intel · MegaStood · Mar 20, 2026 · Mar 25, 2026 · Mar 25, 2026 · Mar 25, 2026
diff --git a/LUNAR_LAKE_COMPATIBILITY.md b/LUNAR_LAKE_COMPATIBILITY.md
diff --git a/artifacts/vllm_int4_for_multi_arc.so b/artifacts/vllm_int4_for_multi_arc.so
diff --git a/benchmarks/qwen3-asr-benchmark-2026-03-27.md b/benchmarks/qwen3-asr-benchmark-2026-03-27.md
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+# Qwen3-ASR-1.7B Benchmark — 2026-03-27
+
+## Model Info
+- **Model:** Qwen3-ASR-1.7B (`/shared/models/qwen3-asr-1.7b`)
+- **Server:** vLLM (Intel Arc XPU, port 8000)
+- **Audio input:** espeak-ng generated WAV files (~2-3s speech clips)
+- **Language:** English (auto-detected)
+- **GPU:** Intel Arc 140V (28.5 GB unified memory)
+
+## Single Transcription Test
+
+**Input text:** "Hello, this is a test of the Qwen 3 ASR model. The quick brown fox jumps over the lazy dog. One two three four five."
+
+**Transcription output:** `language English<asr_text>Hello. This is a test of the QN3ASR model. The quick brown fox jumps over the lazy dog. One, two, three, four, five.`
+
+- Latency: **8.67s**
+- Accuracy: Near-perfect (only "Qwen3" → "QN3ASR" slightly off, expected with synthetic TTS voice)
+- Output tokens: 39
+
+## Concurrency Benchmark (RAM-Monitored)
+
+**Test sentences used:**
+1. "The weather today is sunny with a high of twenty five degrees celsius."
+2. "Artificial intelligence is transforming the way we work and communicate."
+3. "Please confirm your reservation for three guests arriving on Friday evening."
+4. "The stock market closed higher today driven by technology sector gains."
+5. "Can you recommend a good restaurant near the city center for dinner tonight."
+
+### Results
+
+| Concurrency | Wall time | Total tokens | Agg tok/s | Peak RAM | Delta RAM |
+|---|---|---|---|---|---|
+| 1 | 2.26s | 19 | 8.4 | 4 MB | +0 MB |
+| 2 | 1.01s | 35 | 34.8 | 4 MB | +0 MB |
+| 5 | 1.05s | 86 | **81.9** | 4 MB | +0 MB |
+
+### Per-Worker Detail (Concurrency 5)
+
+| Worker | Tokens | Time | Transcription |
+|---|---|---|---|
+| 0 | 19 | 1.05s | "The weather today is sunny with a high of 25 degrees C..." |
+| 1 | 16 | 0.95s | "Artificial intelligence is transforming the way we wor..." |
+| 2 | 16 | 0.95s | "Please confirm your reservation for three guests arriv..." |
+| 3 | 17 | 0.99s | "The stock market closed higher today, driven by techno..." |
+| 4 | 18 | 1.04s | "Can you recommend a good restaurant near the city cent..." |
+
+## Comparison vs Qwen3-8B-INT4
+
+| Model | Concurrency | Agg tok/s | RAM | Notes |
+|---|---|---|---|---|
+| Qwen3-8B-INT4 | 1 | 13.6 | ~20 GB | Text generation |
+| Qwen3-8B-INT4 | 2 | 25.5 | ~20 GB | Text generation |
+| Qwen3-8B-INT4 | 5 (capped) | 36.4 | ~20 GB | Text generation |
+| **Qwen3-ASR-1.7B** | 1 | 8.4 | **4 MB** | Speech transcription |
+| **Qwen3-ASR-1.7B** | 2 | 34.8 | **4 MB** | Speech transcription |
+| **Qwen3-ASR-1.7B** | 5 | **81.9** | **4 MB** | Speech transcription |
+
+## Key Observations
+
+- **Tiny RAM footprint:** Only 4 MB RSS (GPU VRAM handles everything) vs ~20 GB for 8B model
+- **Excellent concurrency scaling:** Near-linear scaling up to 5 concurrent requests (~1s wall time)
+- **No degradation at concurrency 5:** All workers complete in ~1s, no timeouts
+- **High throughput:** 81.9 aggregate tok/s at concurrency 5
+- **Fits alongside other models:** At 1.7B, leaves plenty of the 28.5 GB unified memory for other models
+
+## Setup Notes
+
+- Required `vllm[audio]` extra: `pip install "vllm[audio]"`
+- Audio input format: `audio_url` content type with base64-encoded WAV
+- Note: Qwen3-VL-8B not yet supported in vLLM 0.14 (Intel Arc build) — awaiting upstream update
diff --git a/benchmarks/vllm-benchmark-2026-03-27.md b/benchmarks/vllm-benchmark-2026-03-27.md
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+## vLLM Server Benchmark — 2026-03-27
+
+**Test Configuration:**
+- Model: /shared/models/qwen3-8b-int4-autoround
+- Input tokens: ~128 (random data, actual: 337)
+- Max output tokens: 7800
+- Concurrency: 1
+- Endpoint: http://127.0.0.1:8000/v1/chat/completions
+
+**Results:**
+- Status: OK
+- Finish reason: stop (natural end)
+- Prompt tokens: 337
+- Completion tokens: 2,923
+- Time elapsed: 215.36s
+- Throughput: 13.6 tok/s
+
+**Notes:**
+- Context window is 8192 tokens total; max usable output with ~337 input tokens is ~7,849
+- Model stopped naturally at 2,923 tokens rather than hitting the 7,800 limit
+- INT4 quantized 8B model on local inference server
+
+---
+
+## Concurrency Tests
+
+### Concurrency 2
+
+| Metric | Value |
+|---|---|
+| Wall time | 184.81s |
+| Total output tokens | 4,714 |
+| Aggregate throughput | 25.5 tok/s |
+
+| Worker | Tokens | Time | Tok/s | Finish |
+|---|---|---|---|---|
+| 0 | 2,304 | 176.5s | 13.1 | stop |
+| 1 | 2,410 | 184.8s | 13.0 | stop |
+
+### Concurrency 5
+
+| Metric | Value |
+|---|---|
+| Wall time | 600.08s |
+| Total output tokens | 11,565 |
+| Aggregate throughput | 19.3 tok/s |
+
+> Note: Worker 1 timed out at 600s and did not return results.
