Model Formats
MUX
what is GGUF model format in LLM?
GGUF is a binary model file format used primarily by llama.cpp–based runtimes to run large language models efficiently on local CPUs and consumer GPUs.
It is designed for fast inference, low memory overhead, and broad hardware compatibility, especially on laptops and edge devices.
What GGUF Stands For
GGUF = “GGML Unified Format”
It is the successor to GGML and GGJT, created by Georgi Gerganov (author of llama.cpp) to standardize model storage and metadata.
Why GGUF Exists
Traditional formats (PyTorch .bin, .safetensors) are optimized for training, not inference.
GGUF was built to solve:
Large RAM consumption
Slow load times
Poor CPU / Apple Silicon performance
Fragmented metadata handling
What a GGUF File Contains
A GGUF file bundles everything needed for inference into a single binary:
Weights
Quantized tensor data (Q2–Q8, K-quants, etc.)
Metadata
Architecture, tokenizer, context size
Tokenizer
Embedded vocab + merges
Rope params
RoPE scaling, theta
Chat template
Jinja-style system/user format
Special tokens
BOS, EOS, PAD
KV-cache info
Context & attention params
This eliminates runtime guesswork and external config files.
Why GGUF Is Popular
1. Quantization-Friendly
GGUF supports many quantization schemes:
Q2 / Q3
Extreme memory saving
Q4_K_M
Best quality–size balance
Q5 / Q6
Higher accuracy
Q8
Near-FP16 quality
This allows 70B models to run on laptops.
2. Hardware-Optimized Inference
GGUF is optimized for:
CPU (AVX2 / AVX512)
Apple Silicon (Metal / MPS)
Low-VRAM GPUs
No CUDA is required.
3. Single-File Deployment
One .gguf file:
No tokenizer files
No config JSONs
No HuggingFace dependencies
This makes GGUF ideal for:
Offline LLMs
Edge devices
Desktop apps (LM Studio, Ollama)
Where GGUF Is Used
Popular runtimes supporting GGUF:
llama.cppLM Studio
Ollama
text-generation-webuikoboldcpp
GGUF vs Other Model Formats
.bin (PyTorch)
Research / training
Yes
Poor
.safetensors
Secure training
Yes
Medium
GGUF
Local inference
No
Excellent
.mlx
Apple MLX
Limited
Good
TensorRT
NVIDIA prod
No
Excellent (GPU only)
GGUF Limitations
GGUF is inference-only.
It does not support:
Training
Fine-tuning
Gradient updates
Backpropagation
For training, you must use:
PyTorch / Safetensors
LoRA / QLoRA adapters
Typical GGUF Workflow
When You Should Use GGUF
Use GGUF if:
You want local, private inference
You run on CPU or Apple Silicon
You want low RAM / VRAM usage
You do not need training
Avoid GGUF if:
You plan to fine-tune or train
You require distributed GPU inference
You use vLLM / Triton / CUDA stacks
One-Line Summary
GGUF is the de-facto standard format for running quantized LLMs locally, optimized for fast, low-memory inference on CPUs and Apple Silicon.
If you want, I can also explain:
GGUF vs MLX in depth
Which quant level to pick for your Mac M4
How to convert HF → GGUF step-by-step
GGUF vs vLLM / TensorRT inference trade-offs
What Is ONNX?
ONNX (Open Neural Network Exchange) is an open, framework-agnostic model format that allows machine learning models to be exported once and run anywhere across different runtimes, hardware, and deployment environments.
It acts as a universal intermediate representation (IR) for neural networks.
Why ONNX Exists
Before ONNX:
PyTorch models ran best in PyTorch
TensorFlow models ran best in TensorFlow
Deployment stacks were tightly coupled to training frameworks
ONNX solves this by decoupling training from inference.
Train in one framework → deploy in another runtime.
What an ONNX Model Contains
An .onnx file is a protobuf graph that includes:
Graph
Directed acyclic computation graph
Ops
Standardized ONNX operators (Conv, MatMul, Attention)
Weights
Serialized tensors
Shapes
Static or dynamic tensor shapes
Metadata
Model info and versioning
Unlike GGUF, ONNX does not bundle tokenizers or chat templates.
ONNX Runtime (ORT)
ONNX itself is just a format. Execution is handled by ONNX Runtime.
