LLM Inference Optimization and Quantization 2026

Comprehensive guide to efficient LLM deployment covering quantization methods, inference frameworks, and production optimization techniques.

Last Updated: January 2026

Executive Summary
Quantization Methods
Inference Frameworks
Speculative Decoding
KV Cache Optimization
Continuous Batching
Hardware Considerations
Production Deployment Patterns
Edge and Mobile Deployment
Benchmarks and Performance
Practical Recommendations

Executive Summary

The LLM inference landscape has matured significantly in 2025-2026, with several key developments:

Quantization: FP8 has emerged as the gold standard for quality/performance balance on Hopper GPUs
Frameworks: vLLM dominates production deployments; SGLang leads in throughput with RadixAttention
Memory: PagedAttention reduced KV cache waste from 60-80% to under 4%
Batching: Continuous batching delivers up to 23x throughput improvement
Hardware: H100 pricing dropped from $8/hr to ~$3/hr; B200s entering production

Key Performance Numbers (2026)

Metric	Typical Improvement
PagedAttention throughput	2-4x vs traditional
FP8 vs FP16 speed	30-33% faster
Speculative decoding	Up to 3x faster
Continuous batching	23x throughput
FlashAttention-3 (H100)	840 TFLOPS (85% utilization)

Quantization Methods

Quantization reduces numerical precision of model weights and activations, trading minimal quality for significant memory and speed gains.

Overview of Methods

Method	Bits	Target Hardware	Quality Retention	Best For
FP8	8	NVIDIA Hopper+	~99%	Production GPU serving
AWQ	4	GPU	~95%	Creative writing, coding
GPTQ	4	GPU	~90%	Max throughput
GGUF	2-8	CPU/Apple Silicon	~92%	Local/edge deployment
INT8	8	General	~97-99%	Wide compatibility
INT4	4	General	~90-95%	Memory-constrained

FP8 Quantization (Recommended for Production)

FP8 is the most stable option across model sizes, particularly on NVIDIA Hopper GPUs with native Transformer Engine support.

Key Benefits:

Near-lossless quality (0.1-0.3% perplexity increase typical)
33% faster inference vs FP16
50% memory reduction for KV cache
Native hardware acceleration on H100/H200

Benchmark Results (Mistral 7B):

FP8 vs FP16:
- Latency (TTFT): 8.5% decrease
- Speed (tokens/sec): 33% improvement
- Throughput: 31% increase

Best Practices:

# vLLM FP8 inference
from vllm import LLM

llm = LLM(
    model="meta-llama/Llama-3.1-70B",
    quantization="fp8",
    kv_cache_dtype="fp8"  # Recommended for Hopper
)

GPTQ (GPU-Optimized)

Post-training quantization focused on GPU inference with excellent throughput.

Characteristics:

Requires calibration dataset (quality depends on dataset selection)
Excellent with Marlin kernels (2.5x faster than base GPTQ)
Best raw throughput on NVIDIA GPUs
Supports 2/3/4/8-bit quantization

Performance Note: Kernels matter more than algorithms. Marlin uses the same GPTQ weights but runs 2.5x faster thanks to optimized CUDA kernels.

# GPTQ with Marlin kernels in vLLM
llm = LLM(
    model="TheBloke/Llama-2-70B-GPTQ",
    quantization="marlin"
)

AWQ (Activation-Aware)

Preserves "salient" weights (top 1%) that carry most important information.

Key Insight: Not all weights are equally important. AWQ identifies and protects critical weights during quantization.

Advantages:

No backpropagation required (faster quantization)
Better quality preservation for creative/coding tasks
Excellent GPU inference speeds
95% quality retention typical

When to Use:

Creative writing applications
Code generation
Tasks requiring nuanced output
Large models (70B+) where quality matters most

GGUF (CPU/Edge Optimized)

The standard for CPU inference and Apple Silicon, evolved from GGML.

