GPU Comparisons — Which GPU Is Right for You?
Choosing the right GPU for AI inference, LLM hosting, speech processing, or rendering depends on VRAM capacity, memory bandwidth, compute throughput, and budget. This page compares every GPU available at GigaGPU side by side so you can make an informed decision.
All figures are drawn from manufacturer specifications and our own benchmarking. For workload-specific numbers, see our tokens/sec benchmarks, TTS latency benchmarks, and cost-per-token calculator.
All GPUs available as dedicated bare-metal servers with full root access. No shared resources.
Full GPU Specification Comparison
Side-by-side specs for every GPU available at GigaGPU. Scroll horizontally on mobile.
| GPU | VRAM | Architecture | Cores | Boost Clock | FP32 TFLOPS | Bandwidth | PCIe | Price From |
|---|---|---|---|---|---|---|---|---|
| RTX 3050 | 6 GB GDDR6 | Ampere | 2,304 | 1,470 MHz | 6.8 | 168 GB/s | 4.0 x8 | /mo |
| RTX 4060 | 8 GB GDDR6 | Ada Lovelace | 3,072 | 1,830 MHz | 15.1 | 272 GB/s | 4.0 x8 | /mo |
| RTX 5060 | 8 GB GDDR7 | Blackwell 2.0 | 3,840 | 2,497 MHz | 19.2 | 448 GB/s | 5.0 x8 | /mo |
| RTX 4060 Ti 16GB | 16 GB GDDR6 | Ada Lovelace | 4,352 | 2,535 MHz | 22.1 | 288 GB/s | 4.0 x8 | /mo |
| RX 9070 XT | 16 GB GDDR6 | RDNA 4.0 | 4,096 | 2,970 MHz | 48.7 | 645 GB/s | 5.0 x16 | /mo |
| RTX 5080 | 16 GB GDDR7 | Blackwell 2.0 | 10,752 | 2,617 MHz | 56.3 | 960 GB/s | 5.0 x16 | /mo |
| RTX 3090 | 24 GB GDDR6X | Ampere | 10,496 | 1,695 MHz | 35.6 | 936 GB/s | 4.0 x16 | /mo |
| Arc Pro B70 | 32 GB GDDR6 | Xe2 | 4,096 | TBA | 22.9 | 608 GB/s | 5.0 x16 | /mo |
| Radeon AI Pro R9700 | 32 GB GDDR6 | RDNA 4 | 4,096 | 2,920 MHz | 47.8 | 645 GB/s | 5.0 x16 | /mo |
| RTX 5090 Popular | 32 GB GDDR7 | Blackwell 2.0 | 21,760 | 2,407 MHz | 104.8 | 1,790 GB/s | 5.0 x16 | /mo |
| Ryzen AI MAX+ 395 | 96 GB LPDDR5X | Strix Halo | 126 TOPS | 5,100 MHz | 14.8 | 256 GB/s | 4.0 | /mo |
| RTX 6000 PRO Flagship | 96 GB GDDR7 | Blackwell 2.0 | 24,064 | 2,617 MHz | 126.0 | 1,790 GB/s | 5.0 x16 | /mo |
Specifications from manufacturer datasheets. Prices are live from our billing system and may vary by configuration. The Ryzen AI MAX+ 395 is an APU with unified memory, not a discrete GPU — TOPS figure replaces CUDA core count.
Visual GPU Performance Comparison
See how each GPU stacks up across the metrics that matter most for AI and compute workloads.
Best GPU for Your Workload
Quick recommendations based on common AI and compute workloads.
Small LLMs (7–13B)
Chatbots, simple agents, and internal tools using models like Mistral 7B, LLaMA 3 8B, or Phi-4. 8–16 GB VRAM is sufficient at Q4 quantisation.
Large LLMs (33–70B)
Production inference for LLaMA 3.3 70B, DeepSeek-R1 70B, or Qwen3 72B. Requires 24–32 GB VRAM at Q4, or 96 GB for full-precision 70B deployments.
Speech & TTS
Self-hosted Whisper transcription, XTTS-v2 voice cloning, or Kokoro TTS. Speech models are smaller but benefit from fast compute for real-time synthesis.
Multimodal & Vision
OCR pipelines, document understanding, or vision-language models like LLaVA and Llama 3.2 Vision. Large context windows and image encoding demand ample VRAM.
Code Models
DeepSeek Coder, Qwen2.5-Coder, or StarCoder2 for code completion, review, and agentic coding. 16–32 GB covers most code model sizes at Q4.
Maximum VRAM (405B / Fine-Tuning)
Running the largest open models (LLaMA 3 405B, DeepSeek-V3 685B MoE) or fine-tuning with LoRA/QLoRA. 96 GB unified memory is the sweet spot.
Key Differences: NVIDIA vs AMD vs Intel for AI
Understanding the ecosystem trade-offs beyond raw specifications.
NVIDIA (CUDA)
The default choice for AI. CUDA has the widest ecosystem support — Ollama, vLLM, PyTorch, TensorFlow, and virtually every AI framework works out of the box. The RTX 5090 and RTX 6000 PRO represent the current performance ceiling for single-GPU inference. If you want guaranteed compatibility with any model or framework, NVIDIA is the safest bet.
AMD (ROCm)
AMD GPUs offer strong value with high VRAM-per-pound. The Radeon AI Pro R9700 delivers 32 GB for significantly less than NVIDIA equivalents. ROCm support has improved substantially — PyTorch, Ollama (via llama.cpp with ROCm), and vLLM all work. The RX 9070 XT is an excellent mid-range option with impressive bandwidth. Best for teams comfortable with a slightly less mature toolchain in exchange for better pricing.
Intel (Xe2 / oneAPI)
The Arc Pro B70 brings 32 GB of VRAM on Intel’s Xe2 architecture at a competitive price point. oneAPI and SYCL support is growing, and IPEX (Intel Extensions for PyTorch) enables many standard AI workflows. Still the newest entrant with the smallest ecosystem, but worth considering for VRAM-heavy workloads where NVIDIA pricing is prohibitive.
GPU Comparison — Frequently Asked Questions
Common questions about choosing a GPU for AI and compute workloads.
Available on all servers
- 1Gbps Port
- NVMe Storage
- 128GB DDR4/DDR5
- Any OS
- 99.9% Uptime
- Root/Admin Access
Our dedicated GPU servers provide full hardware resources and a dedicated GPU card, ensuring unmatched performance and privacy. Perfect for AI inference, LLM hosting, speech processing, rendering, and any other GPU-accelerated workload — with no shared resources.
Get in Touch
Not sure which GPU is right for your workload? Our team can help you choose the right configuration for your model size, throughput requirements, and budget.
Contact Sales →Or browse the knowledgebase for setup guides and documentation.
Find Your Perfect GPU
Flat monthly pricing. Full GPU resources. UK data centre. Deploy on any of our 12 GPU options in under an hour.
