Fewer bits, measured.
Quantization squeezes models, signals and sound into fewer bits. We show you exactly what that saves — and what it costs — with live calculators and plain-English guides.
Drag the slider. The blue curve is the original signal, the teal staircase is what survives at that bit depth, and the magenta trace is the error you pay.
- Bits
- 3
- Levels
- 8
- Signal-to-noise
- —
Run bigger models on smaller hardware
GGUF, GPTQ, AWQ, FP8 and 4-bit formats, VRAM maths and how much quality you trade.
Signals and audioTurn voltages into numbers
ADC resolution, the 6.02N + 1.76 dB rule, bit depth, dither and noise shaping.
Music timingTighten a groove without killing it
Grid values, quantize strength, swing and groove templates in any DAW.
PhysicsWhy energy comes in steps
Planck, Bohr's hydrogen levels, particles in boxes and quantum numbers.
Will it fit on your GPU?
A quick check using parameters × bits ÷ 8, plus 1 GB and 5% for runtime overhead, with a short context. For KV cache, long context and 20 GPU presets, open the full calculator.
What 4-bit actually buys you
An 8-billion-parameter model stored at 16 bits needs about 16 GB just for its weights. The same model in the popular Q4_K_M format averages roughly 4.9 bits per weight, so it needs about 4.9 GB — small enough for a mid-range laptop GPU.
| Format | Bits / weight | Size | Typical use |
|---|---|---|---|
| F16 | 16.0 | 14.96 GiB | Reference quality |
| Q8_0 | 8.50 | 7.95 GiB | Near-lossless |
| Q6_K | 6.56 | 6.14 GiB | Quality-first |
| Q5_K_M | 5.70 | 5.33 GiB | Balanced |
| Q4_K_M | 4.89 | 4.58 GiB | Common default |
| Q3_K_M | 4.00 | 3.74 GiB | Tight memory |
Source: llama.cpp quantize README. Compare 32 formats in the formats atlas.
Tools
Every calculator shows its working, runs in your browser and stores nothing on a server.
LLM VRAM calculator
Weights, KV cache and overhead for any model, quant and context length — with a fit check across 20 GPUs and Macs.
Quantization playground
Quantize a signal or a weight distribution to 1–16 bits and watch the error, histogram and SQNR change live.
ADC and bit-depth calculator
LSB size, ideal SNR, ENOB from measured SINAD, oversampling gain and audio file size in one panel.
MIDI quantize and swing lab
Play a loose drum loop, then quantize it with strength and swing and hear the difference in the browser.
Energy levels calculator
Hydrogen transitions with photon wavelength and colour, plus particle-in-a-box energies.
Start reading
Guides written for the person who just searched the term and for the engineer who needs the exact number.
- Signals & audio
ADC quantization error, SQNR and ENOB explained
LSB size, the ±½ LSB error bound, the Δ²/12 noise model, the 6.02N + 1.76 dB derivation, ENOB, oversampling and dither in one place.
- AI models
GGUF quant types explained: legacy, k-quants and i-quants
Decode GGUF names like Q4_K_M and IQ3_XXS, compare bits per weight, and learn to pick and build the right llama.cpp quant.
- AI models
KV cache quantization: the memory math and how to do it
The KV cache formula, worked Llama 3.1 examples at 8K and 128K context, and how to quantize the cache in llama.cpp, Ollama and vLLM.
- AI models
LLM quantization: the complete practical guide
Memory math, method families from GPTQ to NVFP4, quality metrics, and a hardware-first checklist for quantizing LLMs.
- Music timing
Quantize in music production: grid, strength, swing and groove
Grid values in milliseconds, quantize strength, swing as a ratio, groove templates, audio quantize and how to tighten timing without losing feel.
- Foundations
What is quantization? One idea behind AI, audio, music and physics
How mapping values to discrete levels works, the math of its error, and where the same idea shows up, from 4-bit LLMs to atoms.
Serving a model in production? Shrink its bill.
Most teams run inference at 16 bits by default. Moving to a well-chosen 8-bit or 4-bit format can cut the memory a model needs by half or more, which often means fewer or smaller GPUs. Tell us what you run and we send back a free, specific optimization plan.
Get my free optimization planEstimated savings
— per month
≈ — per year. A rough illustration, not a quote: real savings depend on your traffic, latency targets and hardware.
Watch
Hand-picked explainers. Each video loads only when you press play.
Optimize your AI: quantization explained
Matt WilliamsHow LLMs survive in low precision
Julia TurcQuantization part 6: quantization noise demo
On YouTubeThe Quantization Challenge
Compress an open model to fit in 8 GB and keep as much quality as you can. Transparent scoring, public leaderboard, prizes funded by sponsors and supporters.
See the brief and registerKeep the tools free
Support pays for hosting, new tools, writers and engineers, and the prize pool. Every pledge is acknowledged and allocated in the open.
Support the projectQuestions people ask first
Short answers; each links to the full guide.
What is quantization, in one sentence?
It is representing values with fewer distinct levels — fewer bits — by rounding each value to the nearest allowed level, which saves memory and bandwidth at the cost of a small, measurable error. Read the full explanation.
Does quantizing an LLM make it worse?
Slightly, and how slightly depends on the bit width and method. 8-bit formats are usually close to the original; 4-bit formats lose a little more; below about 3 bits quality drops faster. Measure it on your own task rather than trusting a single benchmark. See the guide.
Which GGUF file should I download?
Q4_K_M is a common default when memory is tight, Q5_K_M or Q6_K if you have room, and Q8_0 if you want near-reference quality. Use the VRAM calculator to check what fits, then read GGUF quant types explained.
Is it the same "quantization" as in audio or physics?
The digital meaning — rounding to discrete levels — is shared by AI, ADCs, audio and MIDI timing. In physics, quantization describes nature itself: some quantities can only take discrete values. The intuition of steps is the same; the cause is different. Physics guide.
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Through advertising, sponsorships, reader support and optional paid help for teams who ask for it. Sponsors never decide what we write; see our editorial policy.
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