Frank
219a097b39
Every payload swap a live endpoint has ever run is now one row in that endpoint's Eras table, with its rollback path in the same row. No per-attempt page, no per-swap ledger, nowhere else. - Added: LLM/frank-archon.md (radiance, 2x R9700 TP=2), LLM/frank-dragoon.md (halogen flash-server, 19 eras), LLM/frank-zealot.md (llama.cpp PrismML fork on the 3090). Each carries Current shape, Eras, Laws, wire contract, files. - Deleted: 45 per-payload runbooks -- the frank-dragoon line (11 including the pre-name halogen/kingjones/kairic/CIRU/eemin/agentionai/dflash2 pages and the benchmark page), the frank-zealot line (llama.cpp/vLLM/ik_llama/SGLang eras), the frank-archon line (vLLM/FreeToken/R9V/llama.cpp/radiance eras), plus frank-medium, frank-npu and the ubatch/PCIe one-offs that owned nothing. - Rewired: every wikilink and markdown-path reference to a folded page points at the endpoint page instead; the three Research see-also lists and the ubatch sweep's runbook link included. No anchored links existed in the set. - Regenerated both index pages as pure locators -- one row, one clause, no numbers -- so the docs_completeness locator law holds for all 153 pages. tests/docs_completeness.bats green: index links every page, no unresolved links. The pages moved to .AGENTTRASH/20261018-doc-dedupe/LLM/ (gitignored, and git history holds every byte).
223 lines
6.1 KiB
Markdown
223 lines
6.1 KiB
Markdown
---
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type: reference
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description: "Dankserver uses NVIDIA GPU with system RAM for hybrid processing."
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tags:
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- llm
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timestamp: 2026-08-28
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---
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# Hybrid CPU GPU Processing Dankserver
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## Overview
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Dankserver has **no NVIDIA GPU**: the RTX 3090 left the box on 2026-09-15 and the host now runs two AMD Radeon AI PRO R9700s (`lspci | grep -ci nvidia` → 0, read 2026-10-18 — [[Dankserver System Specs]]). This page's vLLM-era hybrid guidance is reference material, not the serving path: the live service is radiance 1.0.13 as `frank-archon`, and its hybrid mechanism is a pinned host pool (43.88 GiB per rank, `scripts/frank-archon.env:184`) plus the CPU-resident 47.68 GiB n-gram/PLE table, not vLLM KV swapping ([[frank-archon]]).
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## Architecture
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### Model Weights
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In the setup this page describes, model weights stay on GPU and the GPU must hold all parameters. As a statement about vLLM it is no longer true: current vLLM ships CPU weight offload as `cpu_offload_gb`, plus the grouped prefetch path (`offload_group_size`) — `vllm/config/offload.py:23,54` ([source](https://raw.githubusercontent.com/vllm-project/vllm/main/vllm/config/offload.py), read 2026-10-18). What remains true is the capacity arithmetic: offloaded weights cost PCIe traffic per token (see [[PCIe Link and Atomics]] §2).
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### KV Cache
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KV cache moves between GPU and CPU:
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1. Active KV cache pages stay in GPU VRAM
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2. Inactive pages swap to CPU RAM
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3. Swapped pages write to disk swap space if RAM is full
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4. Pages reload to GPU when needed
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### Compute Distribution
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- GPU handles all matrix multiplication and attention computation
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- CPU handles tokenization, detokenization, and input processing
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- CPU handles KV cache swap operations
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- GPU worker processes run on CPU cores via multiprocessing
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## Optimal Settings for Dankserver
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### GPU Memory Utilization
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Set `--gpu-memory-utilization` based on your GPU:
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| GPU | Recommended Value |
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|-----|-------------------|
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| 24 GB (RTX 3090/4090) | 0.7-0.8 |
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| 40 GB (A100) | 0.8-0.9 |
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| 80 GB (A100 80GB) | 0.85-0.95 |
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| 80 GB (H100) | 0.9-0.95 |
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Dankserver has neither NVIDIA card today: it carries 2× 32 GB (32,624 MiB) R9700s.
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The live engine does not use this knob at all — it claims an explicit per-card
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pair instead (`--vram-weights-mib 25600` / `--vram-kv-mib 5120`,
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`scripts/frank-archon.env:230-231`).
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Lower values leave more GPU memory for KV cache. This reduces swap frequency.
