llm-grill 0.1.1

CLI for benchmarking LLM inference servers (vLLM, SGLang, llama.cpp)

llm-grill

CLI for benchmarking LLM inference servers: vLLM, SGLang, llama.cpp, LiteLLM.

Measures TTFT, TPOT, end-to-end latency, throughput, success rate, KV cache quality metrics, and load ramp (breaking-point detection) on multi-turn conversation scenarios.

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Install

Requires Python 3.11+ and uv.

uv tool install llm-grill

Verify:

llm-grill --version

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Quick start

Copy the example scenario and adapt it to your setup:

cp scenarios/example.yaml scenarios/my-bench.yaml
# Edit URLs, model name, and API key

1. Check connectivity

llm-grill ping scenarios/my-bench.yaml

2. Run a benchmark

llm-grill run scenarios/my-bench.yaml --output results.jsonl

After the run, tables are printed automatically:

• Benchmark Summary — latency, throughput, success rate per server/model
• Conversation Quality Metrics — KV cache hit rate, turn-to-turn latency ratio, context growth factor
• Load Ramp Results — (if ramp_levels is set) one row per (server, model, concurrency level)

3. Generate a report from an existing results file

# Terminal table (summary + conversation metrics)
llm-grill report results.jsonl

# JSON (both sections, pipeable)
llm-grill report results.jsonl --format json

# CSV (raw requests, pandas-ready)
llm-grill report results.jsonl --format csv --output summary.csv

# Hide conversation metrics table
llm-grill report results.jsonl --no-conversations

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Commands

Command	Description
llm-grill run <scenario>	Run a benchmark, stream results to JSONL
llm-grill ping <scenario>	Test server connectivity
llm-grill show-scenario <scenario>	Validate and display a scenario
llm-grill report <results.jsonl>	Generate a report from a results file

run options

Option	Default	Description
--output / -o	results-<name>.jsonl	Output file path
--format / -f	jsonl	jsonl or csv
--quiet / -q	off	Suppress progress and tables

report options

Option	Default	Description
--format / -f	table	table, json, or csv
--output / -o	—	Output path for CSV format
--no-conversations	off	Hide the conversation metrics table

Global options

Option	Description
--verbose / -v	Enable debug logging
--version / -V	Print version and exit

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Supported backends

Backend	Type	Metrics source	Notes
vLLM	vllm	Prometheus /metrics	KV cache usage
SGLang	sglang	Prometheus /metrics	Cache hit rate
llama.cpp	llamacpp	/health endpoint	GGUF models
LiteLLM	litellm	Gateway routing	Proxy for multiple backends
OpenAI-compatible	openai	—	Reuses vLLM client

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Scenario format (YAML)

name: my-scenario
description: Optional description

backends:
  - name: gpu-vllm
    url: http://gpu-vllm:8000
    api_key: none                    # "none", a literal key, or ${ENV_VAR}
    type: vllm                       # vllm | sglang | llamacpp | litellm | openai
    timeout: 120.0

models:
  - name: devstral-small-2-24b
    max_tokens: 512
    temperature: 0.0

conversations:
  - name: multi-turn-debug
    turns:
      - role: system
        content: "You are an expert developer."
      - role: user
        content: "My FastAPI app returns 500 errors under load. What should I check?"
      - role: user
        content: "The DB connection pool is exhausted. How do I configure it in SQLAlchemy?"

targets:
  - backend: gpu-vllm
    model: devstral-small-2-24b
    conversation: multi-turn-debug

load:
  concurrent_users: 10
  iterations: 3
  ramp_up_seconds: 5.0
  think_time_seconds: 0.0

Each role: user turn triggers an inference request. Conversation history (including assistant responses) is carried forward, so the server sees a growing context.

Load ramp

Add ramp_levels to sweep concurrency levels in a single run. When set, concurrent_users is ignored.

load:
  iterations: 3
  ramp_levels: [1, 5, 10, 20, 50, 100]
  ramp_pause_seconds: 10.0   # pause between levels, default 10 s
  think_time_seconds: 0.0

Results are tagged with concurrent_users_level in the JSONL output and displayed in a Load Ramp Results table sorted by (server, model, users).

