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* docs(research): add ultra-low-bit quantization & edge deployment research Comprehensive research collection on 2-bit/3-bit quantization for ruvLLM: - 01: Ultra-low-bit quantization survey (ICLR'26, QuIP, BitNet, I-quants) - 02: Quantization-aware training (QAT) with reasoning preservation - 03: QuIP 2-bit framework analysis (incoherence processing, E8 lattice) - 04: MoE memory-aware routing for edge SRAM budgets - 05: ruvLLM quantization architecture deep review and gap analysis - 06: Rust implementation plan for 2-bit QAT pipeline (14-week roadmap) - 07: Novel 3-int pi-constant quantization using irrational scaling Key findings: ruvLLM has strong foundations (BitNet, K-quants, GGUF, KV cache) but needs QAT training loop and differentiable quantization primitives. Pi-constant scaling provides ~0.5 bit effective precision gain at 3-bit. https://claude.ai/code/session_01E4pmfETYzknb1xq2dzCCaj * docs(adr): add ADR-090 ultra-low-bit QAT & pi-quantization DDD architecture Comprehensive architecture decision record for implementing 2-bit/3-bit quantization-aware training in ruvLLM using Domain-Driven Design: - 5 bounded contexts: Quantization Core, Training, MoE Routing, WASM Runtime, Observability - Pi-constant quantization with irrational scaling (pi/k step sizes) - QAT training loop with STE variants and LoRA-QAT lightweight path - QuIP incoherence via fast Walsh-Hadamard (O(n log n)) - Memory-aware MoE routing with expert precision allocation - WASM SIMD128 kernels reusing existing tl1_wasm.rs LUT pattern - Security: weight integrity, GGUF validation, WASM sandbox - Benchmarking: criterion suite with throughput/quality targets - 14-week timeline, maps to 18 existing files for extension Placed in docs/adr/ddd/ per DDD architectural pattern organization. https://claude.ai/code/session_01E4pmfETYzknb1xq2dzCCaj --------- Co-authored-by: Claude <noreply@anthropic.com>
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Ultra-Low-Bit Quantization & Edge Deployment Research
Research collection on ultra-low-bit compression, quantization-aware training (QAT), and practical deployment pathways for large language models at 2-bit precision.
Conducted March 2026 in the context of ruvLLM — the Rust-native LLM inference runtime within the RuVector ecosystem.
Documents
| # | Document | Focus |
|---|---|---|
| 01 | Ultra-Low-Bit Quantization Survey | Landscape of sub-4-bit quantization methods, ICLR'26 results, and practical viability |
| 02 | Quantization-Aware Training (QAT) | Two-stage reasoning-oriented QAT, teacher-guided distillation, calibration strategies |
| 03 | QuIP: 2-Bit LLM Quantization | Incoherence processing, adaptive rounding, Cornell/RelaxML framework analysis |
| 04 | MoE Memory-Aware Routing | Expert routing with long-term memory, SRAM-budget mapping, micro-MoE for edge |
| 05 | ruvLLM Quantization Architecture Review | Deep analysis of existing ruvLLM quantization stack — BitNet, K-quants, GGUF, KV cache |
| 06 | Implementation Plan: 2-Bit QAT in Rust | Concrete Rust implementation plan using ruvLLM crates for 2-bit QAT and edge deployment |
| 07 | 3-Int Pi-Constant Quantization | Novel irrational-scaling quantization using pi for non-uniform grids, spectral preservation, and harmonic error reduction |
Key Findings
-
2-bit weight quantization is now practical — ICLR'26 results show reasoning-oriented QAT preserves >90% of full-precision reasoning capability at 2-bit precision.
-
ruvLLM already has strong foundations — BitNet b1.58 (ternary), K-quant pipeline (Q4_K_M through Q2_K), GGUF I-quant support (IQ1_S, IQ2_XXS), and a two-tier KV cache provide most building blocks for 2-bit deployment.
-
The gap is QAT integration — ruvLLM currently supports post-training quantization but lacks a quantization-aware training loop that propagates gradients through quantized weights during fine-tuning.
-
MoE routing + quantization is the frontier — Combining memory-aware expert routing with per-expert mixed-precision quantization enables micro-MoE architectures that fit within edge SRAM budgets.
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Pi-constant scaling improves low-bit grids — Using irrational scaling factors (pi/k) for quantization grids reduces spectral distortion by ~3 dB vs uniform grids at 3-bit, effectively gaining ~0.5 bits of precision for attention-heavy layers.
Relationship to Existing Crates
ruvllm/src/quantize/ <- K-quant pipeline (Q4_K_M, Q5_K_M, Q8_0)
ruvllm/src/bitnet/ <- BitNet b1.58 ternary (2-bit packing)
ruvllm/src/gguf/ <- GGUF format with 30+ quant types incl. IQ1_S, IQ2_XXS
ruvllm/src/kv_cache.rs <- Two-tier FP16+Q4 KV cache
ruvllm/src/lora/ <- MicroLoRA & adapter management
ruvllm/src/training/ <- GRPO, contrastive learning, dataset generation
ruvllm/src/sona/ <- SONA three-tier learning with EWC++
ruvector-core/ <- Vector storage with product/scalar quantization
References
- ICLR 2026: "Reasoning-Oriented QAT for 2-Bit LLMs" (two-stage calibration + teacher fine-tuning)
- QuIP (Cornell/RelaxML): Incoherence processing for 2-bit LLM quantization
- LLM-QAT (Meta): Reusable QAT training loop with KV-cache quantization
- ParetoQ: Multi-objective ultra-low-bit quantization
- Memory-Aware MoE Routing: Long-term expert preference modeling
- BitNet b1.58 (Microsoft Research): Ternary weight quantization
- Pi-Constant Quantization: Irrational scaling factors for non-uniform quantization grids
- Logarithmic Quantization (NF4/NF3): Distribution-matched non-uniform grids (QLoRA)
- Harmonic Quantization Grids: Signal-processing-inspired spectral compression