2nd/10_Wiki/Topics/AI_and_ML/Ultra-Efficiency.md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-ultra-efficiency	Ultra Efficiency	10_Wiki/Topics	verified	self	Model Compression Efficient Inference BitNet Sparse Models	none	A	0.88	applied	efficiency quantization compression sparse distillation		2026-05-10	pending	language framework python bitsandbytes-llama_cpp-mlx

Ultra Efficiency

매 한 줄

"매 model이 quality는 유지하며 compute / memory / energy 를 극단적으로 줄이는 spectrum". 매 1-bit BitNet (Microsoft 2024), GPTQ/AWQ quantization, MoE sparse activation, structured pruning, distillation 의 stack 으로 frontier models 가 phone 에서 inference 가능. 매 2026 mainstream.

매 핵심

매 four pillars

1. Quantization: FP16 → INT8 → INT4 → 1.58-bit (BitNet b1.58).
2. Sparsity: structured (2:4 NVIDIA), unstructured pruning, MoE.
3. Distillation: teacher → student (Phi-3, Llama 3.2, Gemma 2).
4. Architecture: linear attention (Mamba, RWKV), Mixture of Experts.

매 quantization spectrum

Format	Size	Quality loss	Tool
FP16	1x	baseline	-
INT8	0.5x	~1%	bitsandbytes
INT4 (GPTQ/AWQ)	0.25x	2-3%	autogptq, AWQ
INT4 (NF4 + double quant, QLoRA)	0.25x	<2%	bitsandbytes
1.58-bit (BitNet)	~0.1x	~equiv at scale	bitnet.cpp
1-bit (HQQ, AQLM)	~0.06x	3-5%	research

매 응용

1. On-device LLM (iPhone Neural Engine, Snapdragon NPU).
2. Cost reduction in cloud inference (vLLM + AWQ, 4x throughput).
3. Edge inference (Llama 3.2 1B / 3B on RPi).
4. Long-context (memory-bound → quantize KV cache).

💻 패턴

Pattern 1: AWQ quantization (vLLM)

from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer

model_path = "meta-llama/Llama-3.1-70B-Instruct"
quant_path = "llama-3.1-70b-awq"
quant_config = {"zero_point": True, "q_group_size": 128, "w_bit": 4}

model = AutoAWQForCausalLM.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)
model.quantize(tokenizer, quant_config=quant_config)
model.save_quantized(quant_path)

Pattern 2: bitsandbytes 4-bit load (QLoRA-ready)

from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
)
model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3.1-8B",
    quantization_config=bnb,
    device_map="auto",
)

Pattern 3: llama.cpp GGUF (CPU + Metal)

# Quantize to Q4_K_M (4-bit, balanced)
./llama-quantize model.gguf model-Q4_K_M.gguf Q4_K_M

# Run with Metal acceleration
./llama-cli -m model-Q4_K_M.gguf -p "Hello" -n 256 -ngl 99

Pattern 4: MLX (Apple Silicon, 2026 default for Mac)

from mlx_lm import load, generate

model, tokenizer = load("mlx-community/Llama-3.1-8B-Instruct-4bit")
response = generate(
    model, tokenizer,
    prompt="Explain entropy",
    max_tokens=256,
    temp=0.7,
)

Pattern 5: Distillation loop (student-teacher)

import torch.nn.functional as F

def distill_loss(student_logits, teacher_logits, labels, T=2.0, alpha=0.7):
    soft = F.kl_div(
        F.log_softmax(student_logits / T, dim=-1),
        F.softmax(teacher_logits / T, dim=-1),
        reduction="batchmean",
    ) * (T ** 2)
    hard = F.cross_entropy(student_logits, labels)
    return alpha * soft + (1 - alpha) * hard

Pattern 6: KV cache quantization (long context)

# vLLM kv cache INT8 — 2x context length on same GPU
from vllm import LLM, SamplingParams

llm = LLM(
    model="meta-llama/Llama-3.1-70B",
    quantization="awq",
    kv_cache_dtype="fp8",  # or "int8"
    max_model_len=128000,
)

Pattern 7: Structured sparsity (2:4 NVIDIA)

# Hopper / Ampere 의 2:4 structured sparse → 2x throughput
from torch.sparse import to_sparse_semi_structured

dense = layer.weight
sparse = to_sparse_semi_structured(dense)  # mask + compress
layer.weight = nn.Parameter(sparse)

매 결정 기준

상황	매 technique
Cloud GPU, throughput-bound	AWQ INT4 + vLLM
Apple Silicon	MLX 4-bit
CPU-only / edge	llama.cpp GGUF Q4_K_M
Custom finetune on 24GB	QLoRA NF4 + LoRA
Phone / NPU	BitNet b1.58 / Llama 3.2 1B
Long context 128K+	KV cache FP8

기본값: AWQ INT4 for serving, MLX 4-bit for Mac dev, GGUF Q4_K_M for portable.

🔗 Graph

• 부모: Model Compression
• 변형: LLM_Optimization_and_Deployment_Strategies · LLM_Optimization_and_Deployment_Strategies · Mixture of Experts
• 응용: LLM_Optimization_and_Deployment_Strategies · QLoRA
• Adjacent: BitNet · Mamba

🤖 LLM 활용

언제: cost / latency / memory budget tight. on-device deployment. long context. 언제 X: training new SOTA frontier (use full precision).

❌ 안티패턴

• Quantize without calibration: GPTQ/AWQ 의 calibration set 누락 → quality cliff.
• Pure 1-bit on small models: BitNet은 scale 효과. <1B 에선 quality loss.
• Distill across architecture mismatch: tokenizer 다르면 logit alignment 불가.
• Ignoring KV cache: model 만 quantize, KV cache fp16 → memory bound 그대로.
• Over-quantize attention scores: softmax precision 손실 → garbage output.

🧪 검증 / 중복

• Verified (BitNet b1.58 paper Microsoft 2024, AWQ MIT 2023, vLLM docs, MLX docs Apple 2024-2026).
• 신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — quantization spectrum + 2026 stack (BitNet, MLX, vLLM AWQ)