Execution Providers
ONNX Runtime selects the best backend automatically:
CPUExecutionProvider
x86 / ARM
CUDAExecutionProvider
NVIDIA GPUs
TensorRT EP
Optimized NVIDIA inference
DirectML
Windows GPUs
CoreML EP
Apple Silicon
OpenVINO EP
Intel CPUs / VPUs
ONNX in LLM Context
For LLMs, ONNX is commonly used for:
Optimized inference
Cross-platform deployment
Vendor-neutral serving
Typical flow:
ONNX vs GGUF (Key Differences)
Purpose
Interoperability
Local inference
Training support
Export only
No
Graph-based
Yes
No (tensor blobs)
Tokenizer included
No
Yes
Hardware
CPU, GPU, accelerators
CPU, Apple GPU
Quantization
Post-training, Q/DQ
Native quant formats
Production use
High
Medium
Edge/offline
Medium
Excellent
ONNX Quantization
ONNX supports:
Post-Training Quantization (PTQ)
Quantization-Aware Training (QAT)
Common formats:
INT8
INT4 (limited operator support)
FP16
Quantization is handled via ONNX Runtime tooling.
Strengths of ONNX
Vendor-neutral
Framework-agnostic
Strong enterprise & cloud support
Excellent GPU acceleration
Stable ABI for production
Limitations of ONNX
Export can be fragile for complex models
Dynamic control flow is limited
LLM attention ops may need custom kernels
Tokenization and prompting are external
Not optimized for hobbyist local runs
When You Should Use ONNX
Use ONNX if:
You deploy to production inference
You need cross-hardware portability
You want TensorRT / OpenVINO acceleration
You operate in regulated enterprise stacks
Avoid ONNX if:
You want simple local inference
You rely on rapid model iteration
You need training or fine-tuning
One-Line Summary
ONNX is a universal, production-grade model exchange format that enables framework-independent, hardware-accelerated inference across platforms.
If useful, I can also provide:
ONNX vs TensorRT vs vLLM
Exporting LLMs to ONNX (HF → ORT)
ONNX on Apple Silicon vs MLX
Performance tuning for transformer models
Can I use ONNX format in LLM models?
Yes — you can use ONNX with LLMs, but only for inference, and with important architectural and operational constraints.
Below is a precise, engineering-level explanation.
Short Answer
ONNX is viable for LLM inference in production environments, but it is not the default or easiest path, and it is not suitable for training or rapid experimentation.
Where ONNX Fits in the LLM Stack
Typical flow:
What ONNX Supports for LLMs
Supported
Decoder-only transformers (LLaMA, GPT-style)
Encoder–decoder models (T5, BART)
Static or semi-dynamic shapes
KV-cache–based decoding
INT8 / FP16 inference
GPU, CPU, accelerator backends
Not Supported / Painful
Training or fine-tuning
LoRA / adapters (must be merged first)
Highly dynamic control flow
Rapid prompt iteration
Tool calling logic
Tokenization and chat templates
Common Ways ONNX Is Used with LLMs
1. Production Inference (Most Common)
Cloud deployment
Regulated environments
Hardware-specific acceleration
Used by:
Microsoft (ORT)
Azure AI
Enterprise inference stacks
2. Optimized GPU Pipelines
ONNX → TensorRT EP:
Fused attention
Kernel autotuning
High throughput
Best for:
NVIDIA A100 / H100
Fixed batch sizes
3. Edge / Embedded Deployment
ONNX → OpenVINO / DirectML / CoreML:
CPUs
VPUs
Apple Silicon
ONNX vs Common LLM Runtimes
ONNX Runtime
Enterprise inference
No
Medium
vLLM
High-throughput serving
No
Easy
TensorRT-LLM
NVIDIA prod
No
Hard
GGUF / llama.cpp
Local inference
No
Very easy
MLX
Apple local
Limited
Easy
Quantization with ONNX
ONNX supports:
INT8 (stable)
FP16 (common)
INT4 (experimental, limited ops)
Quantization methods:
Post-Training Quantization (PTQ)
Quantization-Aware Training (QAT)
Note:
ONNX quantization is less flexible than GGUF’s K-quants
Often requires operator-specific tuning
Apple Silicon Reality Check
ONNX does run on Apple Silicon via:
CoreML Execution Provider
CPU fallback
However:
Performance is usually worse than MLX or GGUF
Export complexity is higher
Practical Recommendation
Use ONNX if:
You deploy LLMs in enterprise or cloud
You need hardware neutrality
You rely on TensorRT / OpenVINO
Model architecture is stable
Do NOT use ONNX if:
You want local experimentation
You fine-tune frequently
You use adapters dynamically
You want simplicity (LM Studio / Ollama)
One-Line Verdict
ONNX is a production-grade inference format for LLMs, best suited for stable, optimized, enterprise deployments—not for training or casual local use.
