GPU-MART.COM · GPU COMPARE CENTER
GPU COMPARE
FIND YOUR MATCH
GPU comparison chart and benchmark tool covering 25+ NVIDIA models. Compare specs side-by-side to find the best GPU for AI, rendering, streaming or stable diffusion — then deploy as a GPU VPS or dedicated GPU server on GPU-Mart.
25+GPU Models
5Use Case Scenarios
4Max Side-by-Side
16Key Parameters
VRAM:
Showing 25 GPUs
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| GPU Model | VRAM | CUDA Cores | Mem BW | FP32 TF | FP16 TF | NVENC | ECC | Gen AI | Training | Streaming | Rendering | Price/mo |
|---|
Parameter Guide
Which GPU Parameters Matter Most?
Reference guide to evaluate GPU specs by workload — rated ★ to ★★★★★ per scenario.
| GPU Parameter | 🧠 Generative AI | 🔬 AI Training | 👁️ Computer Vision | 📡 Streaming | 🎮 Rendering |
|---|
Scenario Deep Dive
GPU Recommendations by Workload
Detailed parameter comparison for each use case scenario. Click a tab to explore the recommended GPUs for that workload.
Generative AI — LLM / Image / Speech Inference
Key metrics: VRAM capacity (LLM ≥48 GB / Stable Diffusion ≥16 GB), Memory Bandwidth, and FP16 / Tensor TFLOPS. MIG support enables multi-tenant deployments. ECC memory ensures reliability in long-running inference tasks.
⚡ VRAM ≥ 48 GB
⚡ FP16 Tensor TFLOPS
⚡ Memory Bandwidth
⚡ ECC + MIG
| GPU Parameter | H100 | A100 | RTX Pro 6000 | RTX Pro 5000 | RTX 5090 | RTX A6000 | RTX Pro 4000 | RTX A5000 |
|---|---|---|---|---|---|---|---|---|
| CUDA Cores | 14,592 | 6,912 | 24,064 | 14,080 | ~20,000 | 10,752 | 6,144 | 8,192 |
| Tensor Core Gen | 4th (Hopper) | 3rd (Ampere) | 5th (Blackwell) | 5th (Blackwell) | 5th (Blackwell) | 3rd (Ampere) | 5th (Blackwell) | 3rd (Ampere) |
| VRAM (GB) | 80 | 80 | 96 | 48 / 72 | 32 | 48 | 24 | 24 |
| Memory Bandwidth (GB/s) | 3,352 | 2,039 | 1,792 | 1,344 | ~1,500 | 768 | 672 | 768 |
| FP16 Tensor TFLOPS | 1,979 (sparse) | 312 (sparse) | — | — | ~200 | 77.6 | — | 42.5 |
| NVENC / AV1 | None | None | 4× / Yes | 2× / Yes | 2× / Yes | 1× / No | 2× / Yes | 1× / No |
| RT Core Gen | None | None | 4th | 4th | 4th | 2nd | 4th | 2nd |
| NVLink | Yes | Yes | Yes | Yes | Yes | Yes | No | Yes |
| MIG Support | Yes | Yes | Yes | Yes | No | No | — | No |
| ECC Support | Yes (on-chip) | Yes | Yes (GDDR7) | Yes (GDDR7) | No | Yes | Yes (GDDR7) | Yes |
AI Training & Fine-tuning
Decisive metrics: FP16 Tensor TFLOPS, Memory Bandwidth, VRAM (full training ≥64 GB recommended), NVLink for multi-GPU scaling, MIG for workload isolation, and ECC for data integrity during long training runs.
⚡ FP16 TFLOPS
⚡ NVLink
⚡ VRAM ≥ 64 GB
⚡ MIG + ECC
| GPU Parameter | H100 | A100 | RTX Pro 6000 | RTX Pro 5000 | RTX A6000 | A40 | RTX 5090 |
|---|---|---|---|---|---|---|---|
| CUDA Cores | 14,592 | 6,912 | 24,064 | 14,080 | 10,752 | 10,752 | ~20,000 |
| Tensor Core Gen | 4th | 3rd | 5th | 5th | 3rd | 3rd | 5th |
| VRAM (GB) | 80 | 80 | 96 | 48 / 72 | 48 | 48 | 32 |
| Memory Bandwidth (GB/s) | 3,352 | 2,039 | 1,792 | 1,344 | 768 | 696 | ~1,500 |
| FP16 Tensor TFLOPS | 1,979 | 312 | — | — | 77.6 | 75.6 | ~200 |
| NVLink | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| MIG Support | Yes | Yes | Yes | Yes | No | No | No |
| ECC Support | Yes (on-chip) | Yes | Yes (GDDR7) | Yes (GDDR7) | Yes | Yes | No |
Computer Vision — Video Analytics & Detection
Important metrics: VRAM (≥8 GB), NVDEC for hardware video decode acceleration, Tensor Cores for inference throughput, and CUDA core count for parallel frame processing pipelines.
