# M8: Voice Pipeline Capacity Research

*Research Date: 2026-02-10*
*Tested on: RTX PRO 6000 Blackwell (96GB VRAM) × 2*

## Executive Summary

**Tested:** 5-100 concurrent sessions through full Whisper→vLLM→Kokoro pipeline

**Result:** 100% success rate at all concurrency levels. No crashes, no failures.

**Practical capacity:** 20-30 concurrent voice calls with <5s response latency per GPU.

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## Test Results

### Full Voice Pipeline (STT→LLM→TTS)

| Concurrent | LLM avg | TTS avg | Wall time | Throughput | Success |
|------------|---------|---------|-----------|------------|---------|
| 5          | 421ms   | 538ms   | 2.9s      | 1.7/s      | 100%    |
| 10         | 425ms   | 922ms   | 5.2s      | 1.9/s      | 100%    |
| 15         | 440ms   | 530ms   | 7.8s      | 1.9/s      | 100%    |
| 20         | 474ms   | 1,267ms | 10.2s     | 2.0/s      | 100%    |
| 30         | 496ms   | 795ms   | 15.4s     | 1.9/s      | 100%    |
| 50         | 623ms   | 1,232ms | 24.9s     | 2.0/s      | 100%    |

### Isolated TTS Stress Test (Kokoro CPU)

| Concurrent | Wall time | Avg latency | Success |
|------------|-----------|-------------|---------|
| 25         | 10.9s     | 434ms       | 100%    |
| 30         | 11.1s     | 371ms       | 100%    |
| 40         | 15.3s     | 383ms       | 100%    |
| 50         | 18.6s     | 371ms       | 100%    |
| 75         | 27.9s     | 372ms       | 100%    |
| 100        | 36.2s     | 361ms       | 100%    |

### Isolated LLM Stress Test (vLLM)

| Concurrent | Avg latency | p95     | Success |
|------------|-------------|---------|---------|
| 1          | 60ms        | 60ms    | 100%    |
| 5          | 62ms        | 65ms    | 100%    |
| 10         | 66ms        | 71ms    | 100%    |
| 20         | 77ms        | 79ms    | 100%    |

---

## Key Findings

### 1. LLM (vLLM) is Bulletproof
- Only +200ms degradation from 5→50 concurrent (421ms→623ms)
- vLLM batching is extremely efficient
- Continuous batching handles burst load gracefully

### 2. TTS (Kokoro CPU) is the Bottleneck
- ~370ms floor per utterance regardless of load
- Scales linearly (no batching benefit on CPU)
- **Never crashes** — just queues gracefully
- Throughput: ~2.7 req/sec sustained

### 3. No Breaking Point Found
- Pushed to 100 concurrent with zero failures
- System degrades gracefully (linear queue stacking)
- No cliff, no crashes, no OOM

---

## VRAM Analysis (Corrected)

### How vLLM Actually Works
The model loads **once** and handles multiple concurrent requests through batching. Model weights are shared across all users.

**Correct breakdown:**
- Model weights: ~21-24GB (loaded ONCE, shared)
- KV cache per user: ~2GB for 8K context
- Overhead: marginal

**With 96GB total:**
- ~24GB for model weights
- ~72GB available for KV cache
- At 2GB per user = **~36 theoretical concurrent users**

### vs. Naive Analysis
A naive analysis (model VRAM per user) incorrectly suggests 3-4 users per GPU. This misunderstands vLLM's architecture.

---

## Practical Capacity Estimates

### Per GPU (RTX PRO 6000 Blackwell, 96GB)

| Use Case | Concurrent Users | Latency Budget |
|----------|------------------|----------------|
| Real-time voice | 20-30 | <5s response |
| Burst capacity | 50+ | <15s response |
| Theoretical max | ~36 | KV cache limited |

### Across Cluster (2× GPUs)

| Configuration | Total Capacity |
|---------------|----------------|
| Conservative | 40-60 concurrent voice calls |
| Aggressive | 100+ with queue tolerance |

---

## Bottleneck Analysis

**Current stack latency breakdown:**
- STT (Whisper): ~200ms
- LLM (vLLM): ~100ms (short responses)
- TTS (Kokoro CPU): ~370ms

**Total round-trip:** ~700ms minimum

### Optimization Paths

1. **GPU TTS:** Would cut TTS to ~50-100ms, 3-4× capacity improvement
2. **Streaming TTS:** Start playback before full generation
3. **Response caching:** Frequently used phrases pre-rendered

---

## Test Scripts

- `tools/voice-stress-test.py` — Full pipeline stress test
- `tools/tts-stress-test.sh` — Isolated TTS stress test
- `tools/livekit-stress-test.py` — Concurrent LLM test

Commit: `30a6aa5`

---

## Conclusion

The voice pipeline is **production-ready** for 20-30 concurrent users per GPU. The system degrades gracefully under load with no crash behavior. TTS is the bottleneck; GPU acceleration would unlock 3-4× more capacity.

**Recommendation:** Ship it. Optimize TTS later.

## 100-Concurrent Stress Test (2026-02-10 17:30 UTC)

**Test:** Full pipeline (STT → LLM → TTS) at 100 concurrent sessions

| Metric | Value |
|--------|-------|
| Success Rate | 100/100 (100%) |
| Wall Time | 49s |
| Throughput | 2.1 round-trips/sec |

### Per-Stage Latency

| Stage | Avg | P95 |
|-------|-----|-----|
| STT | 64ms | 77ms |
| LLM | 1043ms | 1076ms |
| TTS | 438ms | 515ms |

### Key Findings

1. **No crashes** — system degrades gracefully under extreme load
2. **Throughput ceiling at 2.1 req/sec** — same as 50 concurrent, batching saturated
3. **LLM is the bottleneck at scale** (1043ms avg), not TTS (438ms)
4. **Queue stacking is linear** — requests wait in order, no cascading failures

### Implications

- GPU Kokoro would cut ~300ms latency but won't improve throughput
- Throughput ceiling is vLLM continuous batching capacity
- For higher throughput: add more GPU inference capacity (second model instance)
- 20-30 concurrent voice calls confirmed as realistic capacity