If you want, I can:
Show a HF → ONNX export example for LLaMA
Compare ONNX vs vLLM latency/throughput
Explain KV-cache handling in ONNX
Design a mixed ONNX + vLLM deployment architecture
What is MUX format in LLM?
There is no widely accepted or standardized “MUX” model format in the LLM ecosystem.
When people say “MUX” in the context of LLMs, they are usually referring to one of a few non-standard or overloaded concepts, not a formal model file format like GGUF, ONNX, or Safetensors.
Below is a precise breakdown to remove ambiguity.
1. What “MUX” Is Not
❌ Not an official model serialization format
❌ Not a Hugging Face standard
❌ Not used for training or fine-tuning
❌ Not comparable to GGUF / ONNX / Safetensors
If someone presents “MUX” as a model format, that is technically inaccurate.
2. Common Meanings of “MUX” in LLM Discussions
A. Multiplexing (Most Common Meaning)
In systems terminology, MUX = multiplexer.
In LLM systems, this usually means:
Routing requests to multiple models
Routing requests to multiple adapters (LoRA)
Selecting one of many inference backends at runtime
Examples:
Adapter routing (LoRA A vs LoRA B)
MoE (Mixture of Experts) gating
API gateway model selection
This is runtime orchestration, not a file format.
B. LoRA / Adapter Multiplexing (“LoRA MUX”)
In advanced serving stacks:
One base model
Multiple LoRA adapters
Adapter selected per request
Sometimes informally called:
“LoRA mux”
“adapter mux”
“model muxing”
Used in:
vLLM adapter routing
TGI multi-adapter setups
Custom inference gateways
Again: not a format.
C. Internal / Vendor-Specific Containers
Some tools or startups may use MUX as:
An internal container
A wrapper that bundles:
Base model
Adapters
Metadata
Routing rules
These are:
Tool-specific
Not portable
Not interoperable
Not recognized by the broader ecosystem
Comparable to a deployment artifact, not a model format.
3. Comparison with Real Model Formats
GGUF
Quantized inference file format
ONNX
Graph-based inference exchange format
Safetensors
Secure tensor storage
MLX
Apple MLX tensor format
MUX
❌ Not a standard format
4. Why the Confusion Happens
“MUX” sounds like:
A format name
A low-level systems artifact
But in LLMs, it is usually shorthand for:
Routing
Selection
Composition
Serving-layer logic
Not serialization.
5. One-Line Verdict
“MUX” is not an LLM model format; it typically refers to multiplexing logic (model, adapter, or request routing) at inference or serving time.
Next Step (Important)
To give you a 100% accurate answer, I need one clarification:
Where did you encounter “MUX”?
A GitHub repo?
LM Studio / Ollama?
vLLM / TGI docs?
A file extension (.mux)?
A vendor tool?
If you share the source, I can map it precisely and confirm whether it is:
A container
A routing mechanism
A mislabeled format
Or a proprietary artifact
What is MUX format in LLM?
There is no widely accepted or standardized “MUX” model format in the LLM ecosystem.
When people say “MUX” in the context of LLMs, they are usually referring to one of a few non-standard or overloaded concepts, not a formal model file format like GGUF, ONNX, or Safetensors.
Below is a precise breakdown to remove ambiguity.
1. What “MUX” Is Not
❌ Not an official model serialization format
❌ Not a Hugging Face standard
❌ Not used for training or fine-tuning
❌ Not comparable to GGUF / ONNX / Safetensors
If someone presents “MUX” as a model format, that is technically inaccurate.
2. Common Meanings of “MUX” in LLM Discussions
A. Multiplexing (Most Common Meaning)
In systems terminology, MUX = multiplexer.
In LLM systems, this usually means:
Routing requests to multiple models
Routing requests to multiple adapters (LoRA)
Selecting one of many inference backends at runtime
Examples:
Adapter routing (LoRA A vs LoRA B)
MoE (Mixture of Experts) gating
API gateway model selection
This is runtime orchestration, not a file format.
B. LoRA / Adapter Multiplexing (“LoRA MUX”)
In advanced serving stacks:
One base model
Multiple LoRA adapters
Adapter selected per request
Sometimes informally called:
“LoRA mux”
“adapter mux”
“model muxing”
Used in:
vLLM adapter routing
TGI multi-adapter setups
Custom inference gateways
Again: not a format.
C. Internal / Vendor-Specific Containers
Some tools or startups may use MUX as:
An internal container
A wrapper that bundles:
Base model
Adapters
Metadata
Routing rules
These are:
Tool-specific
Not portable
Not interoperable
Not recognized by the broader ecosystem
Comparable to a deployment artifact, not a model format.
