Benchmarks, GPU comparisons, deployment guides, and cost analysis — everything you need to run AI on dedicated GPU servers.
01.ai's Yi 34B delivers strong bilingual performance and long context. On a 96GB card it runs at FP16 with serious concurrency headroom.
Fresh benchmarks, comparisons, and deployment guides from the GigaGPU team.
Force the model to emit valid JSON, a regex, or a choice from a set. vLLM supports three backends with…
Two vLLM parameters jointly decide how much concurrency your dedicated GPU can sustain. Get them wrong and you leave half…
A compressed reference to the vLLM engine flags that matter in production, grouped by what they actually affect.
Unsloth's optimised kernels let you fine-tune 8B-class models on a single 16GB card with surprising throughput. Here is the setup.
Hugging Face TRL's SFTTrainer is the vanilla fine-tuning API that every framework wraps. Using it directly gives you full control.
TGI supports half a dozen quantization formats with different flags, precision, and supported architectures - a cheat sheet for each…
oobabooga's text-generation-webui is often dismissed as a toy. Configured properly it is a legitimate production API on a dedicated GPU.
BigCode's StarCoder 2 15B is a permissively-licensed coding model that fits a 16GB card and handles 600+ languages.
Upstage's Solar 10.7B uses depth up-scaling to get 13B-class performance in a smaller footprint - fits a 16GB card at…
Find exactly what you need — from GPU benchmarks to deployment tutorials.
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