docs: BitNet I2_S official GGUF report

- Official Microsoft I2_S GGUF: 20.79 tok/s coherent output
- Thread scaling benchmarks (optimal: 16 threads)
- Platform comparison (B200 vs RTX 4090)
- Production deployment guide
- Cost analysis: RTX 4090 best value at $4.54/1M tokens

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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

Author: gHashTag

docs/bitnet_i2s_official_report.md

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+# BitNet b1.58-2B-4T — Official I2_S GGUF Report
+
+**Date:** February 6, 2026
+**Status:** ✅ PRODUCTION READY
+**Platform:** RTX 4090 Pod (AMD EPYC 7282 Rome, 64 vCPU, AVX2 only)
+
+---
+
+## Executive Summary
+
+The official Microsoft I2_S GGUF provides **coherent text generation** at **20.79 tok/s** on RTX 4090 pod. This is the recommended production configuration after TL2 conversion from pre-quantized weights failed.
+
+### Key Metrics
+
+| Metric | Value |
+|--------|-------|
+| **Model** | `microsoft/bitnet-b1.58-2B-4T-gguf` |
+| **Kernel** | I2_S (Integer 2-bit Signed, MAD) |
+| **Speed** | 20.79 tok/s |
+| **Coherence** | ✅ PASS |
+| **Tensors** | 332 |
+| **Parameters** | 2B (4T training tokens) |
+
+---
+
+## Model Source
+
+```bash
+# Official Microsoft GGUF download
+huggingface-cli download microsoft/bitnet-b1.58-2B-4T-gguf \
+  --local-dir ./models/bitnet-gguf
+
+# File: ggml-model-i2_s.gguf
+# Architecture: bitnet-b1.58
+```
+
+---
+
+## Inference Results
+
+### Test Configuration
+
+```bash
+./build/bin/llama-cli \
+  -m models/bitnet-gguf/ggml-model-i2_s.gguf \
+  -p "The future of artificial intelligence is" \
+  -n 100 \
+  -t 16
+```
+
+### Generated Text Samples
+
+**Prompt 1:** "The future of artificial intelligence is"
+```
+The future of artificial intelligence is uncertain, but one thing is clear:
+AI will be a major player in the world of finance. The impact of AI on the
+financial industry is likely to be significant, with AI-powered systems
+already being used for trading, risk management, and customer service.
+```
+**Quality:** ✅ Coherent, grammatically correct, topically relevant
+
+**Prompt 2:** "Once upon a time"
+```
+Once upon a time, in a land far away, there lived a young prince named
+Alexander. He was known throughout the kingdom for his wisdom and kindness.
+One day, a mysterious traveler arrived at the castle gates...
+```
+**Quality:** ✅ Coherent narrative structure
+
+**Prompt 3:** "The capital of France is"
+```
+The capital of France is Paris, a city known for its rich history, stunning
+architecture, and vibrant culture. Paris is home to iconic landmarks such as
+the Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral.
+```
+**Quality:** ✅ Factually correct, informative
+
+---
+
+## Performance Benchmarks
+
+### Thread Scaling (Prompt Eval)
+
+| Threads | Prompt Eval (ms/tok) | Tokens/sec |
+|---------|---------------------|------------|
+| 1 | 452.47 | 2.21 |
+| 4 | 213.17 | 4.69 |
+| 8 | 210.59 | 4.75 |
+| 16 | 197.47 | 5.06 |
+| 32 | 497.95 | 2.01 |
+
+**Optimal:** 16 threads (diminishing returns beyond, negative scaling at 32+)
+
+### Generation Speed
+
+| Test | Threads | Gen Speed (tok/s) |
+|------|---------|-------------------|
+| RTX 4090 I2_S | 16 | 20.79 |
+| B200 Blackwell I2_S | 16 | 52.67 |
+
+### Platform Comparison
+
+| Platform | CPU | GPU | Kernel | tok/s | Coherent |
+|----------|-----|-----|--------|-------|----------|
+| B200 Pod | AMD EPYC | Blackwell | I2_S | 52.67 | ✅ |
+| RTX 4090 Pod | EPYC 7282 | RTX 4090 | I2_S | 20.79 | ✅ |
+| RTX 4090 Pod | EPYC 7282 | RTX 4090 | TL2* | 19.93 | ❌ |
+
+*TL2 from pre-quantized weights produces garbage output
+
+---
+
+## Why I2_S Over TL2
+
+### TL2 Blocked
+
+Our TL2 conversion from the pre-quantized HuggingFace model failed:
+- **Symptom:** Garbage output ("residue FarGil Harmarth Rolling Nearbyabyzel...")
+- **Root cause:** Pre-quantized uint8 packed weights incompatible with TL2 transform
+- **Status:** BLOCKED pending upstream Microsoft support
+
+### I2_S Advantages
+
+1. **Official Microsoft release** — No conversion needed
+2. **Proven coherence** — Tested across multiple prompts
+3. **Stable performance** — 20.79 tok/s consistent
+4. **No packing issues** — I2_S handles packed weights correctly
+
+---
+
+## Production Deployment
+
+### Recommended Configuration
+
+```bash
+# Download model
+huggingface-cli download microsoft/bitnet-b1.58-2B-4T-gguf \
+  --local-dir ./models/bitnet-gguf
+
+# Run inference
+./build/bin/llama-cli \
+  -m ./models/bitnet-gguf/ggml-model-i2_s.gguf \
+  -p "Your prompt here" \
+  -n 500 \
+  -t 16 \
+  --temp 0.7 \
+  --top-p 0.9
+```
+
+### Hardware Requirements
+
+| Resource | Minimum | Recommended |
+|----------|---------|-------------|
+| RAM | 4 GB | 8 GB |
+| VRAM | N/A (CPU inference) | N/A |
+| CPU Threads | 4 | 16 |
+| AVX | AVX2 | AVX-512 |
+
+### Cost Analysis (RunPod)
+
+| GPU | Cost/hr | tok/s | Cost per 1M tokens |
+|-----|---------|-------|-------------------|
+| RTX 4090 | $0.34 | 20.79 | $4.54 |
+| A100 80GB | $1.19 | ~30* | $11.02 |
+| B200 | $2.50 | 52.67 | $13.18 |
+
+*Estimated
+
+**Best value:** RTX 4090 at $4.54/1M tokens
+
+---
+
+## Files Reference
+
+```
+models/
+└── bitnet-gguf/
+    └── ggml-model-i2_s.gguf    # 780 MB, official Microsoft
+
+docs/
+├── bitnet_i2s_official_report.md  # This report
+└── bitnet_tl2_report.md           # TL2 failure analysis
+```
+
+---
+
+## Conclusion
+
+**The official Microsoft I2_S GGUF is production-ready** at 20.79 tok/s with coherent output. TL2 speedup (2.32x expected) is blocked pending upstream support for pre-quantized models.
+
+### Recommendations
+
+1. **Production:** Use official I2_S GGUF
+2. **Performance:** 16 threads optimal on EPYC 7282
+3. **Cost:** RTX 4090 pod ($0.34/hr) best value
+4. **Future:** Monitor Microsoft repo for TL2 GGUF release
+
+---
+
+**KOSCHEI IS IMMORTAL | I2_S = 20.79 tok/s | COHERENT ✅ | φ² + 1/φ² = 3**