How do I use this GPU comparison tool?

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.

What is the difference between a GPU VPS and a dedicated GPU server?

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.

A100 vs A6000 - which GPU should I choose?

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.

What is the best GPU for AI training and fine-tuning?

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.

What is the best GPU for Stable Diffusion?

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.

What is the best GPU for rendering (3D / CGI)?

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.

What is the best GPU for streaming and video transcoding?

NVENC encoder count and generation are the decisive factors. The RTX Pro 6000 leads with 4x 9th-gen NVENC with AV1 support. The RTX Pro 5000 and RTX Pro 4000 offer 2x 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.

Which GPU has the most VRAM for large AI models?

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-MART.COM · GPU COMPARE CENTER 2026

GPU COMPARE

Q: A100 vs H100 - which is better for AI and LLM inference?

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.

FIND YOUR MATCH

Use GPU Compare, an interactive GPU comparison chart, to compare NVIDIA GPUs by VRAM, compute performance, encoder features, and workload fit. Compare up to four models for AI, rendering, streaming, or graphics workloads, then explore matching GPU VPS plans or GPU dedicated server plans .

25+GPU Models

5Use Case Scenarios

4Max Side-by-Side

16Key Parameters

View Hosting Plans →

Selected: 0 / 4

USE CASE:

VRAM:

Showing 25 GPUs

💡 Compare GPUs side-by-side — up to 4 at once

1Click any GPU card to select it 2Select up to 4 GPUs 3Hit "COMPARE SELECTED" in the bar above 4View full specs, scenario fit & recommendations

Ready to Choose a GPU Server?

Compare live GPU VPS and dedicated server plans, or request a configuration recommendation based on your model, workload, VRAM target, and budget.

View Live GPU Plans Request a GPU Recommendation

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	14,336	6,912	24,064	14,080	21,760	10,752	8,960	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	32	48	24	24
Memory Bandwidth (GB/s)	2,000	2,039	1,792	1,344	1,792	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	No	No	No	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

For high-VRAM LLM inference and image generation, compare RTX Pro 6000 hosting , H100 GPU servers , or explore LLM hosting options .

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	14,336	6,912	24,064	14,080	10,752	10,752	21,760
Tensor Core Gen	4th	3rd	5th	5th	3rd	3rd	5th
VRAM (GB)	80	80	96	48	48	48	32
Memory Bandwidth (GB/s)	2,000	2,039	1,792	1,344	768	696	1,792
FP16 Tensor TFLOPS	1,979	312	—	—	77.6	75.6	~200
NVLink	Yes	Yes	No	No	Yes	No	No
MIG Support	Yes	Yes	Yes	Yes	No	No	No
ECC Support	Yes (on-chip)	Yes	Yes (GDDR7)	Yes (GDDR7)	Yes	Yes	No

For larger training jobs and multi-GPU requirements, review NVIDIA A100 rental , H100 dedicated servers , and multi-GPU configurations .

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	21,760	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	96	24	8	8	6
Memory Bandwidth (GB/s)	1,792	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

For computer vision, AI agents, and production inference, compare RTX 5090 GPU rental , RTX A5000 hosting , or explore AI server plans .

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	8,960	21,760	3,840	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	24	32	8	8	48	16
Memory Bandwidth (GB/s)	1,792	1,344	672	1,792	~288	272	768	448
MIG Support	Yes	Yes	—	No	No	No	No	No
ECC Support	Yes (GDDR7)	Yes (GDDR7)	Yes (GDDR7)	No	No	No	Yes	Yes

For OBS streaming, video encoding, and media workflows, explore GPU streaming hosting , compare RTX 5060 hosting , or review RTX Pro 4000 hosting .

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	21,760	14,080	16,384	10,752	8,960	8,192	3,072
VRAM (GB)	96	32	48	24	48	24	24	8
Memory Bandwidth (GB/s)	1,792	1,792	1,344	1,008	768	672	768	272
FP32 TFLOPS	125	~105	70	82.6	38.7	~44	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

For GPU rendering, visualization, and creative production, compare RTX 4090 dedicated servers , RTX A6000 hosting , or explore GPU rendering server options .

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. Select up to four GPUs, then click "COMPARE SELECTED" in the sticky bar to open a side-by-side view with specs, workload-fit ratings, and recommendations. You can also filter models by use case or VRAM.

A GPU VPS provides a virtualized server environment with exclusive access to a full physical GPU through PCIe Passthrough. It includes dedicated GPU resources, CPU, RAM, and storage for the selected plan. A GPU dedicated server provides an entire physical machine and is better suited to multi-GPU, high-intensity, or hardware-isolation requirements.

The H100 leads this comparison in FP16 Tensor performance, with 1,979 sparse FP16 Tensor TFLOPS and 4th-gen Tensor Cores, making it a strong option for large-scale LLM inference and training. The A100 remains a proven choice for many inference workloads. Both are listed with 80GB VRAM, ECC, MIG support, and NVLink for multi-GPU scaling.

The A100 is listed with 80GB VRAM, high memory bandwidth, ECC, MIG, and NVLink, making it suitable for high-throughput inference and multi-GPU training. The RTX A6000 provides 48GB GDDR6 memory, ECC, NVLink, and professional graphics capabilities for rendering, visualization, and mixed AI workloads.

The H100 and A100 are the strongest options in this chart when multi-GPU training with NVLink is required. The RTX Pro 6000 offers 96GB GDDR7 memory, while the RTX Pro 5000 provides 48GB GDDR7 memory. Both are listed with ECC and MIG support, but without NVLink, making them suitable for high-VRAM single-GPU workloads and selected fine-tuning tasks.

The 16GB to 24GB range is a practical starting point for many Stable Diffusion workloads. The RTX A5000, RTX Pro 4000, and RTX 4090 each provide 24GB of VRAM in this comparison. For larger batch workflows, higher-resolution generation, or multiple concurrent pipelines, the RTX Pro 6000 provides 96GB of VRAM.

The RTX Pro 6000 leads this chart with 96GB VRAM, 4th-gen RT Cores, and 125 FP32 TFLOPS, making it suitable for complex scenes and large textures. The RTX 5090, at approximately 105 FP32 TFLOPS with 32GB VRAM, and the RTX 4090, at 82.6 FP32 TFLOPS with 24GB VRAM, are strong alternatives for demanding rendering workloads.

NVENC encoder count, encoder generation, AV1 support, and NVDEC capabilities are the key factors. The RTX Pro 6000 is listed with four 9th-gen NVENC encoders and AV1 support. RTX Pro 5000, RTX Pro 4000, RTX 5090, and RTX 5060 each provide dual NVENC with AV1 support. A100 and H100 do not include NVENC hardware for streaming.

The RTX Pro 6000 has the most VRAM in this comparison at 96GB GDDR7, with ECC and MIG support. The H100 and A100 each provide 80GB VRAM with ECC, MIG, and NVLink. The RTX Pro 5000 provides 48GB GDDR7 with ECC and MIG support, while the table lists no NVLink support for this model.

GPU Hosting — gpu-mart.com

Ready to Deploy
Your GPU Server?

Deploy any GPU from this comparison as a GPU VPS or dedicated GPU server. A100, H100, RTX Pro 6000 and 25+ models — hourly or monthly, no contracts, instant provisioning.

View All Plans → Rent by the Hour

GPU Comparison

Side-by-side parameter breakdown