RTX 4000 Ada vs H200
Explore a head to head comparison of specifications, performance, and pricing.
RTX 4000 Ada
The NVIDIA RTX 4000 Ada delivers high-performance computing capabilities for AI, machine learning, and data science applications.
H200
The NVIDIA H200 is an advanced Hopper-based GPU that significantly boosts performance for generative AI, LLM, and HPC workloads with enhanced memory and bandwidth.
RTX 4000 Ada vs H200: Which Should You Choose?
The H200 offers 141 GB of VRAM — 7× the 20 GB on the RTX 4000 Ada — making it better suited for large model workloads that require holding more parameters in GPU memory. On FP16 throughput, the H200 delivers 267.6 TFLOPS versus 26.73 TFLOPS on the RTX 4000 Ada — 10× faster for mixed-precision training and inference. Memory bandwidth favors the RTX 4000 Ada at 0.36 TB/s compared to 0.00 TB/s on the H200, which directly impacts inference latency for memory-bandwidth-bound models. Architecturally, the RTX 4000 Ada is built on Ada Lovelace while the H200 uses Hopper, reflecting different generational capabilities and optimizations. On Shadeform, the RTX 4000 Ada starts from $0.79/hr versus $2.45/hr for the H200 — 210% more expensive — reflecting the performance premium. The H200 is available across 7 cloud providers on Shadeform compared to 1 for the RTX 4000 Ada, giving more options for region and pricing flexibility.
RTX 4000 Ada — Best Use Cases
- •LLM inference and model serving
- •Image generation and diffusion models
- •Smaller fine-tuning runs
- •Cost-efficient GPU compute
Choose RTX 4000 Ada when:
- ✓20 GB VRAM is sufficient for your workload
- ✓Cost efficiency is your primary concern
- ✓Your workload does not require peak FP16 throughput
- ✓Your preferred provider already has availability
H200 — Best Use Cases
- •Training large language models (7B–405B parameters)
- •High-throughput LLM inference
- •Mixture-of-experts and transformer workloads
- •Distributed multi-GPU training runs
Choose H200 when:
- ✓You need 141 GB+ VRAM for large models or long context windows
- ✓Maximum performance justifies the higher cost
- ✓You are training large models or running high-throughput inference
- ✓You need flexibility across multiple cloud providers or regions
See how the RTX 4000 Ada & H200 compare
Compare detailed hardware specifications and average pricing for the RTX 4000 Ada and H200.
Compare Hardware Specifications
| RTX 4000 Ada | H200 | |
|---|---|---|
| GPU Type | RTX 4000 Ada | H200 |
| VRAM per GPU | 20 GB | 141 GB |
| Manufacturer | NVIDIA | NVIDIA |
| Architecture | Ada Lovelace | Hopper |
| Interconnect | PCIe Gen4 | SXM5 |
| Memory Bandwidth | 360 GB/s | 4.8 TB/s |
| FP16 TFLOPS | 26.73 TFLOPS (1:1) | 267.6 TFLOPS (4:1) |
| CUDA Cores | 6144 | 16896 |
| Tensor Cores | 192 (4th Gen) | 528 (4th Gen) |
| RT Cores | 48 (3rd Gen) | N/A |
| Base Clock | 1500 MHz | 1500 MHz |
| Boost Clock | 2175 MHz | 1980 MHz |
| TDP | 130W | 350-700W |
| Process Node | TSMC 4N | TSMC 4N |
| Data Formats | FP8, INT8, BF16, FP16, TF32, FP32 | FP8, INT8, BF16, FP16, TF32, FP32, FP64 |
Compare Average On-Demand Pricing
| RTX 4000 Ada | H200 | |
|---|---|---|
| 1 GPU | $0.79 /hr | $3.33 /hr |
| 2 GPUs | N/A | $14.79 /hr |
| 4 GPUs | N/A | $7.60 /hr |
| 8 GPUs | N/A | $23.48 /hr |
Frequently Asked Questions: RTX 4000 Ada vs H200
The main differences are VRAM (20 GB vs 141 GB), FP16 throughput (26.73 vs 267.6 TFLOPS), architecture (Ada Lovelace vs Hopper). The RTX 4000 Ada uses the Ada Lovelace architecture while the H200 is based on Hopper, giving each GPU different generational capabilities.
The H200 is generally better for large language model training due to its higher throughput and 141 GB of VRAM, which allows fitting larger models or larger batch sizes in a single pass. For smaller models or fine-tuning tasks where cost matters more, both GPUs can be effective.
On Shadeform, the RTX 4000 Ada is available from $0.79/hr. The H200 starts from $2.45/hr. Prices vary by provider, region, and contract length. Reserved commitments can reduce hourly costs significantly compared to on-demand pricing.
The H200 has more VRAM at 141 GB, compared to 20 GB on the RTX 4000 Ada. Higher VRAM allows you to run larger models without quantization, use longer context windows, and process larger batch sizes — all of which improve throughput and reduce latency for memory-bound workloads.
Based on TFLOPS per dollar, the H200 offers better raw compute value at current Shadeform on-demand rates. However, the best choice depends on your specific workload — if you need the extra VRAM or throughput of the RTX 4000 Ada, paying the premium may be justified by faster job completion and lower total cost.
The H200 is currently available across 7 cloud providers on Shadeform's network, compared to 1 for the RTX 4000 Ada. Shadeform lets you deploy either GPU across all available providers from a single platform, so you can always find available capacity without manually checking each cloud.
Mixing different GPU types in a single training cluster is generally not recommended, as it creates performance bottlenecks where faster GPUs wait for slower ones. For best results, use a homogeneous cluster of either RTX 4000 Ada or H200. Shadeform supports on-demand clusters of up to 64 GPUs of the same type with no commitment required.
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