Format Benefits:

Single-file format with embedded metadata
Native support in llama.cpp, Ollama
Flexible CPU/GPU offloading
Wide quantization range (Q2_K to Q8_0)

Quantization Levels:

Q2_K:  ~2.5 bits/weight - Extreme compression, quality loss
Q4_K_M: ~4.5 bits/weight - Good balance (recommended)
Q5_K_M: ~5.5 bits/weight - Better quality, still fast
Q8_0:   8 bits/weight - Near-lossless

Practical Guidance:

Use Q5_K_M for best quality/size balance
GGUF has overhead in vLLM - stick to llama.cpp/Ollama
Best for Apple Silicon where unified memory shines

Quality Comparison by Task

Task	Best Method	Notes
Code Generation	GGUF Q5_K_M	54.27% HumanEval (only 2% below baseline)
General Chat	AWQ/FP8	Consistent quality across inputs
High Throughput	GPTQ/Marlin	Speed over quality
Long Context	FP8	KV cache benefits
Edge/Mobile	GGUF Q4_K	Memory efficiency

Inference Frameworks

Framework Comparison (2026)

Framework	Best For	Throughput	Setup Time	Notes
vLLM	Production serving	120-160 req/s	Hours	Industry standard
SGLang	Agents, RAG	Up to 3.1x vLLM	Hours	RadixAttention
TensorRT-LLM	Max NVIDIA perf	Highest	1-2 weeks	Complex setup
llama.cpp	Edge/local	Varies	Minutes	CPU excellence
Ollama	Dev/prototyping	Moderate	Minutes	Simplest setup

vLLM

The production standard for LLM serving, developed at UC Berkeley.

Key Features:

PagedAttention for memory efficiency
Continuous batching
Tensor parallelism
OpenAI-compatible API
Wide model support

Performance Characteristics:

120-160 requests/second typical
50-80ms time to first token
Scales well from 10 to 100+ concurrent users

Quick Start:

pip install vllm

# Start server
python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-3.1-8B \
    --tensor-parallel-size 2 \
    --quantization fp8

Optimization Flags:

from vllm import LLM, SamplingParams

llm = LLM(
    model="meta-llama/Llama-3.1-70B",
    tensor_parallel_size=4,
    gpu_memory_utilization=0.95,
    enable_chunked_prefill=True,
    enable_prefix_caching=True,  # Reuse common prefixes
    quantization="fp8",
    kv_cache_dtype="fp8"
)

SGLang

Next-generation framework with RadixAttention for improved KV cache reuse.

Key Innovation - RadixAttention: Keeps user prompts in KV cache for reuse when parts repeat:

Chat history caching
Few-shot example reuse
System prompt sharing

Performance:

Up to 3.1x throughput vs vLLM on 70B models
Matches or exceeds TensorRT-LLM
Ideal for multi-turn conversations and agents

Use Cases:

Chatbots with long conversation history
RAG systems with repeated context
Agent workflows with tool chains
Few-shot prompting scenarios

import sglang as sgl

@sgl.function
def multi_turn_chat(s, messages):
    for msg in messages:
        if msg["role"] == "user":
            s += sgl.user(msg["content"])
        else:
            s += sgl.assistant(sgl.gen("response"))
    return s

TensorRT-LLM

NVIDIA's optimized inference library for maximum performance.

Advantages:

Highest raw performance on NVIDIA hardware
Native FP8/INT8 support
In-flight batching
Paged KV cache

Performance:

4.6x A100 performance on H100
10,000 tok/s at 100ms TTFT possible
35-50ms TTFT at low concurrency

Trade-offs:

Complex setup (1-2 weeks expert time)
NVIDIA ecosystem lock-in
Requires Docker typically
Best if already using Triton/NeMo

# Docker-based setup
docker run --gpus all -v ./models:/models \
    nvcr.io/nvidia/tritonserver:24.01-trtllm-python-py3 \
    python build.py --model_dir /models/llama-70b

llama.cpp

Pure C/C++ inference with exceptional portability.

Strengths:

Zero dependencies
Runs anywhere (server, laptop, phone, RPi)
Fastest startup time
GGUF native support
Active community

Performance (CPU):

Optimized for single-stream efficiency
Excellent on Apple Silicon
Reasonable throughput on high-core-count CPUs

# Build and run
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp && make

# Run inference
./main -m models/llama-3-8b.Q5_K_M.gguf \
    -p "Hello, how are you?" \
    -n 128 \
    --threads 8

Ollama

Simplest local LLM deployment with automatic model management.

