GPU Hosting & AI Engineering Blog
Benchmarks, GPU comparisons, deployment guides, and cost analysis — everything you need to run AI on dedicated GPU servers.
PCIe Lanes and Multi-GPU Performance on Dedicated Servers
x16 per card, x8, x4 - the PCIe topology of your server decides how much performance you extract from multi-GPU setups.
Latest Articles
Fresh benchmarks, comparisons, and deployment guides from the GigaGPU team.
One Big GPU vs Many Small GPUs – The Architectural Debate
The case for one 96GB card versus three or four 16GB cards at similar price - which wins for which…
Nvidia vs AMD vs Intel – Three-Way AI GPU Comparison 2026
All three vendors now compete seriously for AI workloads. A practical comparison of the software stacks, performance, and operational tradeoffs.
Multi-Tenant GPU Server Isolation Patterns
How to serve multiple tenants from one GPU server without one customer's workload starving another.
Multi-GPU NCCL Tuning on Dedicated Servers
The NCCL environment variables that actually move the needle on multi-GPU inference and training without NVLink.
Model Sharding vs Batch Scaling – Which Comes First
When your workload outgrows one GPU, do you split the model or run more replicas? The decision is almost always…
Model Parallelism Without NVLink – What Actually Works
Consumer and workstation GPUs in 2026 lack NVLink. Tensor and pipeline parallelism still work over PCIe - here is how…
LoRAX Multi-LoRA Serving on a Dedicated GPU
One base model, many LoRA adapters, one GPU - how to serve dozens of fine-tuned variants without running dozens of…
Intel Arc Pro B70 32GB vs RTX 5080 16GB for LLM Serving
Intel's 32GB workstation card against Nvidia's Blackwell flagship - does double the VRAM beat better software?
Intel Arc Pro B70 vs RTX 3090 – 32GB vs 24GB Class
Intel's 32GB newcomer against Nvidia's Ampere veteran - the fight for the large-VRAM value tier.
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