GPU Hosting & AI Engineering Blog
Benchmarks, GPU comparisons, deployment guides, and cost analysis — everything you need to run AI on dedicated GPU servers.
Ollama num_parallel and num_queue Tuning
Two Ollama environment variables control how many requests run in parallel versus queue. Defaults crash under moderate traffic.
Latest Articles
Fresh benchmarks, comparisons, and deployment guides from the GigaGPU team.
Ollama Keep-Alive and Model Memory Tuning
Ollama unloads models from VRAM after idle. Adjust keep_alive to avoid cold-start latency or to share a GPU between models…
Nemotron 70B Self-Hosted
Nvidia's Nemotron 70B extends Llama 3.1 70B with RLHF and domain tuning. Hosting is similar to stock Llama 70B but…
Molmo 7B Self-Hosted Vision-Language Model
Allen AI's Molmo 7B is a compact, trained-from-scratch VLM with particularly strong pointing and counting capabilities.
Mixtral 8x22B on a Dedicated GPU
Mistral's Mixtral 8x22B is a 141B total / 39B active MoE that needs serious VRAM - but quantised it fits…
Mistral Small 3 Self-Hosted Deployment
Mistral's 24B Small 3 refresh lands between the 7B and 70B class with genuinely strong benchmarks and fits a single…
Mistral Nemo 12B on a Dedicated GPU
Mistral Nemo 12B offers 128k context on a single mid-tier card - the practical long-context model for dedicated GPU hosting.
LoRA Fine-Tuning Mistral 7B on a Dedicated GPU
LoRA at FP16 works comfortably on a 24GB GPU for Mistral 7B - the fastest practical path to a fine-tuned…
llama.cpp Server Thread Tuning for Dedicated GPUs
llama.cpp exposes five thread-related knobs that interact in non-obvious ways. Getting them right doubles throughput on some dedicated configurations.
llama.cpp n-gpu-layers Tuning for Mixed Inference
-ngl controls how many transformer layers live on the GPU. Picking the right number balances speed against VRAM - with…
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