2025-2026 Updates:

Flash Attention enabled by default (v0.13.5)
Vulkan acceleration support
Improved GPU detection
20% faster inference vs GUI alternatives

Limitations:

Single-user focused (4 parallel requests max)
No advanced batching
Not suitable for production scale

Best For:

Local development
Prototyping
Privacy-first single-user apps
Quick model testing

# One-line setup
ollama run llama3.1:70b

# With custom parameters
ollama run llama3.1:70b --num-gpu 2 --ctx-size 8192

Speculative Decoding

Speculative decoding accelerates inference by having a small "draft" model propose tokens that a larger "target" model verifies in parallel.

How It Works

Draft Phase: Small model generates K candidate tokens quickly
Verify Phase: Large model verifies all K tokens in single forward pass
Accept/Reject: Accept matching tokens, reject and regenerate mismatches
Guarantee: Output is mathematically identical to target model alone

Performance Gains

Method	Speedup	Notes
Basic Speculative	2-3x	Requires separate draft model
EAGLE-3	2.5-3x	No separate model needed
Apple ReDrafter	2.8x	RNN-based draft head
PEARL	4.43x vs AR	Adaptive draft length

Key Techniques (2025-2026)

EAGLE-3:

Lightweight prediction head attached to target model layers
No separate draft model required
Improved acceptance rates
Optimized for NVIDIA GPUs

Apple ReDrafter:

RNN-based draft model on LLM hidden states
Dynamic tree attention over beam search
Knowledge distillation training
State-of-the-art on Vicuna benchmarks

PEARL (Parallel Speculative Decoding):

Pre-verify: Validates first draft token during drafting
Post-verify: Generates more tokens during verification
Adaptive draft length based on acceptance patterns
1.5x improvement over vanilla speculative decoding

Implementation in vLLM

from vllm import LLM, SamplingParams

# With separate draft model
llm = LLM(
    model="meta-llama/Llama-3.1-70B",
    speculative_model="meta-llama/Llama-3.1-8B",
    num_speculative_tokens=5,
    speculative_draft_tensor_parallel_size=1
)

# With ngram-based speculation (no draft model)
llm = LLM(
    model="meta-llama/Llama-3.1-70B",
    speculative_model="[ngram]",
    num_speculative_tokens=5,
    ngram_prompt_lookup_max=4
)

Best Practices

Draft Model Selection:
- Latency matters more than capability
- 7-8B draft models work well for 70B targets
- Same tokenizer required
When to Use:
- Latency-sensitive applications
- Single-user scenarios
- Long generation tasks
When to Skip:
- High-throughput batch scenarios (batching more efficient)
- Very short outputs
- Memory-constrained environments

KV Cache Optimization

The KV (Key-Value) cache stores attention computations to avoid recomputation, but traditionally wastes 60-80% of allocated memory.

The Problem

A 70B model with 8K context requires ~20GB cache per request
Batch of 32 = ~640GB cache memory
KV cache often exceeds model weights in memory consumption
Traditional systems achieve only 20-38% memory utilization

PagedAttention (vLLM)

Revolutionary memory management that reduced waste to under 4%.

How It Works:

Divides KV cache into fixed-size pages (like OS virtual memory)
Non-contiguous storage with indirection table
Fine-grained allocation and deallocation
Enables memory sharing between requests

Impact:

2-4x throughput improvement
Equivalent to doubling GPU investment
Enables larger batch sizes

# PagedAttention is automatic in vLLM
# Key configuration options:
llm = LLM(
    model="meta-llama/Llama-3.1-70B",
    gpu_memory_utilization=0.9,  # Reserve 90% for model + cache
    max_model_len=8192,  # Context length affects cache size
)

FP8 KV Cache

Halves KV cache memory with minimal quality impact.