⚡ NVDEC Gen
⚡ Tensor Cores
⚡ VRAM ≥ 8 GB
⚡ CUDA Cores
| GPU Parameter | RTX 5090 | RTX A6000 | RTX Pro 5000 | RTX Pro 6000 | RTX A5000 | RTX 4060 | RTX 3060 Ti | RTX 2060 |
|---|---|---|---|---|---|---|---|---|
| CUDA Cores | ~20,000 | 10,752 | 14,080 | 24,064 | 8,192 | 3,072 | 4,864 | 1,920 |
| Tensor Core Gen | 5th | 3rd | 5th | 5th | 3rd | 4th | 3rd | 1st |
| VRAM (GB) | 32 | 48 | 48 / 72 | 96 | 24 | 8 | 8 | 6 |
| Memory Bandwidth (GB/s) | ~1,500 | 768 | 1,344 | 1,792 | 768 | 272 | 448 | 336 |
| NVENC / AV1 | 2× / Yes | 1× / No | 2× / Yes | 4× / Yes | 1× / No | 1× / Yes | 1× / No | 1× / No |
| NVDEC Generation | 6th | 5th | 6th | 6th | 5th | 5th | 5th | 4th |
| RT Core Gen | 4th | 2nd | 4th | 4th | 2nd | 3rd | 2nd | 1st |
| MIG Support | No | No | Yes | Yes | No | No | No | No |
| ECC Support | No | Yes | Yes (GDDR7) | Yes (GDDR7) | Yes | No | No | No |
Streaming & Media Processing — Live / Transcode
Decisive metrics: NVENC encoder count and generation (AV1 support for latest codecs), NVDEC generation for concurrent decode, and concurrent session capacity. VRAM matters less here — encoder count and codec support are paramount.
⚡ NVENC Count + Gen
⚡ AV1 Support
⚡ NVDEC Gen
⚡ Concurrent Sessions
| GPU Parameter | RTX Pro 6000 | RTX Pro 5000 | RTX Pro 4000 | RTX 5090 | RTX 5060 | RTX 4060 | RTX A6000 | RTX A4000 |
|---|---|---|---|---|---|---|---|---|
| CUDA Cores | 24,064 | 14,080 | 6,144 | ~20,000 | ~4,000 | 3,072 | 10,752 | 6,144 |
| NVENC Count / Gen / AV1 | 4× / 9th / Yes | 2× / 9th / Yes | 2× / 9th / Yes | 2× / 9th / Yes | 2× / 9th / Yes | 1× / 8th / Yes | 1× / 7th / No | 1× / 7th / No |
| NVDEC Generation | 6th | 6th | 6th | 6th | 6th | 5th | 5th | 5th |
| VRAM (GB) | 96 | 48 / 72 | 24 | 32 | 8 / 12 | 8 | 48 | 16 |
| Memory Bandwidth (GB/s) | 1,792 | 1,344 | 672 | ~1,500 | ~300 | 272 | 768 | 448 |
| MIG Support | Yes | Yes | — | No | No | No | No | No |
| ECC Support | Yes (GDDR7) | Yes (GDDR7) | Yes (GDDR7) | No | Unknown | No | Yes | Yes |
Graphics, Rendering & Cloud Gaming
Key metrics: RT Core generation for ray tracing, FP32 TFLOPS for rasterization performance, VRAM capacity (≥16 GB for complex scenes), and Virtualization / SR-IOV for cloud gaming multi-tenant deployments.
