GPU Hosting & AI Engineering Blog
Benchmarks, GPU comparisons, deployment guides, and cost analysis — everything you need to run AI on dedicated GPU servers.
LangChain Agents vs LlamaIndex Agents
Comparing LangChain agents with LlamaIndex agents for building AI applications. Tool use, RAG integration, production readiness, and when each framework delivers better results.
Latest Articles
Fresh benchmarks, comparisons, and deployment guides from the GigaGPU team.
AutoGen vs CrewAI vs LangGraph: 2026
Comparing AutoGen, CrewAI, and LangGraph for building multi-agent AI systems in 2026. Architecture patterns, ease of use, production readiness, and…
PagedAttention vs Standard KV Cache
Comparing PagedAttention memory management with standard contiguous KV cache allocation for LLM inference. Memory efficiency, throughput gains, and why PagedAttention…
Speculative Decoding vs Continuous Batching
Comparing speculative decoding and continuous batching for LLM inference optimisation. How each technique improves different metrics, and when to use…
KV Cache vs Model Quantization: What to Compress
Comparing KV cache compression and model weight quantisation for reducing LLM memory usage. When to compress the cache, when to…
FP16 vs FP8 vs INT4: Precision vs Speed
Comparing FP16, FP8, and INT4 precision formats for LLM inference. Throughput benchmarks, quality impact, VRAM requirements, and GPU hardware compatibility…
AWQ vs GPTQ vs GGUF vs EXL2: 2026 Guide
Comparing AWQ, GPTQ, GGUF, and EXL2 quantisation formats for LLM inference in 2026. Speed benchmarks, quality retention, framework support, and…
Edge AI vs Centralized GPU Inference
Edge AI inference on local devices versus centralised GPU server inference. Comparing latency profiles, model size constraints, cost structures, and…
TGI vs Ollama: Production vs Development Serving
Hugging Face TGI versus Ollama for LLM serving. Compare production-grade features against development simplicity and learn where each tool belongs…
Colocation vs Dedicated vs Cloud GPU
Comparing GPU colocation, dedicated GPU servers, and cloud GPU instances for AI workloads. Ownership models, control levels, and cost structures…
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