Frank
677b4af2a4
- SWAT from DFlash (see ngram-mod benchmarks below); draft model not loaded. Active spec: --spec-type ngram-mod n_min=48/n_max=64/n_match=24. - Update Dankserver specs, deployment.md, DFlash doc to ngram-mod. - VRAM footprint: 14 GB (was 20 GB), ~10 GB free headroom.
69 lines
2.4 KiB
Markdown
69 lines
2.4 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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The active `frank-default-small` service (llama.cpp, `b10108+`) serves the HauhauCS Qwen3.6-35B-A3B uncensored `Q6_K_P` model on port 7770 with **ngram-mod** speculative decoding. It runs 3 parallel slots at 200000 context each (pool 600000). All 40 layers' MoE expert weights sit on CPU (`--n-cpu-moe 40`) while non-expert tensors and the full-attention KV cache stay on GPU (`--gpu-layers all`, KV K=`q8_0`, V=`f16`). It uses batch 3072 / ubatch 1536 with `--ctx-checkpoints` and `--no-mmap`. At load it uses about 14 GB of the 24 GB VRAM and about 32 GB of RAM.
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## Hardware Relevance
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- 24 GB VRAM limits the GPU-resident footprint. With 3 x 200000 context plus batch 3072/1536 activation buffers, the non-expert weights and KV cache consume about 14 GB, leaving about 10 GB of headroom. MoE experts stay on CPU; `--no-mmap` preloads expert tensors into RAM, eliminating decode page faults (uses ~32 GB of the 62 GB available).
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- 62 GB system RAM holds the ~29 GB of MoE expert weights with room to spare (~57 GB available after load).
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- 127 GB swap is available for CPU KV cache paging if GPU KV offload is disabled.
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- Root disk has approximately 140 GB free. Store the GGUF models under `/home/dank/models` (about 32 GB total).
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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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