tftsr_ai/roles/ollama

Role: ollama

Purpose

Install, configure, and maintain Ollama inference server(s) on the AI server host. Two instances run simultaneously — one per NUMA socket — to utilize both CPU sockets on the Dell M630 (2× E5-2690v4).

Instances

Service	Port	NUMA Node	CPUs (physical only)	RAM binding	Purpose
ollama.service	11434	Node 1	1 3 5 … 27 (odd)	--membind=1	General models
ollama-node0.service	11435	Node 0	0 2 4 … 26 (even)	--membind=0	Coding models

Both instances share the same model storage directory (/mnt/ai_data/ollama_models) and Ollama API key. Weights are loaded once into the NUMA node's memory; they are not duplicated between instances.

Configuration

Node 1 — systemd override

Applied via /etc/systemd/system/ollama.service.d/override.conf (templated from templates/ollama/override.conf.j2):

Variable	Value	Description
OLLAMA_API_KEY	(from Vault)	Shared key for all API requests
OLLAMA_HOST	0.0.0.0:11434	Listen on all interfaces, port 11434
OLLAMA_MODELS	/mnt/ai_data/ollama_models	Shared model storage
OLLAMA_KEEP_ALIVE	-1	Never unload models from RAM
OLLAMA_FLASH_ATTENTION	1	Fused softmax — ~20% less memory bandwidth
OLLAMA_NUM_THREADS	14	Physical cores on NUMA node 1 only
OLLAMA_NUM_PARALLEL	2	Concurrent inference streams per instance
OLLAMA_MAX_LOADED_MODELS	3	3 models warm per instance (6 total)
CPUAffinity	1 3 5 … 27	Odd CPUs = socket 1 physical cores
ExecStart	numactl --membind=1 ollama serve	Pin memory allocations to Node 1 RAM

Node 0 — standalone systemd unit

Deployed to /etc/systemd/system/ollama-node0.service (from templates/ollama/ollama-node0.service.j2). Uses the same variables but with:

Variable	Value
OLLAMA_HOST	0.0.0.0:11435
CPUAffinity	0 2 4 … 26
ExecStart	numactl --membind=0 ollama serve

NUMA Rationale

On the M630 with dual E5-2690v4:

• Node 1 (odd CPUs) has ~120 GB free RAM — assigned general models (larger)
• Node 0 (even CPUs) has ~75 GB free RAM — assigned coding models

Without numactl --membind, the OS allocates model weights and KV cache across both nodes, causing cross-socket memory traffic (~40 GB/s vs ~68–75 GB/s local). CPUAffinity alone sets the scheduler; numactl sets the memory policy.

OLLAMA_FLASH_ATTENTION

Enables fused softmax kernel — reduces attention memory bandwidth by ~20% and improves throughput at all context lengths on AVX2 (E5-2690v4). Note: OLLAMA_KV_CACHE_TYPE is intentionally not set — q8_0 dequantization overhead regressed throughput on this CPU despite the bandwidth savings.

Upgrade Procedure

ansible-playbook playbooks/02_infrastructure.yml -K -e @local.yml --tags ollama

The official install script detects the existing installation and performs an in-place upgrade. Both ollama.service and ollama-node0.service are restarted.

Tags

ansible-playbook playbooks/site.yml --tags ollama -K -e @local.yml