---
type: reference
description: "This page maps LLM quantization formats to the GPU and CPU architectures that support them."
tags:
  - llm
  - quantization
timestamp: 2026-08-28
---

# Quantization by Hardware Architecture

## Overview

This page maps LLM quantization formats to the GPU and CPU architectures that support them. It answers one question: which quantization type works well on a given piece of hardware?

Use this page when you choose a model format for Dankserver or Dankclaw. The short answer:

- NVIDIA Ampere (RTX 30 series): AWQ or GPTQ 4-bit.
- NVIDIA Ada (RTX 40 series): FP8 or AWQ.
- NVIDIA Hopper / Blackwell (H100, RTX 50 series): FP8, NVFP4.
- AMD CDNA (MI300 series): FP8 or MXFP4.
- AMD RDNA 3 / Strix Halo (gfx11): Quark INT4 (vLLM), GGUF Q4_K_M, or ROCmFP4.
- CPU-only: any GGUF quant.

## Quantization Types

### FP8 (E4M3)

FP8 uses 8 bits per weight. vLLM supports two modes: W8A8 (quantize weights and activations) and W8A16 (weights only).

| Hardware | Native support | Notes |
|----------|----------------|-------|
| NVIDIA Hopper (SM 9.0) | yes | full FP8 tensor cores |
| NVIDIA Ada (SM 8.9) | yes | full FP8 tensor cores |
| NVIDIA Ampere (SM 8.0/8.6) | no | W8A16 via Marlin; W8A8 runs via fallback |
| NVIDIA Turing (SM 7.5) | no | W8A16 via Marlin only |
| NVIDIA Blackwell (SM 12.0) | yes | FP8 and native FP4 |
| AMD MI300/MI350 (gfx942/gfx950) | yes | via ROCm AITER |
| AMD MI250/MI325 | no | simulation: dequantize to FP16 |
| AMD RDNA 3/3.5 (gfx11/gfx1151) | no | software emulation only |
| CPU | no | FP8 models run via dequant |

Rules:

- Use FP8 on Ada, Hopper, Blackwell, or MI300+.
- On Ampere (RTX 3090), FP8 gives memory savings only. Run AWQ or BF16 instead.
- Do not expect compute speedup from FP8 on RDNA or on the RTX 3090.

### INT8

INT8 W8A8 is broadly supported.

| Hardware | Native support |
|----------|----------------|
| NVIDIA Volta through Blackwell | yes |
| AMD MI300/MI350 | yes |
| AMD RDNA 3/3.5 | no |
| x86 CPU | yes, via llama.cpp or llm-compressor |

### INT4 (AWQ, GPTQ)

INT4 is the standard choice for 24 GB GPUs such as the RTX 3090.

| Hardware | AWQ | GPTQ |
|----------|-----|------|
| NVIDIA Turing | yes | yes |
| NVIDIA Ampere | yes | yes |
| NVIDIA Ada / Hopper / Blackwell | yes | yes |
| AMD ROCm (vLLM) | no | no |
| AMD ROCm (Quark) | yes | yes |
| AMD ROCm GGUF | yes | yes |

Rules:

- AWQ is the default choice for NVIDIA with vLLM.
- On AMD, use GGUF INT4 or the AMD Quark toolkit. vLLM does not list AWQ or GPTQ in its native ROCm matrix.

### GGUF (Q4_0, Q5_K_M, Q8_0, etc.)

GGUF runs on CPU everywhere. GPU acceleration is available through CUDA, ROCm, SYCL, and Vulkan backends. This is the format llama.cpp uses.

Good GGUF quant choices:

| Use case | Quant |
|----------|-------|
| Quality first | Q8_0, Q6_K |
| Balanced | Q5_K_M, Q4_K_M |
| Small footprint | Q4_0, IQ4_XS |
| Smallest | IQ2_XS, Q2_K |

### FP4

FP4 has two variants: NVFP4 (NVIDIA) and MXFP4 (OCP standard).

| Hardware | NVFP4 | MXFP4 |
|----------|-------|-------|
| NVIDIA Blackwell | native | aligned |
| NVIDIA Ada / Ampere | emulation | emulation |
| AMD MI300X/MI355 | via ROCm | native |
| AMD MI250/MI325 | simulation | simulation |
| AMD RDNA 3 | emulation | aligned, beta |

### ROCmFP4 (ROCmFPX fork)

ROCmFP4 is a GGUF weight format from the ROCmFPX llama.cpp fork. It targets AMD hardware, especially Strix Halo (gfx1151). It is roughly 4.25 bpw and about 12% smaller than Q4_K_M. It uses HIP/ROCm and Vulkan kernels.

Use ROCmFP4 only with the ROCmFPX fork of llama.cpp. Upstream llama.cpp cannot read these files.

## Choices for This Project

### Dankserver (RTX 3090, 24 GB, Ampere)

The RTX 3090 has no native FP8 or FP4 tensor cores. Use:

| Ranking | Format | Why |
|---------|--------|-----|
| 1 | AWQ 4-bit | best fit for 24 GB; native kernels |
| 2 | GPTQ 4-bit | good alternative to AWQ |
| 3 | BF16 + KV offload | quality reference; CPU-offload experts |
| 4 | FP8 | only if the FP8 file is all you have; memory savings, no compute win |

### Dankclaw (Strix Halo gfx1151, RDNA 3.5)

RDNA 3.5 has no native FP8 tensor cores. The APU shares memory. The active Ling-3.0-flash service runs vLLM-ROCm with the AMD Quark INT4 (W4A16) format (the jcbtc CIRU Strix-native bundle). INT4 weights keep the 124B model inside the 96 GiB frame buffer with a full-precision KV cache.

| Ranking | Format | Why |
|---------|--------|-----|
| 1 | INT4 W4A16 (Quark/vLLM) | active; fits 96 GiB; native ROCm kernels; concurrency |
| 2 | MXFP4 (Quark) | same-bit, higher accuracy; beta on gfx1151 (A/B experiment) |
| 3 | GGUF Q4_K_M / Q8_0 | works with upstream llama.cpp only |
| 4 | FP8 (vLLM) | software emulation; memory savings only; no compute win |

## See Also

- [[DFlash DSpark Speculative Decoding]]
- [[Running Qwen3.6 35B A3B FP8 on Dankserver]]
- [[Running Qwen3.6 27B FP8 on Dankclaw]]
- [[Dankclaw System Specs]]
- [[Dankserver System Specs]]
- [[VLLM Overview]]
- [[Shared Memory Architecture Dankclaw]]