NeoSignal Optimization Advisor: Choose the Right Quantization for Your Deployment

A 70B parameter model in FP16 needs 140GB of memory—more than any single GPU can hold. Quantization compresses models to fit, but the landscape is fragmented. GPTQ offers 4-bit precision with calibration. AWQ claims better quality at the same compression. GGUF enables CPU inference. BitsAndBytes works with HuggingFace Transformers. FP8 requires Hopper GPUs. Each method has hardware requirements, engine compatibility, and quality tradeoffs that aren't obvious from the names alone.

NeoSignal Optimization Advisor showing quantization method comparison

NeoSignal Optimization Advisor cuts through the confusion. Select your model, target hardware (GPU, CPU, or edge), serving engine (vLLM, TensorRT-LLM, llama.cpp, SGLang, Transformers), and quality priority. The advisor scores compatible methods across quality preservation, memory savings, and engine support, then recommends the best fit. You see memory before and after quantization, expected quality impact, and a ready-to-use configuration snippet for your chosen engine. Implementation steps guide you from environment setup to deployment.

The benefit: you choose quantization with confidence. No more trial-and-error across methods, no more discovering incompatibilities after downloading a 50GB model. The advisor encodes compatibility constraints and quality characteristics so you can deploy efficiently.

Detailed Walkthrough

The Quantization Landscape

Quantization reduces model precision from 16-bit floating point to lower bit widths. The tradeoffs:

Each method has specific hardware and engine requirements that determine whether it's even an option for your deployment.

Quantization Methods Database

NeoSignal maintains detailed profiles for 13 quantization methods:

FP16 (Baseline): Half-precision floating point. No quantization, full quality. Requires CUDA GPU with FP16 support. Works with vLLM, TensorRT-LLM, SGLang, and Transformers.

INT8 Dynamic: Weights quantized to 8-bit, activations computed at runtime. 50% memory savings with ~0.5% perplexity increase. No calibration needed. Works on GPU and CPU.

INT8 Static: Both weights and activations quantized with calibration. Slightly better quality than dynamic. Requires calibration dataset (128-512 samples).

FP8 E4M3: 8-bit floating point optimized for inference. 50% savings with minimal quality impact (~0.2% perplexity). Requires Hopper (H100) or Ada Lovelace GPUs and CUDA 12+.

FP8 E5M2: Wider dynamic range than E4M3, less precision. Better for training, slightly lower quality for inference.

GPTQ 4-bit: Post-training quantization using optimal brain quantization. 75% memory savings. Requires calibration data. Widely supported in vLLM, Transformers, SGLang.

GPTQ 8-bit: Higher precision GPTQ variant. 50% savings with lower quality impact than 4-bit.

AWQ 4-bit: Activation-aware weight quantization. Claims better quality than GPTQ at same compression. Calibration required. Well-supported in vLLM and SGLang.

GGUF Q4_K_M: llama.cpp 4-bit quantization. Excellent for CPU and Apple Silicon. No calibration needed. Works on CPU, GPU, and edge devices.

GGUF Q5_K_M: Higher quality 5-bit GGUF variant. Better quality, slightly larger.

GGUF Q8_0: 8-bit GGUF. Best GGUF quality, 50% savings.

BitsAndBytes 4-bit: NF4/FP4 quantization for HuggingFace Transformers. QLoRA compatible. No calibration needed. GPU only.

BitsAndBytes 8-bit: LLM.int8() quantization. Handles outliers automatically.

Input Configuration

The advisor collects information needed for accurate recommendations:

Model Selection: Choose from NeoSignal's model database. Parameter count determines memory requirements before and after quantization.

Target Hardware: GPU, CPU, or edge device. This filters methods to those that work on your hardware:

• GPU: All methods available
• CPU: INT8, GGUF variants only
• Edge: GGUF variants only

Serving Engine: vLLM, TensorRT-LLM, llama.cpp, SGLang, or Transformers. Each engine supports specific quantization methods:

• vLLM: FP16, INT8, FP8, GPTQ, AWQ
• TensorRT-LLM: FP16, INT8, FP8
• llama.cpp: GGUF variants only
• SGLang: FP16, GPTQ, AWQ
• Transformers: FP16, INT8, GPTQ, BitsAndBytes

Quality Priority: Slider from 0 (prioritize memory/speed) to 100 (prioritize quality). This weights the scoring:

• High priority (70-100): Prefer methods with low quality impact
• Balanced (30-70): Balance quality and compression
• Low priority (0-30): Prefer maximum memory savings

Scoring Methodology

The advisor scores each compatible method across three dimensions:

Quality Score (weighted by priority): 100 minus quality impact rating. FP8's 2% impact scores 98, while GPTQ 4-bit's 15% impact scores 85.

Memory Savings Score (inverse priority weight): Method's memory savings × 100. AWQ 4-bit's 75% savings scores 75.

Compatibility Score: 100 for methods that match hardware and engine requirements, 0 otherwise.

