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chore: async inference store write (#3318)

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# What does this PR do?


## Test Plan
```
cd /docs/source/distributions/k8s-benchmark
# start mock server
python openai-mock-server.py --port 8000
# start stack server
uv run --with llama-stack python -m llama_stack.core.server.server docs/source/distributions/k8s-benchmark/stack_run_config.yaml
# run benchmark script
uv run python3 benchmark.py --duration 30 --concurrent 50 --base-url=http://localhost:8321/v1/openai/v1 --model=vllm-inference/meta-llama/Llama-3.2-3B-Instruct
```
Before:

============================================================
BENCHMARK RESULTS
============================================================
Total time: 30.00s
Concurrent users: 50
Total requests: 1267
Successful requests: 1267
Failed requests: 0
Success rate: 100.0%
Requests per second: 42.23


After:

============================================================
BENCHMARK RESULTS
============================================================
Total time: 30.00s
Concurrent users: 50
Total requests: 1449
Successful requests: 1449
Failed requests: 0
Success rate: 100.0%
Requests per second: 48.30
This commit is contained in:
ehhuang 2025-09-04 11:37:46 -07:00 • committed by GitHub
parent 5bbca56cfc
commit bcc7f2c7d0
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2 changed files with 10 additions and 2 deletions
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8
docs/source/distributions/k8s-benchmark/stack_run_config.yaml
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@ -3,6 +3,7 @@ image_name: kubernetes-benchmark-demo
apis:
- agents
- inference
- safety
- telemetry
- tool_runtime
- vector_io
@ -30,6 +31,11 @@ providers:
db: ${env.POSTGRES_DB:=llamastack}
user: ${env.POSTGRES_USER:=llamastack}
password: ${env.POSTGRES_PASSWORD:=llamastack}
safety:
- provider_id: llama-guard
provider_type: inline::llama-guard
config:
excluded_categories: []
agents:
- provider_id: meta-reference
provider_type: inline::meta-reference
@ -95,6 +101,8 @@ models:
- model_id: ${env.INFERENCE_MODEL}
provider_id: vllm-inference
model_type: llm
shields:
- shield_id: ${env.SAFETY_MODEL:=meta-llama/Llama-Guard-3-1B}
vector_dbs: []
datasets: []
scoring_fns: []