Benchmarks, GPU comparisons, deployment guides, and cost analysis — everything you need to run AI on dedicated GPU servers.
How continuous batching dramatically improves GPU utilisation for LLM inference, with before/after benchmarks, vLLM configuration, and practical tuning for production workloads.
Fresh benchmarks, comparisons, and deployment guides from the GigaGPU team.
A visual breakdown of how context length scales VRAM usage for popular LLMs, comparing KV cache growth across LLaMA 3,…
Why per-token AI API pricing becomes unsustainable at scale — the mathematics of linear cost growth, hidden multipliers, and the…
Time to first token benchmarks across six GPUs for popular LLMs — p50, p90, and p99 latency at idle and…
Deploy llama.cpp on a dedicated GPU server for fast GGUF model inference. Covers GPU offloading, quantisation tiers, server mode configuration,…
Benchmark comparison of LLaMA 3 8B inference speed across GPTQ, AWQ, and GGUF quantisation formats on six GPUs, with VRAM…
Detailed VRAM usage for LLaMA 3 8B at different context lengths from 4K to 128K tokens, covering KV cache scaling…
LLaMA 3 8B throughput scaling from 1 to 64 concurrent requests across four GPUs — requests/sec, tokens/sec, and per-request latency…
How much VRAM does LLaMA 3 70B need at different context lengths? Full breakdown from 4K to 128K tokens with…
Deploy self-hosted legal AI for contract analysis, document review, and case research on dedicated GPU servers. Covers model selection, OCR…
Find exactly what you need — from GPU benchmarks to deployment tutorials.
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