GH200 vs A6000
Explore a head to head comparison of specifications, performance, and pricing.
GH200
The NVIDIA GH200 is an advanced Hopper-based GPU that significantly boosts performance for generative AI, LLM, and HPC workloads with enhanced memory and bandwidth.
A6000
The NVIDIA A6000 delivers high-performance computing capabilities for AI, machine learning, and data science applications.
GH200 vs A6000: Which Should You Choose?
The GH200 offers 96 GB of VRAM — 2× the 48 GB on the A6000 — making it better suited for large model workloads that require holding more parameters in GPU memory. On FP16 throughput, the GH200 delivers 267.6 TFLOPS versus 38.71 TFLOPS on the A6000 — 7× faster for mixed-precision training and inference. Memory bandwidth favors the A6000 at 0.77 TB/s compared to 0.00 TB/s on the GH200, which directly impacts inference latency for memory-bandwidth-bound models. Architecturally, the GH200 is built on Hopper while the A6000 uses Ampere, reflecting different generational capabilities and optimizations. On Shadeform, the A6000 starts from $0.49/hr versus $1.49/hr for the GH200 — 204% more expensive — reflecting the performance premium. The A6000 is available across 6 cloud providers on Shadeform compared to 2 for the GH200, giving more options for region and pricing flexibility.
GH200 — 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 GH200 when:
- ✓You need 96 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
- ✓Your preferred provider already has availability
A6000 — Best Use Cases
- •General-purpose deep learning training
- •Fine-tuning models up to 13B parameters
- •AI inference at moderate throughput
- •Computer vision and NLP workloads
Choose A6000 when:
- ✓48 GB VRAM is sufficient for your workload
- ✓Cost efficiency is your primary concern
- ✓Your workload does not require peak FP16 throughput
- ✓You need flexibility across multiple cloud providers or regions
See how the GH200 & A6000 compare
Compare detailed hardware specifications and average pricing for the GH200 and A6000.
Compare Hardware Specifications
| GH200 | A6000 | |
|---|---|---|
| GPU Type | GH200 | A6000 |
| VRAM per GPU | 96 GB | 48 GB |
| Manufacturer | NVIDIA | NVIDIA |
| Architecture | Hopper | Ampere |
| Interconnect | NVLink-C2C | PCIe Gen4 |
| Memory Bandwidth | 4 TB/s or 4.9 TB/s | 768 GB/s |
| FP16 TFLOPS | 267.6 TFLOPS (4:1) | 38.71 TFLOPS (1:1) |
| CUDA Cores | 16896 | 10752 |
| Tensor Cores | 528 (4th Gen) | 336 (3rd Gen) |
| RT Cores | N/A | 84 (2nd Gen) |
| Base Clock | 1500 MHz | 1410 MHz |
| Boost Clock | 1980 MHz | 1800 MHz |
| TDP | 900W-1000W | 300W |
| Process Node | TSMC 4N | TSMC 8nm |
| Data Formats | FP8, INT8, BF16, FP16, TF32, FP32, FP64 | INT8, BF16, FP16, TF32, FP32 |
Compare Average On-Demand Pricing
| GH200 | A6000 | |
|---|---|---|
| 1 GPU | $2.86 /hr | $0.90 /hr |
| 2 GPUs | N/A | $1.79 /hr |
| 4 GPUs | N/A | $3.58 /hr |
| 8 GPUs | N/A | $4.16 /hr |
Frequently Asked Questions: GH200 vs A6000
The main differences are VRAM (96 GB vs 48 GB), FP16 throughput (267.6 vs 38.71 TFLOPS), architecture (Hopper vs Ampere). The GH200 uses the Hopper architecture while the A6000 is based on Ampere, giving each GPU different generational capabilities.
The GH200 is generally better for large language model training due to its higher throughput and 96 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 A6000 is available from $0.49/hr. The GH200 starts from $1.49/hr. Prices vary by provider, region, and contract length. Reserved commitments can reduce hourly costs significantly compared to on-demand pricing.
The GH200 has more VRAM at 96 GB, compared to 48 GB on the A6000. 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 GH200 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 A6000, paying the premium may be justified by faster job completion and lower total cost.
The A6000 is currently available across 6 cloud providers on Shadeform's network, compared to 2 for the GH200. 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 GH200 or A6000. Shadeform supports on-demand clusters of up to 64 GPUs of the same type with no commitment required.
Explore GH200 & A6000 Instances
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