Recommendation: On Hopper GPUs, use FP8 over INT8 for KV cache - lower accuracy impact in most cases.

llm = LLM(
    model="meta-llama/Llama-3.1-70B",
    kv_cache_dtype="fp8"  # vs "auto" (fp16)
)

Advanced Techniques

LMCache: Multi-tier caching system for enterprise workloads:

GPU → CPU DRAM → Local disk
3-10x latency reduction for repeated contexts
Cross-instance cache sharing

PagedEviction (2025): Block-wise eviction of low-importance cache blocks without modifying CUDA kernels.

KV Cache Offloading: Move cache to CPU memory when GPU is constrained:

Enables models larger than GPU memory
Uses NVIDIA unified memory or custom solutions
Trade latency for capacity

Memory Planning

Rule of Thumb: Reserve 40-60% of GPU memory for KV cache.

Model Size	Recommended KV Cache Budget	Max Batch @ 4K Context
7B	20-30% GPU memory	64+
13B	30-40% GPU memory	32-48
70B	50-60% GPU memory	8-16

Continuous Batching

Continuous batching dynamically manages request batches at the iteration level, dramatically improving GPU utilization.

Batching Strategies Comparison

Strategy	Description	Best For
Static	Fixed batch, wait for all	Offline batch jobs
Dynamic	Time-window batching	Image generation
Continuous	Iteration-level scheduling	LLM serving

How Continuous Batching Works

Requests enter queue as they arrive
At each decoding iteration:
- Completed sequences exit batch immediately
- New requests fill empty slots
No waiting for longest sequence
GPU stays maximally utilized

Performance Impact

23x throughput improvement with vLLM continuous batching
GPU utilization increases from <40% to ~50%+ with memory-aware scheduling
Requests return immediately upon completion

Framework Support

All major frameworks support continuous batching:

vLLM: Native continuous batching
SGLang: Enhanced with RadixAttention
TensorRT-LLM: "In-flight batching"
LMDeploy: "Persistent batching"
HuggingFace TGI: Built-in support

2025 Advancements: Memory-Aware Dynamic Batching

Research published in 2025 introduces dynamic batch size adjustment based on:

Real-time memory monitoring
SLA constraints
Latency feedback

Results: Up to 28% additional throughput improvement over static continuous batching.

Implementation Example

# vLLM handles continuous batching automatically
# Key parameters to tune:

from vllm import AsyncLLMEngine, EngineArgs

engine_args = EngineArgs(
    model="meta-llama/Llama-3.1-70B",
    max_num_seqs=256,  # Max concurrent sequences
    max_num_batched_tokens=8192,  # Max tokens per batch
    scheduler_delay_factor=0.0,  # 0 = greedy scheduling
)

engine = AsyncLLMEngine.from_engine_args(engine_args)

Best Practices

Set appropriate max_num_seqs: Balance memory vs throughput
Monitor queue depth: Custom metric for HPA scaling
Use prefill chunking: Prevent long prompts from blocking decode
Enable prefix caching: Reuse common prompt prefixes

Hardware Considerations

NVIDIA GPU Comparison (2026)

GPU	Memory	Bandwidth	FP8 TFLOPS	Best For	Typical Price
A100	40/80GB HBM2e	2 TB/s	N/A	Cost-efficient production	$1.50-2/hr
H100	80GB HBM3	3.35 TB/s	1979	Highest performance	$2.85-3.50/hr
H200	141GB HBM3e	4.8 TB/s	1979	Large models, long context	$4-6/hr
B200	192GB HBM3e	8 TB/s	4500	Next-gen cutting edge	Limited availability

H100 vs A100 Decision Matrix

Choose H100 when:

FP8 precision is beneficial (transformer workloads)
Maximum throughput required
Long context support needed (80GB + 3.35TB/s bandwidth)
Real-time inference at scale

Choose A100 when:

Optimizing for cost
Legacy model compatibility required
Mid-sized LLMs (7B-13B)
Burstable/occasional workloads

H100 Key Advantages

Transformer Engine: Automatic FP8/FP16 precision selection
4th Gen Tensor Cores: 4x performance vs A100 3rd gen
Memory Bandwidth: 1.6x improvement (3.35 vs 2 TB/s)
Compute: 3x+ FP16 tensor performance