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Metrics

Latency & throughput

Metric	Description
TTFT	Time to First Token — from request sent to first token received (client-side, includes network)
TPOT	Time Per Output Token — (E2E - TTFT) / (completion_tokens - 1)
E2E latency	Total time from request to last token
tokens/s	completion_tokens / E2E latency (per request) or total across all requests / benchmark duration
success rate	% of requests completed without error

t0       → request sent
t_first  → first non-empty content chunk received
t_last   → stream ends ([DONE] or connection close)

TTFT   = t_first - t0
E2E    = t_last  - t0
TPOT   = (E2E - TTFT) / max(completion_tokens - 1, 1)

Measurement includes network round-trip. For cross-server comparisons, run from the same network location.

Conversation quality (multi-turn)

Computed per (server, model, conversation) group:

Metric	Description	Interpretation
Turn-to-Turn Ratio	mean(TTFT turn > 0) / mean(TTFT turn 0)	< 1 → KV cache reduces prefill time
Context Growth Factor	mean(E2E last turn) / mean(E2E first turn)	> 1 → latency increases with context
KV Cache Hit Rate	Prompt tokens served from cache	SGLang only (Prometheus)
KV Cache Usage	GPU KV cache capacity used	vLLM only (Prometheus)

GPU monitoring

Enable per-backend GPU metrics (utilization, memory, temperature, power) collected via SSH:

backends:
  - name: gpu-vllm
    url: http://gpu-vllm:8000
    type: vllm
    gpu_monitoring: true
    ssh_host: gpu-vllm       # defaults to URL host if omitted
    ssh_user: root            # default

Requires nvidia-smi on the target host and SSH key-based access.

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Output format (JSONL)

One JSON object per request, written incrementally:

{
  "scenario": "my-scenario",
  "target_server": "gpu-vllm",
  "target_model": "devstral-small-2-24b",
  "conversation": "multi-turn-debug",
  "turn": 1,
  "iteration": 0,
  "user_id": 3,
  "timestamp_start": "2026-03-10T14:00:00+00:00",
  "ttft_s": 0.142,
  "tpot_s": 0.018,
  "e2e_latency_s": 1.23,
  "prompt_tokens": 45,
  "completion_tokens": 64,
  "tokens_per_second": 52.0,
  "success": true,
  "error": null,
  "kv_cache_usage": 0.34,
  "requests_running": 8.0,
  "concurrent_users_level": 10
}

The file is valid even if the benchmark is interrupted — each line is a complete record.

Read with pandas:

df = pd.read_json("results.jsonl", lines=True)
df.groupby("target_server")[["ttft_s", "e2e_latency_s", "tokens_per_second"]].mean()

Read with polars:

df = pl.read_ndjson("results.jsonl")
df.group_by("target_server").agg(pl.col("ttft_s").mean())

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API keys

Use ${ENV_VAR} syntax to read from environment variables at load time:

backends:
  - name: gateway
    url: http://my-litellm-proxy:4000
    api_key: ${LITELLM_API_KEY}
    type: litellm

export LITELLM_API_KEY="sk-..."
llm-grill run scenarios/my-scenario.yaml

Never commit literal API keys in scenario files.

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LiteLLM gateway routing

When backends are behind a LiteLLM proxy, define one backend entry for the gateway and use model aliases to route:

backends:
  - name: gateway
    url: http://my-litellm-proxy:4000
    api_key: ${LITELLM_API_KEY}
    type: litellm

models:
  - name: devstral-small-llama    # LiteLLM alias → llama.cpp
    max_tokens: 512
  - name: devstral-small-vllm     # LiteLLM alias → vLLM
    max_tokens: 512

targets:
  - backend: gateway
    model: devstral-small-llama
    conversation: short-code-question
  - backend: gateway
    model: devstral-small-vllm
    conversation: short-code-question

Aliases must match model_name values in LiteLLM's config.yaml.

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Troubleshooting

Problem	Fix
ModuleNotFoundError: llm_grill	Run make install
ValidationError on scenario load	Run llm-grill show-scenario file.yaml for details
TTFT always < 1 ms	Server not streaming — check stream: true support
All requests connection refused	Run llm-grill ping file.yaml — check URL/port
401 Unauthorized	Set api_key: ${MY_VAR} and export the variable
ping times out on LiteLLM	LiteLLM /health does live inference — check gateway URL

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Contributing

See CONTRIBUTING.md.

License

Apache 2.0 — see LICENSE.