> ## Documentation Index
> Fetch the complete documentation index at: https://phaseo.app/docs/llms.txt
> Use this file to discover all available pages before exploring further.

> ## Agent Instructions
> Phaseo Gateway base URL: https://api.phaseo.app/v1. Send `Authorization: Bearer $PHASEO_API_KEY` with every request.
> Model IDs look like `provider/model`, for example `openai/gpt-6-astra`. Do not guess IDs: list them with `GET /v1/models` or check https://phaseo.app/models. `phaseo/free` routes to an available free model and needs no wallet balance.
> For new text integrations prefer `POST /v1/responses`. `POST /v1/chat/completions` (OpenAI format) and `POST /v1/messages` (Anthropic format) suit existing code.
> The full API contract is https://phaseo.app/docs/openapi/v1/openapi.yaml. Official SDKs: `@phaseo/sdk` (TypeScript) and `phaseo` (Python), plus Go, C#, PHP, Ruby and Rust.
> Search these docs over MCP at https://phaseo.app/docs/mcp, and install Phaseo skills with `npx skills add https://phaseo.app/docs`. If a page is wrong or out of date, report it with the MCP `submit_feedback` tool.
> Manage Phaseo accounts with the `phaseo` CLI (`npm install -g @phaseo/cli`, then `phaseo login` or `phaseo login --device-code`), the `/v1` Management API or the MCP server at `https://mcp.phaseo.app/mcp?access=manage`. Do not send users to the dashboard for account tasks. A person is still needed for sign-up, approving a sign-in, paying the first top-up on the checkout link that `phaseo credits add` returns when no card is saved, confirming each saved-card top-up from the emailed link, and account security (password, MFA, passkeys). Create API keys with `phaseo keys create --name <name> --json`; never guess one. Credit top-ups need `phaseo login --scopes billing:write` first. Task map: https://phaseo.app/docs/v1/developers/manage-from-an-agent.md.

# Quantization Formats

> BF16, FP16, FP8, FP4, INT8, INT4 and what each format means in practice.

This guide explains common precision formats you will see in model variants and inference runtimes.

## Quick reference

| Format | Typical usage | Main benefit | Main risk |
| - | - | - | - |
| `FP32` | Baseline/full precision | Maximum numerical stability | Highest memory and compute cost |
| `BF16` | Training + high-quality inference | Strong quality with better efficiency vs FP32 | Still heavier than 8-bit/4-bit |
| `FP16` | Common inference/training paths | Good speed and memory reduction | Lower dynamic range than BF16 |
| `FP8` | Throughput-focused inference/training | Large speed/memory gains | Noticeable quality regressions on difficult prompts |
| `FP4` | Aggressive efficiency modes | Very high density and low cost | Higher output degradation risk |
| `INT8` | Widely supported inference quantization | Strong efficiency with moderate quality impact | Calibration/sensitivity issues for some models |
| `INT4` | Cost-sensitive deployment | Very low memory and high throughput | Bigger quality and robustness tradeoffs |

## BF16 vs FP16

* `BF16` keeps a larger exponent range, which helps stability.
* `FP16` is still common and often fast, but can be less stable for some workloads.
* For quality-critical production paths, `BF16` is often the safer default when available.

## FP8 and FP4

* `FP8` usually gives a good efficiency jump while keeping usable quality for many tasks.
* `FP4` pushes efficiency further, but degradation is more likely (especially nuanced reasoning, long contexts, and strict structured outputs).

## Integer formats (INT8 / INT4)

* Usually optimized for inference throughput and memory.
* Quality depends heavily on quantization method, calibration, and model architecture.
* `INT8` is generally easier to deploy safely than `INT4`.

## Reading model labels

You may see labels like:

* `bf16`, `fp16`, `fp8`, `int8`, `q4`, `4bit`, `8bit`

Naming is not fully standardized across providers. Always verify exact variant details in the model/provider documentation.


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