Benchmark Results:

H100 vs A100 (TensorRT-LLM):
- 4.6x overall performance
- 2x throughput at constant batch size
- 3x throughput at increased batch size

Price Trends (2025-2026)

H100 pricing has dropped dramatically:

2024: $8/hour typical
2025-2026: $2.85-3.50/hour
This effectively eliminates A100's previous cost advantage

Memory Requirements by Model Size

Model	FP16	INT8	INT4
7B	14GB	7GB	4GB
13B	26GB	13GB	7GB
34B	68GB	34GB	17GB
70B	140GB	70GB	35GB

Multi-GPU Strategies

Tensor Parallelism:

Split model layers across GPUs
Reduces per-GPU memory
Requires high-bandwidth interconnect (NVLink)
Best for latency-sensitive serving

Pipeline Parallelism:

Split model stages across GPUs
Better for throughput
Higher latency
Works with slower interconnects

# vLLM tensor parallelism
llm = LLM(
    model="meta-llama/Llama-3.1-70B",
    tensor_parallel_size=4,  # Split across 4 GPUs
)

Production Deployment Patterns

Kubernetes-Native Deployment

llm-d Framework (2025): Kubernetes-native distributed inference with:

Prefill/decode disaggregation
KV-cache-aware load balancing
Multi-accelerator support (NVIDIA, AMD, TPU, Intel)
Traffic-aware autoscaling

v0.4 Results:

40% reduction in time per output token
Improved model-as-a-service efficiency

Architecture Patterns

1. Disaggregated Serving:

┌─────────────────┐     ┌─────────────────┐
│  Prefill Pods   │────▶│  Decode Pods    │
│  (GPU-bound)    │     │  (Memory-bound) │
└─────────────────┘     └─────────────────┘

Separate prefill (prompt processing) from decode (generation)
Optimizes GPU utilization for each phase
Reduces TTFT, improves TPOT consistency

2. Standard Replicated:

┌─────────────────┐
│   Load Balancer │
└────────┬────────┘
    ┌────┴────┐
    ▼         ▼
┌───────┐ ┌───────┐
│ Pod 1 │ │ Pod 2 │
└───────┘ └───────┘

Simpler architecture
Good for smaller models
Standard K8s autoscaling

Autoscaling Configuration

Unique LLM Challenges:

GPU scheduling complexity
30-120 second startup times
Variable request duration
Memory intensity

HPA Configuration:

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: llm-inference
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: vllm-deployment
  minReplicas: 2
  maxReplicas: 10
  metrics:
  - type: Pods
    pods:
      metric:
        name: gpu_utilization
      target:
        type: AverageValue
        averageValue: "80"
  behavior:
    scaleDown:
      stabilizationWindowSeconds: 300  # Prevent thrashing
    scaleUp:
      stabilizationWindowSeconds: 60

Key Metrics for Scaling:

GPU utilization
Inference queue depth
Memory utilization
Request latency percentiles

Cost Optimization

Resource Quotas: Track GPU usage by namespace/model
Spot Instances: Use for non-critical inference
Scheduled Shutdowns: Auto-shutdown dev clusters after hours
Right-sizing: Match GPU to model requirements

Observability Stack

Essential monitoring for production:

Metrics: GPU utilization, queue depth, latency percentiles
Logs: Correlation IDs for request tracing
Tracing: Identify bottlenecks (preprocessing, inference, post)
Alerts: Queue backup, latency SLA breaches, OOM

Edge and Mobile Deployment

The Edge AI Landscape (2026)

Edge LLMs in 2026 represent a paradigm shift, with models under 9B parameters rivaling cloud giants in specific tasks.

Key Compression Techniques

Technique	Memory Reduction	Quality Impact
4-bit quantization	75%	Moderate
2-bit k-means	90%	Significant
2:4 Sparsity	50%+	Minimal
Knowledge distillation	N/A	Can improve
MoE layer dropping	Variable	Task-dependent

Top Edge Models (2026)

Meta-Llama-3.1-8B-Instruct
GLM-4-9B-0414
Qwen2.5-VL-7B-Instruct

These balance performance with 7-9B parameter counts optimized for edge.

