GPU Hosting & AI Engineering Blog
Benchmarks, GPU comparisons, deployment guides, and cost analysis — everything you need to run AI on dedicated GPU servers.
Virtual GPU Partitioning for Inference – Options and Tradeoffs
vGPU, MIG, MPS, and plain CUDA device selection - a plain-English guide to how GPUs get sliced up for multi-workload serving.
Latest Articles
Fresh benchmarks, comparisons, and deployment guides from the GigaGPU team.
Two RTX 6000 Pro Architecture Patterns
192GB of VRAM across two cards. The serving patterns that justify this much capacity and the ones that do not.
Triton Inference Server Configuration for GPU Workloads
Nvidia's Triton Inference Server serves more than LLMs - vision, audio, ensembles. Configuring it correctly on a dedicated GPU is…
TGI Max Batch Prefill Tokens Tuning
Hugging Face Text Generation Inference's prefill batching parameter is the single most impactful knob you can tune for long-prompt workloads.
Tensor Parallelism vs Pipeline Parallelism on Dedicated GPU Servers
The two ways to split a large model across multiple GPUs. When to use which, with concrete numbers from vLLM…
TDP and Power Draw Across the GigaGPU Lineup
Every GPU we host, ranked by total power draw, with the implications for hosting cost, cooling, and tokens per watt.
Splitting Embedding and LLM Across Two GPUs
In a RAG stack the embedder and the LLM compete for VRAM and compute. Putting them on different cards solves…
Single RTX 6000 Pro vs Four RTX 4060 Ti – Grid vs Monolith
One big 96GB card versus four 16GB cards totaling 64GB - which topology wins for varied AI workloads?
SGLang vs vLLM in 2026 – Production Comparison
Both engines claim best-in-class throughput. Running them side-by-side on identical hardware reveals where each actually wins.
Scaling vLLM Across Two GPUs – What Actually Changes
Moving from single-GPU vLLM to two-GPU tensor parallel changes throughput, latency, memory layout, and a few knobs you will not…
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