Benchmarks, GPU comparisons, deployment guides, and cost analysis — everything you need to run AI on dedicated GPU servers.
Mistral Codestral 22B at AWQ-INT4 fits on a 24 GB RTX 4090 with very tight KV cache headroom. The deployment recipe and a clear-eyed look at when you should upgrade.
Fresh benchmarks, comparisons, and deployment guides from the GigaGPU team.
DeepSeek-Coder V2 Lite (16B MoE, 2.4B active) on a single RTX 4090 24 GB — VRAM math, vLLM config, real…
The RTX 4090 punches at roughly the same FP16 TFLOPS class as datacenter A100 cards. Here is the precise benchmark…
Whisper + Llama 3 + Kokoro TTS as a complete voice agent stack on a single RTX 5060 Ti 16…
How to fine-tune Llama 3 8B, Mistral 7B and Qwen 2.5 7B on a single RTX 5060 Ti 16 GB…
Different document types need different RAG strategies. PDF needs OCR, HTML needs cleanup, code needs syntax-aware chunking, tables need their…
Five leading open-weight embedding models compared on retrieval quality, multilingual coverage, and throughput. Pick by workload.
H100 is the datacenter king. RTX 5090 is the consumer flagship. For pure inference, the price gap matters more than…
What to do in the first 30 minutes of an AI inference incident — diagnostic order, common fixes, and when…
What team composition makes a self-hosted AI deployment work — ML engineer, infrastructure engineer, on-call rotation. Realistic for small teams.
Find exactly what you need — from GPU benchmarks to deployment tutorials.
AI Hosting & Infrastructure
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