AIPerf Long-Input and Extreme-Context Benchmarking

Session: 2026-06-18 — Full suite benchmark of 4 models (gemma-4-31b-it, gpt-oss-120b, moonshotai/kimi-k2.6, qwen3.6-27b-nvfp4) on ai.noris.de

Key Finding: Long Inputs > High Concurrency for Bottleneck Isolation

When evaluating maximum node capacity, testing conc=200+ is often operationally irrelevant because TTFT p99 exceeds acceptable thresholds (7s+ at conc=100). The real infrastructure bottleneck for RAG / long-context workloads is memory-bandwidth-bound prefill with long inputs.

Recommended alternative: Long-Input Prefill Test

Concurrency: Fixed low (e.g., 10) to avoid queueing artifacts
Input length: 1024 / 2048 / 4096 tokens (double/triple the standard 512)
Output length: Short (128) to isolate prefill from generation
Duration: 300s to amortize cold-start

Extreme Context Test (10k Input / 10k Output)

For stress-testing context-window limits and sustained generation capacity:

Input: 10,000 tokens
Output: 10,000 tokens
Concurrency: 1 (sequentially, because runtime is 200–1000 seconds per request)
Requests: 10 per model
Purpose: Validates the full KV-cache lifecycle and sustained decode performance

Expected runtime: At 10–50 tok/s output, one request takes 200–1000 seconds. Schedule accordingly.

Template:

schema_version: "2.0"
benchmark:
  models:
    items:
      - name: "vllm/MODEL_NAME"
  endpoint:
    urls: ["https://ai.noris.de/v1"]
    type: "chat"
    streaming: true
  datasets:
    - type: "synthetic"
      name: "main"
      entries: 10
      isl:
        type: "fixed"
        value: 10000
      osl:
        type: "fixed"
        value: 10000
  phases:
    - type: "concurrency"
      name: "profiling"
      requests: 10

Correct AIPerf 0.10.0 Synthetic Dataset Config (Long Input)

schema_version: "2.0"
benchmark:
  models:
    items:
      - name: "vllm/gemma-4-31b-it"
  endpoint:
    urls: ["https://ai.noris.de/v1"]
    type: "chat"
    streaming: true
  datasets:
    - name: "synthetic_long"
      type: "synthetic"
      prompt_template:
        content: "Analyze the following topic in depth and provide detailed insights."
      strategy:
        type: "fixed"
        max_total_tokens: 1152
        input_tokens: 1024
        output_tokens: 128
  phases:
    - type: "concurrency"
      name: "profiling"
      duration: 300

Critical YAML structure rules learned:

datasets MUST be a list (- name: ...), NOT a dict with default: key
name goes directly under the list item, NOT nested under a synthetic: key
prompt_template and strategy are siblings to name and type

Benchmark Results at conc=100 (Standard 512-in / 128-out)

Model	Input tok/s (total)	Output tok/s (total)	Total tok/s	TTFT p99
moonshotai-kimi-k2.6	5,115	1,270	6,385	3,926ms
gemma-4-31b-it	4,385	1,083	5,468	2,060ms
qwen3.6-27b-nvfp4	4,191	992	5,183	7,391ms
gpt-oss-120b	3,300	804	4,104	7,310ms

Prefill degradation @ conc=100: All models lose ~80% of per-user prefill speed vs conc=10.

MSP Service-Tier Perspective (User Preference)

When presenting for infrastructure/commercial decisions, structure per service tier not model comparison:

Single-User Experience (conc=1): TTFT p99, Output/User, SLA class
Multi-Tenant Load (conc=10→100): Throughput scaling, degradation curve
Steady-State: Long-run stability, drift detection
Service Recommendation: Positioning (Entry/Premium), suitable use cases

templates/aiperf-long-input-config.yaml — Working starter template
references/aiperf-suite-orchestrator-pattern.md — Full orchestration pattern
references/aiperf-token-metrics-explained.md — Token metric unit reference

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