Frameworks for Edge

NVIDIA TensorRT Edge-LLM:

C++ framework for automotive/robotics
EAGLE-3 speculative decoding
NVFP4 quantization
Chunked prefill for memory efficiency

Meta ExecuTorch:

PyTorch models direct to edge devices
Powers Instagram, WhatsApp, Messenger, Facebook
No format conversion required

Cactus SDK:

Sub-50ms time-to-first-token on mobile
Cross-platform (iOS, Android)
Supports Qwen, Gemma, Llama, DeepSeek, Phi, Mistral
Y Combinator backed

Mobile Edge Intelligence (MEI)

Hybrid approach combining:

Small Language Models (SLMs) on device
LLMs at edge servers
Speculative decoding for efficiency

Benefits:

Lower latency than cloud
Reduced bandwidth costs
Privacy preservation
Offline capability

Practical Edge Performance

Llama2-7B on NVIDIA Jetson AGX Orin:
- INT4 quantization
- 7GB memory requirement
- ~4.5 tokens/second

7B model on consumer GPU (6GB+ VRAM):
- 15-25 tokens/second typical
- GGUF Q4_K_M quantization

Benchmarks and Performance

FlashAttention-3 (2025-2026)

The latest attention optimization for Hopper GPUs.

Key Numbers:

BF16: 840 TFLOPS (85% utilization)
FP8: 1.3 PFLOPS (1300 TFLOPS)
Memory: Linear in sequence length vs quadratic

Optimizations:

Asynchronous Tensor Core + TMA overlap
Interleaved matmul and softmax
Block quantization for FP8

Memory Savings:

10x at 2K sequence length
20x at 4K sequence length
Enables much longer contexts

Framework Throughput Comparison

Framework	Throughput (req/s)	TTFT (ms)	Best Config
TensorRT-LLM	Highest	35-50	Low concurrency
SGLang	Up to 3.1x vLLM	Varies	High KV reuse
vLLM	120-160	50-80	High concurrency
llama.cpp	Lower	Fast	Single user

Quantization Quality Benchmarks

HumanEval (Code Generation):

GGUF Q5_K_M: 54.27% (only 2% below FP16 baseline)
AWQ 4-bit:   ~52%
GPTQ 4-bit:  ~50%

Quality Retention:

AWQ:  95%
GGUF: 92%
GPTQ: 90%

H100 vs A100 Real-World

Llama-3.1-70B Inference:
─────────────────────────
H100 (TensorRT-LLM):
- Throughput: 10,000 tok/s possible
- TTFT: ~100ms at scale

A100 (vLLM):
- Throughput: ~2,000-3,000 tok/s
- TTFT: ~200-400ms

Practical Recommendations

Decision Tree: Choosing Your Stack

START
  │
  ├─▶ Production at scale?
  │     ├─▶ Yes: High throughput needed?
  │     │     ├─▶ Yes: SGLang or TensorRT-LLM
  │     │     └─▶ No: vLLM (reliable default)
  │     │
  │     └─▶ No: Local/edge deployment?
  │           ├─▶ GPU available: Ollama or llama.cpp
  │           └─▶ CPU only: llama.cpp with GGUF
  │
  └─▶ Development/prototyping: Ollama

Quantization Selection Guide

Scenario	Recommended	Reason
Production GPU (Hopper)	FP8	Best quality/speed balance
Production GPU (Ampere)	AWQ or INT8	No native FP8
High throughput batch	GPTQ + Marlin	Speed priority
Quality-critical	AWQ or FP8	Best retention
Local/laptop	GGUF Q5_K_M	Versatile
Mobile/edge	GGUF Q4_K	Memory efficiency

Quick Start Configurations

Production (H100, 70B model):

from vllm import LLM

llm = LLM(
    model="meta-llama/Llama-3.1-70B",
    tensor_parallel_size=4,
    quantization="fp8",
    kv_cache_dtype="fp8",
    gpu_memory_utilization=0.9,
    enable_prefix_caching=True,
    enable_chunked_prefill=True,
)