Composite formula:

Score = (QualityScore × QualityWeight × 0.5) +
        (MemorySavingsScore × MemorySavingsWeight × 0.4) +
        (CompatibilityScore × 0.1)

Methods with 0 compatibility are excluded. The highest-scoring compatible method becomes the recommendation.

Memory Comparison

The advisor calculates memory impact:

Original Size: Parameters × 2 bytes (FP16)

• 70B model: 140 GB

Quantized Size: Parameters × (bits / 8)

• 4-bit: 70B × 0.5 = 35 GB
• 8-bit: 70B × 1 = 70 GB

Savings: Original - Quantized

• 4-bit: 140 - 35 = 105 GB (75%)
• 8-bit: 140 - 70 = 70 GB (50%)

This visual comparison shows exactly how much memory you'll save.

Configuration Snippets

The advisor generates ready-to-use code for your chosen engine and method:

vLLM with AWQ:

from vllm import LLM

llm = LLM(
    model="meta-llama/Llama-3.1-70B-AWQ",
    quantization="awq",
    dtype="float16",
    tensor_parallel_size=1,
)

TensorRT-LLM with FP8:

trtllm-build \
    --checkpoint_dir ./checkpoint \
    --output_dir ./engine \
    --gemm_plugin float16 \
    --use_fp8_context_fmha enable

llama.cpp with GGUF:

from llama_cpp import Llama

llm = Llama(
    model_path="./Llama-3.1-70B-Q4_K_M.gguf",
    n_ctx=4096,
    n_threads=8,
)

Transformers with BitsAndBytes:

from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
)

model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3.1-70B",
    quantization_config=bnb_config,
    device_map="auto",
)

Implementation Steps

The advisor provides step-by-step implementation guidance:

Step 1: Environment Setup Install required packages for your engine and quantization method:

pip install vllm  # for vLLM
pip install autoawq  # for AWQ
pip install auto-gptq  # for GPTQ
pip install bitsandbytes  # for BNB

Step 2: Prepare Calibration Data (if required) Gather 128-512 representative samples for calibration. Use data similar to your deployment use case.

Step 3: Get or Quantize Model Either download a pre-quantized model from HuggingFace or run quantization yourself. Most popular models have pre-quantized variants available.

Step 4: Validate Quality Run a small benchmark to verify output quality. Check perplexity on a held-out dataset if available.

Step 5: Deploy Deploy with your chosen serving engine. Monitor latency and throughput in production.

Recommendations

The advisor generates contextual guidance:

Quality vs. Memory Tradeoffs:

• "You prioritized quality but AWQ 4-bit has significant compression. Consider 8-bit quantization if quality degradation is noticeable."
• "You prioritized speed/memory but FP8 has moderate compression. Consider 4-bit for more aggressive savings."

Hardware-Specific:

• "FP8 requires Hopper (H100) or Ada Lovelace GPUs. Verify your hardware supports FP8 compute."
• "70B model on CPU may be slow. Consider using fewer threads or a smaller model variant."

Calibration Guidance:

• "This method requires calibration data. Use 128-512 samples representative of your use case for best results."

Model Size Advice:

• "For 70B+ models, consider 4-bit quantization to fit in available GPU memory."

Chat Integration

The Optimization Advisor integrates with NeoSignal AI chat:

Context Sharing: Your configuration and results are available to the chat. Ask "Why is AWQ recommended over GPTQ?" and the response references your quality priority setting.

Quality Questions: "How much will perplexity increase with this method?" gets specific numbers based on the recommended method's characteristics.

Alternative Exploration: "What if I switch to TensorRT-LLM?" triggers analysis of which methods become available or unavailable.

Real-World Usage Patterns

Production Inference Deployment: You're deploying Llama 3.1 70B on 8x A100-80GB for customer-facing inference. Enter the configuration. AWQ 4-bit recommended—75% memory savings, good quality, well-supported in vLLM. Copy the config, deploy.

Local Development: You want to run a 70B model on your MacBook M3 Max. Select CPU hardware and llama.cpp engine. GGUF Q4_K_M recommended—runs efficiently on Apple Silicon with good quality.

Quality-Critical Application: You're building a medical assistant where output quality is paramount. Set quality priority to 90%. FP8 recommended—50% savings with minimal quality impact. Only works if you have H100s.

Maximum Compression: You need to fit a 70B model on a single 24GB RTX 4090. Set quality priority to 20%. GPTQ 4-bit recommended with specific batch size constraints.

From Advisor to Inference

NeoSignal Optimization Advisor simplifies quantization selection by encoding the compatibility matrix, quality characteristics, and engine requirements into automated recommendations. You don't need to research which methods work with which engines or guess at quality tradeoffs.

The output is deployment-ready: a recommended method, configuration code, memory comparison, and implementation steps. When requirements change—different hardware, different engine, different quality needs—return to the advisor and get updated recommendations immediately.

That's the NeoSignal approach to AI infrastructure tooling: expert knowledge about quantization methods encoded in precise calculations, delivered through an interface that makes deployment decisions actionable.