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### Swap Space / KV Offload
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Recent vLLM releases removed the `--swap-space` engine argument — `CacheConfig` on `main` has no `swap_space` field ([source](https://raw.githubusercontent.com/vllm-project/vllm/main/vllm/config/cache.py), read 2026-10-18). Use `--kv-offloading-size` (`kv_offloading_size`, same file) to move KV cache to CPU RAM. In Podman, mount the host cache read-write for KV offload writes. The table below shows historical `--swap-space` guidance as a sizing reference.
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| System RAM | Recommended Swap |
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|------------|------------------|
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| 32 GB | 8-16 GB |
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| 64 GB | 16-32 GB |
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| 128 GB | 32-64 GB |
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| 256 GB | 64-128 GB |
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More swap space allows longer contexts and more concurrent requests.
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### Tensor Parallelism
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Use tensor parallelism for large models:
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```
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vllm serve meta-llama/Llama-3.1-70B-Instruct \
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--tensor-parallel-size 4 \
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--gpu-memory-utilization 0.85
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```
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### KV Cache Data Type
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Use FP8 KV cache for Ampere and newer NVIDIA GPUs. On this host's RDNA4 cards the live engine runs an fp8 KV cache too (`kv fp8` — `scripts/frank-archon.env:264`, which also records that this engine takes only bf16|fp8 for the whole cache).
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```
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vllm serve Qwen/Qwen3.6-35B-A3B-FP8 \
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--kv-cache-dtype fp8 \
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--gpu-memory-utilization 0.9
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```
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FP8 KV cache reduces memory usage by 50% compared to BF16.
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## NUMA Optimization
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Dankserver reports **one** NUMA node today (`lscpu` → `NUMA node(s): 1`, `NUMA node0 CPU(s): 0-7`, read 2026-10-18), so NUMA binding buys nothing on this host; these flags matter on multi-node topologies:
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```
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vllm serve model \
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--tensor-parallel-size 4 \
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--numa-bind
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```
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This pins each GPU worker to its nearest NUMA node. This reduces memory access latency.
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For explicit NUMA binding:
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```
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vllm serve model \
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--tensor-parallel-size 4 \
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--numa-bind \
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--numa-bind-nodes 0 0 1 1
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```
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## CPU Resource Requirements
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VLLM needs CPU cores for:
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- 1 API server process
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- 1 engine core process
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- N GPU worker processes (one per GPU)
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Minimum physical cores = 2 + N (where N is GPU count).
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For Dankserver, which has **2** GPUs (2× R9700) and 8 cores (`lscpu`, read
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2026-10-18):
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- Minimum 4 physical CPU cores by the 2 + N rule
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- Recommended 8+ physical CPU cores — the host has exactly 8 (SMT off)
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## Performance Tuning
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### Chunked Prefill
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Chunked prefill is enabled by default. Tune with:
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```
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vllm serve model \
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--max-num-batched-tokens 8192
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```
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Higher values improve throughput. Lower values improve latency.
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### CUDA Graphs
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CUDA graphs improve throughput. Disable for faster startup:
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```
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vllm serve model \
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--enforce-eager
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```
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### Compilation Cache
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Reuse torch.compile artifacts:
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```
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export VLLM_CACHE_ROOT=~/.cache/vllm
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vllm serve model
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```
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Copy cache between boots for faster startup.
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### Skip Memory Profiling
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Skip memory profiling on repeated boots:
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```
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vllm serve model \
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--kv-cache-memory <value_from_first_boot_log>
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```
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## Monitoring
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Check GPU memory:
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```
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rocm-smi
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```
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Check for preemption warnings in VLLM logs.
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Check CPU utilization:
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```
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htop
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```
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## Example Commands
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### Single GPU, Medium Model
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```
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vllm serve Qwen/Qwen3.6-35B-A3B \
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--gpu-memory-utilization 0.85 \
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--max-num-batched-tokens 4096 \
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--max-num-seqs 128
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```
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### Single GPU, FP8 Model
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```
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vllm serve Qwen/Qwen3.6-35B-A3B-FP8 \
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--kv-cache-dtype fp8 \
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--gpu-memory-utilization 0.9 \
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--max-num-batched-tokens 8192
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```
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### Multi-GPU, Large Model
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```
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vllm serve meta-llama/Llama-3.1-70B-Instruct \
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--tensor-parallel-size 4 \
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--gpu-memory-utilization 0.9 \
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--max-model-len 8192
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```
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## See Also
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- [[VLLM Overview]]
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- [[VLLM Index]]
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- [[RAM CPU Offloading in VLLM]]
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- [[Shared Memory Architecture Dankclaw]]
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- [[Running Qwen3.6 35B A3B FP8 on Dankserver]]
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