Local Development (16GB GPU):

ollama run llama3.1:8b-instruct-q5_K_M

Edge Deployment:

# llama.cpp
./main -m llama-3.1-8b.Q4_K_M.gguf \
    -c 4096 \
    --threads 8 \
    -ngl 99  # Offload all layers to GPU if available

Common Pitfalls to Avoid

GGUF in vLLM: GGUF preserves quality but has poor vLLM performance. Use llama.cpp instead.
Ignoring Kernels: Algorithm is half the story. Marlin kernels make GPTQ 2.5x faster.
Over-allocating Memory: Leave headroom for KV cache growth.
Static Batching for LLMs: Always use continuous batching for real-time serving.
Skipping Calibration: GPTQ quality depends heavily on calibration dataset selection.

Performance Optimization Checklist

Enable FlashAttention (automatic in modern frameworks)
Use appropriate quantization (FP8 on Hopper)
Configure continuous batching
Enable prefix caching if prompts share prefixes
Set appropriate gpu_memory_utilization (0.85-0.95)
Use tensor parallelism for large models
Consider speculative decoding for latency-sensitive apps
Monitor and tune max_num_seqs based on workload

Summary

Key Takeaways for 2026

FP8 is the new default for Hopper GPUs - near-lossless with 30%+ speed gains
vLLM + PagedAttention remains the production standard with 2-4x throughput improvements
SGLang leads in throughput for agent/RAG workloads with RadixAttention
Continuous batching is essential - up to 23x improvement over static batching
Speculative decoding delivers 2-3x speedup for latency-sensitive applications
H100 pricing has normalized ($3-4/hr) making it the default choice over A100
Edge deployment is maturing with sub-9B models achieving competitive results

The Optimization Stack (Ranked by Impact)

Batching: Continuous batching (23x potential)
Memory: PagedAttention (2-4x)
Precision: FP8 quantization (30% speed)
Attention: FlashAttention-3 (1.5-2x)
Decoding: Speculative (2-3x latency)

Table of Contents

Executive Summary

Key Performance Numbers (2026)

Quantization Methods

Overview of Methods

FP8 Quantization (Recommended for Production)

GPTQ (GPU-Optimized)

AWQ (Activation-Aware)

GGUF (CPU/Edge Optimized)

Quality Comparison by Task

Inference Frameworks

Framework Comparison (2026)

vLLM

SGLang

TensorRT-LLM

llama.cpp

Ollama

Speculative Decoding

How It Works

Performance Gains

Key Techniques (2025-2026)

Implementation in vLLM

Best Practices

KV Cache Optimization

The Problem

PagedAttention (vLLM)

FP8 KV Cache

Advanced Techniques

Memory Planning

Continuous Batching

Batching Strategies Comparison

How Continuous Batching Works

Performance Impact

Framework Support

2025 Advancements: Memory-Aware Dynamic Batching

Implementation Example

Best Practices

Hardware Considerations

NVIDIA GPU Comparison (2026)

H100 vs A100 Decision Matrix

H100 Key Advantages

Price Trends (2025-2026)

Memory Requirements by Model Size

Multi-GPU Strategies

Production Deployment Patterns

Kubernetes-Native Deployment

Architecture Patterns

Autoscaling Configuration

Cost Optimization

Observability Stack

Edge and Mobile Deployment

The Edge AI Landscape (2026)

Key Compression Techniques

Top Edge Models (2026)

Frameworks for Edge

Mobile Edge Intelligence (MEI)

Practical Edge Performance

Benchmarks and Performance

FlashAttention-3 (2025-2026)

Framework Throughput Comparison

Quantization Quality Benchmarks

H100 vs A100 Real-World

Practical Recommendations

Decision Tree: Choosing Your Stack

Quantization Selection Guide

Quick Start Configurations

Common Pitfalls to Avoid

Performance Optimization Checklist

Summary

Key Takeaways for 2026

The Optimization Stack (Ranked by Impact)

References