⚡ RT Core Gen
⚡ FP32 TFLOPS
⚡ VRAM ≥ 16 GB
⚡ Virtualization
| GPU Parameter | RTX Pro 6000 | RTX 5090 | RTX Pro 5000 | RTX 4090 | RTX A6000 | RTX Pro 4000 | RTX A5000 | RTX 4060 |
|---|---|---|---|---|---|---|---|---|
| RT Core Gen | 4th | 4th | 4th | 3rd | 2nd | 4th | 2nd | 3rd |
| CUDA Cores | 24,064 | ~20,000 | 14,080 | 16,384 | 10,752 | 6,144 | 8,192 | 3,072 |
| VRAM (GB) | 96 | 32 | 48 / 72 | 24 | 48 | 24 | 24 | 8 |
| Memory Bandwidth (GB/s) | 1,792 | ~1,500 | 1,344 | 1,008 | 768 | 672 | 768 | 272 |
| FP32 TFLOPS | 117 | ~100 | 70 | 82.6 | 38.7 | 40 | 27.8 | 15.2 |
| NVENC / AV1 | 4× / Yes | 2× / Yes | 2× / Yes | 2× / Yes | 1× / No | 2× / Yes | 1× / No | 1× / Yes |
| MIG Support | Yes | No | Yes | No | No | — | No | No |
| ECC Support | Yes (GDDR7) | No | Yes (GDDR7) | No | Yes | Yes (GDDR7) | Yes | No |
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FAQ
GPU Comparison FAQ
Common questions about GPU specs, model comparisons and choosing the right GPU for your workload.
Click any GPU card to select it — the border turns green. Select up to 4 GPUs, then hit "COMPARE SELECTED" in the sticky bar to open a side-by-side modal with full specs, scenario-fit ratings, and recommendations. You can also filter by use case or VRAM to narrow down the list first.
A GPU VPS partitions a physical GPU using MIG or vGPU, giving you an isolated slice at a lower price — ideal for inference, development, and lighter workloads. A dedicated GPU server gives you exclusive access to the full GPU and its entire memory bandwidth — best for training, large-scale inference, and production workloads that need consistent, unshared performance.
The H100 has dramatically higher memory bandwidth (3,352 vs 2,039 GB/s) and 4th-gen Tensor Cores, making it significantly faster for large-scale LLM inference and training. The A100 is a proven, cost-effective choice for most inference workloads — 80 GB VRAM, ECC, MIG support, and strong multi-GPU scaling via NVLink.
The A100 uses HBM2e memory and NVLink for high-throughput inference and multi-GPU training. The RTX A6000 uses GDDR6 (48 GB VRAM) and is better suited for large rendering scenes and models up to 70B quantized — at a lower monthly cost. If raw AI throughput matters most, A100. If you need more VRAM headroom for rendering or mixed workloads, A6000.
The H100 leads on raw throughput (1,979 FP16 TFLOPS, 3,352 GB/s bandwidth). For cost-effective training, the A100, RTX Pro 6000, and RTX Pro 5000 are strong choices — all support NVLink, ECC, and MIG. The RTX Pro 6000 (96 GB GDDR7) is particularly compelling for fine-tuning large models that need maximum VRAM.
The sweet spot is 16–24 GB VRAM. The RTX A5000 (24 GB, ECC), RTX Pro 4000 (24 GB, Blackwell 5th-gen Tensor Cores), and RTX 4090 (24 GB) all deliver fast generation. For SDXL batch workflows or maximum resolution, the RTX Pro 6000 (96 GB) is the top choice.
The RTX Pro 6000 leads with 96 GB VRAM, 4th-gen RT Cores, and 117 FP32 TFLOPS — ideal for complex scenes and large textures. The RTX 5090 (~100 FP32 TFLOPS, 4th-gen RT Cores) and RTX 4090 (82.6 TFLOPS, 24 GB) are strong alternatives at lower cost.
NVENC encoder count and generation are the decisive factors. The RTX Pro 6000 leads with 4× 9th-gen NVENC with AV1 support. The RTX Pro 5000 and RTX Pro 4000 offer 2× 9th-gen NVENC / AV1. The RTX 5060 and RTX 4060 are the most affordable options with dual NVENC and AV1. Avoid the A100 and H100 for streaming — they have no NVENC hardware.
The RTX Pro 6000 tops the chart at 96 GB GDDR7 with ECC, MIG, and NVLink — ideal for 70B+ models in full precision. The H100 and A100 both offer 80 GB HBM2e. The RTX Pro 5000 offers 48–72 GB GDDR7 with MIG support, making it a strong option for 34B–70B quantized models.
GPU Comparison
Side-by-side parameter breakdown