- Hardware (GPU, server, networking)
- Power (30-50% of GPU TDP for cooling)
- Facility (rack space, cooling infrastructure)
- Operations (administration, maintenance)
- Depreciation (3-5 years typical)
Performance Benchmarks Summary
Real-world inference performance across GPU configurations
LLM Inference Performance (Tokens/Second)
| GPU | Llama 2 7B | Llama 2 70B | Context Length |
|---|---|---|---|
| RTX 4090 | 90-100 | N/A (too large) | 4K |
| RTX 5090 | 120-140 | 15-20 (INT4) | 8K |
| L40S | 80-95 | N/A | 4K |
| H100 | 150+ | 21,800 | 8K+ |
| H200 | 180+ | 31,700 | 32K+ |
| B200 | 250+ (est.) | ~45,000 (est.) | 128K+ |
Benchmark Methodology: Performance figures based on MLPerf Inference benchmarks (H100, H200) and manufacturer specifications. Llama 2 70B results measured with TensorRT-LLM, FP8 precision, optimal batch sizes. B200 figures estimated based on 2-2.5x H200 performance per NVIDIA claims. Actual performance varies by workload, batch size, framework version, and system configuration.
Key Takeaways
Quick recommendations by deployment scenario
For Local/Edge Deployment
- RTX 4090 remains excellent value with mature software support
- RTX 5090 offers 32GB VRAM but requires software maturation
- L4 is ideal for power-constrained edge deployments
For Cloud Inference
For Enterprise/Datacenter
- H200 eliminates multi-GPU complexity for 70B models
- B200 delivers generational leap but requires infrastructure upgrades
- AMD MI355X provides competitive alternative to B200
Future Considerations
- Quantization continues to enable larger models on smaller GPUs
- FP4 support (Blackwell, CDNA4) will further improve efficiency
- Memory capacity remains primary bottleneck for local deployment
- Software optimization often matters more than raw hardware specs
SLYD Inference Solutions
Comprehensive GPU solutions for AI inference deployment
Hardware Sales
NVIDIA: B200, H200, H100, RTX 6000 Pro, A100
OEMs: Dell, Supermicro, HPE, Lenovo, Gigabyte
GPU Financing
2-3 year lease terms to preserve capital for model development. Flexible upgrade paths as new architectures release.
SLYD Compute Marketplace
Access available compute capacity. Deploy AI applications with one click. Maintain data sovereignty with local deployment.
Consulting Services
GPU selection guidance, infrastructure planning, and TCO optimization. Personalized recommendations for your inference workloads.
Ready to Deploy AI Inference at Scale?
Get personalized GPU recommendations based on your specific inference workloads and deployment requirements. Our team helps you select and deploy the perfect GPU solution.
Frequently Asked Questions
Common questions about GPU selection for AI inference
What is the best GPU for AI inference in 2026?
The best GPU depends on model size and budget. For large language models (70B+), NVIDIA H200 or B200 with 141-192GB HBM3e offers best performance. For mid-range deployments, RTX 6000 Blackwell Pro provides excellent value. For budget deployments, RTX 5090 with 32GB GDDR7 handles 70B models with quantization.
How much VRAM do I need for LLM inference?
At FP16 precision: 7B models need 14-16GB, 13B need 26-28GB, 30B need 60-64GB, 70B need 140-144GB. With INT4 quantization, requirements drop by 75%: 7B fits in 4-5GB, 70B fits in 35-45GB. Always add overhead for KV cache, especially with longer context windows.
Should I buy or rent GPUs for inference?
For less than 3,500 GPU-hours per year, cloud rental is more cost-effective. Above 3,500 hours, consider purchasing. At 24/7 utilization, ownership typically becomes cheaper within 12-18 months. Factor in power, cooling, and facility costs for on-premise deployments.
What is the difference between H100 and H200 for inference?
H200 offers 76% more VRAM (141GB vs 80GB) and 43% higher memory bandwidth (4.8 TB/s vs 3.35 TB/s). This translates to ~45% faster inference on Llama 2 70B. H200 enables single-GPU 70B model serving without tensor parallelism, simplifying deployment.
Is AMD MI300X competitive with NVIDIA for inference?
AMD MI300X offers 192GB HBM3 at lower cost than H100. Performance is 37-66% of H100 in some benchmarks due to software overhead, but excels in memory-bound workloads. ROCm 6.x has improved PyTorch/vLLM support. Best for organizations with ROCm expertise or cost-sensitive memory-intensive deployments.
What factors matter most for inference GPU selection?
Four key factors: 1) Memory capacity to fit model and KV cache, 2) Memory bandwidth affecting token generation speed, 3) Low-precision support (FP8, INT4) for efficiency, 4) Power efficiency for operational cost. For production, also consider batch processing capabilities and multi-GPU scaling options.