# Kingy.ai 24GB comparison hardware/runtime manifest

Evidence captured: 2026-08-17T03:59:40Z

## Physical test system

- GPU: NVIDIA GeForce RTX 4090
- GPU UUID: `GPU-528ee2e7-2cd2-ee57-80b5-3280034175cc`
- VBIOS: `95.02.3C.00.EB`
- Reported VRAM: 24,564 MiB
- Display: disabled
- Power limit: 450 W
- PCIe: generation 4 current/max; x8 current, x16 maximum
- CPU: AMD Ryzen 9 7950X, 16 cores / 32 threads
- System RAM: 134,123,208,704 bytes; no swap
- Storage exposed to container: WD_BLACK SN850X 4TB NVMe plus INTEL SSDSC2KB24 240GB SATA device

## Software

- OS: Ubuntu 22.04.3 LTS
- Kernel: Linux 6.8.0-85-generic x86_64
- NVIDIA driver: 570.195.03
- CUDA toolkit: 11.8, nvcc V11.8.89
- llama.cpp release: b10453
- llama.cpp commit: `3cb7ffb1a1f612d5e4a46244ae5a3c77ad934a70`
- llama.cpp version string: `0.1.0-dev (build 10453, commit 3cb7ffb1a)`
- Compiler: GNU 11.4.0
- Backend: CUDA, compute architecture 89
- Build flags: `-DGGML_CUDA=ON -DLLAMA_CURL=ON -DCMAKE_BUILD_TYPE=Release -DCMAKE_CUDA_ARCHITECTURES=89`

## Core runtime settings

- Full GPU offload: `--n-gpu-layers all` for server tests; `999` for llama-bench
- Flash attention: on
- Main equal-Q4 K/V cache: F16/F16
- Best-fit Gemma 4 64K K/V cache: Q8_0/Q8_0
- Batch / micro-batch: 2,048 / 512
- Threads / batch threads: 16 / 16
- Parallel slots: 1
- Text-only lanes: no multimodal projector loaded
- Core quality lane: speculative decoding disabled
- Controlled non-thinking: temperature 0, fixed seed
- Controlled thinking: temperature 1.0, top-p 0.95, medium reasoning effort, seeds 20260816/17/18

## Artifact hashes

See `work/evidence/sources/artifacts.sha256`; all seven downloaded files matched the predeclared SHA-256 values before testing.

## Interpretation note

The host exposed the RTX 4090 at PCIe 4.0 x8 during capture. Results are for this one physical system and pinned runtime; they are not universal numbers for every RTX 4090 or every 24GB GPU.
