Frank
f650fff0aa
- Normalize 'VLLM' to 'vLLM' in prose across 6 docs (32 occurrences; wikilink targets untouched) - Remove duplicated Environment Variables block in 27B Dankclaw doc - Update stale model refs Qwen3-30B-A3B -> Qwen3.6-35B-A3B in example commands - Fix VLLM Index: -- separators to em-dashes; correct model names on links - README: document active services (ROCmFPX llama-server on Dankclaw, vLLM AWQ on Dankserver), replace broken [[docs/README.md]] link with docs/index.md
70 lines
1.7 KiB
Markdown
70 lines
1.7 KiB
Markdown
# Dankserver System Specs
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## Server Overview
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Dankserver is an NVIDIA GPU server for vLLM inference. The host runs Ubuntu 24.04 with an RTX 3090 and 62 GB of system RAM.
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## Hardware Specifications
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### CPU
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| Property | Value |
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|----------|-------|
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| Architecture | x86_64 |
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| Vendor | AuthenticAMD |
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| Model | AMD Ryzen 7 7700X |
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| Cores | 8 (1 socket) |
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| NUMA Nodes | 1 (node 0, 63503 MB) |
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### GPU
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| Property | Value |
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|----------|-------|
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| Model | NVIDIA GeForce RTX 3090 |
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| VRAM | 24576 MiB (24 GB) |
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| Driver | 595.84 |
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| CUDA Version | 13.2 |
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| CUDA Toolkit | 12.0 (nvcc) |
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### Memory
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| Property | Value |
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|----------|-------|
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| System RAM | 62 GiB |
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| Swap | 127 GiB |
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| /dev/shm | 32 GiB |
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### Storage
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| Filesystem | Size | Used | Avail | Use% |
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|------------|------|------|-------|------|
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| / (nvme0n1p2) | 915G | 729G | 140G | 84% |
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| /mnt/entropy (dm-0) | 91T | 86T | 910G | 99% |
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### OS and Software
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| Property | Value |
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|----------|-------|
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| OS | Ubuntu 24.04.4 LTS (Noble Numbat) |
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| Kernel | 7.0.0-28-generic |
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| Python | 3.12.3 |
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### Active Services
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A vLLM engine core (Podman container `vllm-qwen36-dflash`) occupies approximately 22 GB of the RTX 3090 VRAM (`nvidia-smi` reports `VLLM::EngineCore` at 22164 MiB).
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## vLLM Relevance
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- 24 GB VRAM limits model size. Use FP8 quantization for large models.
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- 62 GB system RAM provides ample swap space for KV cache offloading.
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- 127 GB swap supports large `--swap-space` values for CPU KV cache paging.
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- Root disk has approximately 140 GB free. Store models on /mnt/entropy.
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- CUDA 12.0 and Python 3.12.3 are compatible with vLLM.
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## See Also
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- [[VLLM Overview]]
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- [[Hybrid CPU GPU Processing Dankserver]]
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- [[RAM CPU Offloading in VLLM]]
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- [[Running Qwen3.6 35B A3B FP8 on Dankserver]]
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