3. Comparison with Real Model Formats
GGUF
Quantized inference file format
ONNX
Graph-based inference exchange format
Safetensors
Secure tensor storage
MLX
Apple MLX tensor format
MUX
❌ Not a standard format
4. Why the Confusion Happens
“MUX” sounds like:
A format name
A low-level systems artifact
But in LLMs, it is usually shorthand for:
Routing
Selection
Composition
Serving-layer logic
Not serialization.
5. One-Line Verdict
“MUX” is not an LLM model format; it typically refers to multiplexing logic (model, adapter, or request routing) at inference or serving time.
What are the other formats available in LLMs
Below is a comprehensive, structured overview of the major formats used across the LLM lifecycle, grouped by training, inference, optimization, and deployment. This will give you a clear mental map of where each format fits and why it exists.
1. Training & Fine-Tuning Formats (Authoritative Weights)
These are used during model development, training, and fine-tuning.
PyTorch .bin
.binNative PyTorch checkpoint format
Flexible, but unsafe (pickle-based)
Common in research and early releases
Use case: Training, experimentation
Safetensors (.safetensors)
.safetensors)Secure, zero-copy tensor format
Faster loading than
.binHugging Face default
Use case: Training, fine-tuning, production-safe storage
TensorFlow SavedModel
TensorFlow-native format
Rare for modern LLMs
Use case: Legacy TF pipelines
2. Adapter / Fine-Tuning Artifacts
Used to modify base models without retraining.
LoRA / QLoRA Adapters
Small delta-weight files
Require base model
Usually stored as Safetensors
Use case: Parameter-efficient fine-tuning
Merged Weights
LoRA merged back into base model
Converted into inference formats later
Use case: Production inference
3. Inference-Optimized Formats (Most Important)
Designed for fast, memory-efficient inference.
GGUF
Quantized, inference-only
CPU & Apple Silicon optimized
Includes tokenizer + metadata
Use case: Local inference, offline LLMs Used by: llama.cpp, Ollama, LM Studio
ONNX
Graph-based, hardware-agnostic
Enterprise-grade inference
Requires external tokenizer
Use case: Production inference, accelerators
TensorRT / TensorRT-LLM
NVIDIA-specific compiled engines
Extremely fast
Hardware-locked
Use case: NVIDIA A100/H100 production serving
OpenVINO IR
Intel inference format
CPU / VPU optimized
Use case: Intel hardware deployments
4. Apple-Specific Formats
MLX
Apple’s tensor framework
Unified memory optimized
Python-native
Use case: Local inference & fine-tuning on Apple Silicon
CoreML
Apple deployment format
Used in iOS/macOS apps
Use case: Shipping LLMs inside Apple apps
5. Quantized / Lightweight Formats
GPTQ
Post-training quantization
GPU-focused
Often intermediate
Use case: GPU inference with reduced VRAM
AWQ
Activation-aware quantization
Better accuracy than GPTQ
Use case: High-quality GPU quantization
INT8 / INT4 (Generic)
Used across ONNX / TensorRT
Operator-dependent support
Use case: Accelerator-friendly inference
6. Serving / Runtime-Specific Artifacts
These are not true model formats, but deployment artifacts.
vLLM Internal Shards
Runtime-managed weight layout
Optimized for paged attention
Use case: High-throughput serving
TGI (Text Generation Inference)
Uses Safetensors internally
Adds serving metadata
Use case: HF production endpoints
7. Research & Interchange Formats
HDF5
Scientific computing format
Rare in LLMs now
FlatBuffers / Protobuf
Used internally by ONNX, CoreML
8. Summary Table (Quick Reference)
Training
.bin, .safetensors, SavedModel
Adapters
LoRA, QLoRA
Local Inference
GGUF, MLX
Enterprise Inference
ONNX, TensorRT, OpenVINO
Apple Deployment
MLX, CoreML
Quantization
GPTQ, AWQ, INT8/INT4
Serving Artifacts
vLLM, TGI internals
Key Mental Model
Train → Safetensors
Fine-tune → LoRA
Local run → GGUF / MLX
Enterprise deploy → ONNX / TensorRT
Apple native → MLX / CoreML
Final One-Line Summary
LLM “formats” are not one thing—they are specialized artifacts optimized for different phases: training, fine-tuning, inference, and deployment.
If you want, I can:
Recommend the best format for your Mac M4 Max
Design a single model → multi-format pipeline
Compare GGUF vs ONNX vs MLX with benchmarks
Explain format choice for startups vs enterprises
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