serverAppConfigs/serverAppConfigs-docs/LLM/Hybrid CPU GPU Processing Dankserver.md

type, description, tags, timestamp

type	description	tags	timestamp
reference	Dankserver uses NVIDIA GPU with system RAM for hybrid processing.	llm	2026-08-28

Hybrid CPU GPU Processing Dankserver

Overview

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-radiance-qwen38-iq4r).

Architecture

Model Weights

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, read 2026-10-18). What remains true is the capacity arithmetic: offloaded weights cost PCIe traffic per token (see PCIe Link and Atomics §2).

KV Cache

KV cache moves between GPU and CPU:

1. Active KV cache pages stay in GPU VRAM
2. Inactive pages swap to CPU RAM
3. Swapped pages write to disk swap space if RAM is full
4. Pages reload to GPU when needed

Compute Distribution

• GPU handles all matrix multiplication and attention computation
• CPU handles tokenization, detokenization, and input processing
• CPU handles KV cache swap operations
• GPU worker processes run on CPU cores via multiprocessing

Optimal Settings for Dankserver

GPU Memory Utilization

Set --gpu-memory-utilization based on your GPU:

GPU	Recommended Value
24 GB (RTX 3090/4090)	0.7-0.8
40 GB (A100)	0.8-0.9
80 GB (A100 80GB)	0.85-0.95
80 GB (H100)	0.9-0.95

Dankserver has neither NVIDIA card today: it carries 2× 32 GB (32,624 MiB) R9700s. The live engine does not use this knob at all — it claims an explicit per-card pair instead (--vram-weights-mib 25600 / --vram-kv-mib 5120, scripts/frank-archon.env:230-231).

Lower values leave more GPU memory for KV cache. This reduces swap frequency.

Swap Space / KV Offload

Recent vLLM releases removed the --swap-space engine argument — CacheConfig on main has no swap_space field (source, 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.

System RAM	Recommended Swap
32 GB	8-16 GB
64 GB	16-32 GB
128 GB	32-64 GB
256 GB	64-128 GB

More swap space allows longer contexts and more concurrent requests.

Tensor Parallelism

Use tensor parallelism for large models:

vllm serve meta-llama/Llama-3.1-70B-Instruct \
    --tensor-parallel-size 4 \
    --gpu-memory-utilization 0.85

KV Cache Data Type

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).

vllm serve Qwen/Qwen3.6-35B-A3B-FP8 \
    --kv-cache-dtype fp8 \
    --gpu-memory-utilization 0.9

FP8 KV cache reduces memory usage by 50% compared to BF16.

NUMA Optimization

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:

vllm serve model \
    --tensor-parallel-size 4 \
    --numa-bind

This pins each GPU worker to its nearest NUMA node. This reduces memory access latency.

For explicit NUMA binding:

vllm serve model \
    --tensor-parallel-size 4 \
    --numa-bind \
    --numa-bind-nodes 0 0 1 1

CPU Resource Requirements

VLLM needs CPU cores for:

• 1 API server process
• 1 engine core process
• N GPU worker processes (one per GPU)

Minimum physical cores = 2 + N (where N is GPU count).

For Dankserver, which has 2 GPUs (2× R9700) and 8 cores (lscpu, read 2026-10-18):

• Minimum 4 physical CPU cores by the 2 + N rule
• Recommended 8+ physical CPU cores — the host has exactly 8 (SMT off)

Performance Tuning

Chunked Prefill

Chunked prefill is enabled by default. Tune with:

vllm serve model \
    --max-num-batched-tokens 8192

Higher values improve throughput. Lower values improve latency.

CUDA Graphs

CUDA graphs improve throughput. Disable for faster startup:

vllm serve model \
    --enforce-eager

Compilation Cache

Reuse torch.compile artifacts:

export VLLM_CACHE_ROOT=~/.cache/vllm
vllm serve model

Copy cache between boots for faster startup.

Skip Memory Profiling

Skip memory profiling on repeated boots:

vllm serve model \
    --kv-cache-memory <value_from_first_boot_log>

Monitoring

Check GPU memory:

rocm-smi

Check for preemption warnings in VLLM logs.

Check CPU utilization:

htop

Example Commands

Single GPU, Medium Model

vllm serve Qwen/Qwen3.6-35B-A3B \
    --gpu-memory-utilization 0.85 \
    --max-num-batched-tokens 4096 \
    --max-num-seqs 128

Single GPU, FP8 Model

vllm serve Qwen/Qwen3.6-35B-A3B-FP8 \
    --kv-cache-dtype fp8 \
    --gpu-memory-utilization 0.9 \
    --max-num-batched-tokens 8192

Multi-GPU, Large Model

vllm serve meta-llama/Llama-3.1-70B-Instruct \
    --tensor-parallel-size 4 \
    --gpu-memory-utilization 0.9 \
    --max-model-len 8192

See Also

• VLLM Overview
• VLLM Index
• RAM CPU Offloading in VLLM
• Shared Memory Architecture Dankclaw
• Running Qwen3.6 35B A3B FP8 on Dankserver