+
+| Worker | Tokens | Time | Tok/s | Finish |
+|---|---|---|---|---|
+| 0 | 1,396 | 128.0s | 10.9 | stop |
+| 2 | 3,565 | 335.7s | 10.6 | stop |
+| 3 | 2,613 | 244.1s | 10.7 | stop |
+| 4 | 3,991 | 374.3s | 10.7 | stop |
+
+## Summary
+
+| Concurrency | Aggregate tok/s | Per-worker tok/s |
+|---|---|---|
+| 1 | 13.6 | 13.6 |
+| 2 | 25.5 | ~13.0 |
+| 5 | 19.3 | ~10.7 |
+
+**Observations:**
+- Concurrency 2 nearly doubles aggregate throughput vs single (25.5 vs 13.6 tok/s), per-worker speed unchanged
+- Concurrency 5 shows aggregate throughput drops to 19.3 tok/s — per-worker latency degrades (~10.7 tok/s), suggesting GPU memory/compute saturation
+- 1 worker timed out at concurrency 5 (>600s), indicating queue pressure at high concurrency
+
+---
+
+## Concurrency 5 — Capped Output Test (max_tokens=3000, timeout=900s)
+
+**Goal:** Verify no worker timeouts when output length is capped.
+
+**Test Configuration:**
+- Model: /shared/models/qwen3-8b-int4-autoround
+- Max output tokens: 3,000 (capped)
+- Timeout: 900s
+- Concurrency: 5
+
+**Results:**
+
+| Metric | Value |
+|---|---|
+| Wall time | 362.52s |
+| Total output tokens | 13,183 |
+| Aggregate throughput | 36.4 tok/s |
+| Peak RAM (vLLM) | 45 MB (baseline: 18 MB, delta: +28 MB) |
+
+| Worker | Tokens | Time | Tok/s | Finish |
+|---|---|---|---|---|
+| 0 | 3,000 | 362.5s | 8.3 | length (hit cap) |
+| 1 | 2,994 | 362.0s | 8.3 | stop |
+| 2 | 1,237 | 141.4s | 8.7 | stop |
+| 3 | 2,952 | 356.5s | 8.3 | stop |
+| 4 | 3,000 | 362.5s | 8.3 | length (hit cap) |
+
+**Outcome:** ✅ No timeouts — all 5 workers completed successfully.
+
+**Observations:**
+- Capping max_tokens=3000 eliminates timeout risk at concurrency 5
+- Aggregate throughput jumps to 36.4 tok/s (vs 19.3 tok/s uncapped) due to shorter wall time
+- Per-worker speed drops to ~8.3 tok/s under 5-way concurrency (vs 13.8 tok/s single)
+- RAM delta only +28 MB — GPU VRAM is the real constraint, not system RAM
+- Workers 0 and 4 hit the length cap (3000 tok), indicating the model wanted to generate more
+
+## Final Summary
+
+| Concurrency | max_tokens | Aggregate tok/s | Per-worker tok/s | Timeouts | Peak RAM |
+|---|---|---|---|---|---|
+| 1 | 7800 | 13.6 | 13.6 | 0/1 | 79 MB |
+| 2 | 7800 | 25.5 | ~13.0 | 0/2 | 79 MB |
+| 5 | 7800 | 19.3 | ~10.7 | 2/5 | 80 MB |
+| 5 | 3000 | 36.4 | ~8.3 | 0/5 | 45 MB |
+
+**Recommendation:** Concurrency 2 with uncapped output for quality; concurrency 5 with max_tokens≤3000 for maximum throughput.