GPU Hosting & AI Engineering Blog
Benchmarks, GPU comparisons, deployment guides, and cost analysis — everything you need to run AI on dedicated GPU servers.
Heterogeneous Multi-GPU Workload Split – Different Cards, One Server
Can you run an RTX 5090 and an RTX 3090 in the same chassis? Yes - and for many workloads it beats a homogeneous setup.
Latest Articles
Fresh benchmarks, comparisons, and deployment guides from the GigaGPU team.
GPU Memory Bandwidth Across the GigaGPU Lineup
Memory bandwidth decides LLM decode speed more than raw TFLOPS. Here is every card we host ranked on the number…
GPU Interconnect Options for AI Dedicated Servers
NVLink, PCIe peer-to-peer, and CPU-staged transfers - what actually connects the GPUs in your dedicated server.
Which GPU for Stable Diffusion vs LLM – The Split Workload Question
When you host both image and text models on one server, the GPU that wins one workload often loses the…
VRAM Per Pound Across the GigaGPU Lineup 2026
The single most useful chart when you are buying for a fixed VRAM requirement - pounds per gigabyte of usable…
vLLM Speculative Decoding Setup – Faster Tokens, Same Model
Speculative decoding uses a small draft model to speed up a larger one. Configured right on a dedicated GPU it…
vLLM Prefix Caching Performance Gains
Prefix caching reuses KV cache for repeated prompt prefixes. For RAG and few-shot workloads the speed-up is dramatic.
vLLM KV Cache Block Size Tuning
PagedAttention's block size controls memory fragmentation and throughput - the defaults are usually fine but not always.
vLLM Continuous Batching Tuning Guide
The three knobs that actually move vLLM throughput, how to measure their effect, and a tuning recipe for common workloads.
vLLM Chunked Prefill Configuration
Chunked prefill keeps decode latency stable when big prompts arrive during active serving - the right config for mixed workloads.
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