Benchmarks, GPU comparisons, deployment guides, and cost analysis — everything you need to run AI on dedicated GPU servers.
Monthly cost, throughput, MAU break-even and 12-month TCO of running Llama 3.1 70B AWQ INT4 on a single RTX 4090 24GB dedicated server.
Fresh benchmarks, comparisons, and deployment guides from the GigaGPU team.
Exhaustive memory math, max-model-len calculation, FP8 KV essentials, max-num-seqs trade-offs and a real chat session for Llama 3.1 70B AWQ…
Comprehensive RTX 4090 24GB Llama 3.1 70B AWQ INT4 benchmark - 23 t/s decode, 16k workable context, 110 t/s aggregate…
Deep Llama 3.1 8B benchmark on the RTX 4090 24GB across six quantisations, batch 1 to 64, full TTFT curve,…
A senior engineer's tour of the RTX 4090's 1008 GB/s GDDR6X bus, the 72 MB Ada L2 cache, the bandwidth-bound…
A senior infra engineer's tour of how Ada Lovelace's 4th-generation tensor cores execute FP8 (E4M3 and E5M2) natively on the…
A creative studio backend on the RTX 4090 24GB: SDXL, FLUX.1-dev FP8, SD 1.5 + LoRAs and ControlNet pipelines. 12-engineer…
Hermes 3 8B on the RTX 4090 24GB - 195 t/s FP8, 1,140 t/s aggregate, 99.1% tool-call adherence, full 128k…
Gemma 2 9B-it on the RTX 4090 24GB - FP8 weights at 9.5GB, 155 tokens/sec, with full notes on sliding-window…
Gemma 2 27B AWQ INT4 fits the RTX 4090 24GB at ~14GB - 75 tokens/sec at 8k context with full…
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