AMDRDNA 3

Best AI Models for AMD Radeon PRO W7800 (32.0GB)

VRAM:32.0 GB GDDR6·Bandwidth:576.0 GB/s·Stream Processors:4,480·TDP:260W·MSRP:$2,499

32 GB positions this hardware in the professional tier for local AI. Most popular open-source models run comfortably, and even large 70B parameter models are accessible at lower quantization levels.

This memory amount is a sweet spot for enthusiasts and professionals. You can run 13B–30B models like DeepSeek R1 Distill at Q5 or Q6 quality with smooth token generation, and 7B models at near-lossless precision. The 70B class of models (Llama 3 70B, Qwen 72B) becomes possible at Q2–Q3 quantization, though with some quality trade-off. For day-to-day use with coding assistants, chat models, and reasoning tasks, this tier delivers an excellent experience.

Runs Well

  • 7B–13B models at Q6–Q8 quality
  • 14B–30B models at Q4–Q5 quality
  • Small models (3B–7B) at FP16 precision
  • Vision-language models at good quality

Challenging

  • 70B models only at Q2–Q3 (noticeable quality loss)
  • Large context windows with 30B+ models

What LLMs Can AMD Radeon PRO W7800 Run?

254 models · 107 excellent · 77 good

Showing compatibility for AMD Radeon PRO W7800

LLM models compatible with AMD Radeon PRO W7800 — ranked by performance
ModelVRAMGrade
Q4_K_M·75.6 t/s tok/s·262K ctx·RUNS GREAT
16.1 GBS86
Qwen3.8 27B27.8B
Q4_K_M·19.8 t/s tok/s·262K ctx·DECENT
17.4 GBB60
Muse Glimmer 30B29.8B
Q4_K_M·18.8 t/s tok/s·131K ctx·DECENT
18.3 GBB58
Q4_K_M·67.1 t/s tok/s·262K ctx·RUNS GREAT
21.4 GBS85
Q4_K_M·63.6 t/s tok/s·262K ctx·RUNS WELL
19.3 GBA84
Qwen AgentWorld 35B A3B34.7B
Q4_K_M·70.9 t/s tok/s·262K ctx·RUNS GREAT
21.2 GBS86
Q4_K_M·61.0 t/s tok/s·262K ctx·RUNS WELL
21.9 GBA84
Q4_K_M·75.6 t/s tok/s·262K ctx·RUNS GREAT
16.1 GBS86
GLM 4.7 Flash31.2B
Q4_K_M·56.0 t/s tok/s·203K ctx·RUNS WELL
19.8 GBA82
North Mini Code 1.030.5B
Q4_K_M·63.8 t/s tok/s·500K ctx·RUNS WELL
18.7 GBA84
Agents A135.1B
Q4_K_M·67.1 t/s tok/s·262K ctx·RUNS GREAT
21.4 GBS85
Gemma 4 31B IT31.3B
Q4_K_M·16.9 t/s tok/s·262K ctx·DECENT
20.4 GBB56
Q4_K_M·147.0 t/s tok/s·128K ctx·RUNS GREAT
5.5 GBS97
Qwen3.5 9B9.7B
Q4_K_M·54.3 t/s tok/s·262K ctx·RUNS WELL
6.4 GBA81
Q4_K_M·99.5 t/s tok/s·131K ctx·RUNS GREAT
7.7 GBS91
Q4_K_M·64.3 t/s tok/s·262K ctx·RUNS WELL
18.7 GBA84

AMD Radeon PRO W7800 Specifications

Brand
AMD
Architecture
RDNA 3
VRAM
32.0 GB GDDR6
Memory Bandwidth
576.0 GB/s
Stream Processors
4,480
FP16 Performance
90.50 TFLOPS
TDP
260W
Release Date
2023-04-13
MSRP
$2,499

Get Started

Ollama (Recommended)

$curl -fsSL https://ollama.com/install.sh | sh
$ollama run llama3:8b

LM Studio

LM Studio

Download LM Studio, search for a model, and run it with one click.

Prompt Processing

Estimated for Mellum2 12B A2.5B Base, the compute-bound phase that reads your prompt before the first reply token appears.

4,081.5tok/s prefill

Short chat

125 ms

512 tok prompt

Long chat

1.0 s

4,096 tok prompt

Document / codebase

8.0 s

32,768 tok prompt

Prefill is compute-bound and a different number from the decode tok/s shown elsewhere on this page — how prompt processing works →

Efficiency & Value

Based on Mellum2 12B A2.5B Base at ~50.2 tok/s decode.

Tokens per watt

0.19tok/s per W

Higher is better.

$ per tok/s (MSRP)

$49.78

MSRP-based, not street price. Lower is better.

How efficiency & value are calculated →

Performance figures are estimates calibrated as of 2026-09-21 — see calibration basis →

GPUs to Consider Over AMD Radeon PRO W7800

Similar GPUs and upgrades with more VRAM or higher bandwidth for AI

Frequently Asked Questions

Can AMD Radeon PRO W7800 run Gemma 4 26B A4B IT?

Yes, the AMD Radeon PRO W7800 with 32 GB can run Gemma 4 26B A4B IT, Qwen3.8 27B, Muse Glimmer 30B, and 2382 other models. 1250 models run at excellent quality, and 698 at good quality. Check the compatibility table above for the full list with VRAM usage and estimated speed.

Is AMD Radeon PRO W7800 good for AI?

The AMD Radeon PRO W7800 has 32 GB of GDDR6, making it excellent for running local AI models. It supports 1948 models at good quality or better. With 576.0 GB/s memory bandwidth, it delivers solid token generation speeds. This is an enthusiast-grade GPU that handles most popular open-source LLMs.

How many parameters can AMD Radeon PRO W7800 handle?

With 32 GB, the AMD Radeon PRO W7800 supports models from 3B to 30B parameters depending on quantization level. At Q4_K_M (the recommended sweet spot), you can fit roughly 53B parameters. This means 7B models at high quality (Q6/Q8) or 30B+ models at Q4.

What quantization should I use on AMD Radeon PRO W7800?

For the best balance of quality and speed on the AMD Radeon PRO W7800, start with Q4_K_M — it preserves ~85% of the original model quality while keeping VRAM usage reasonable. With 24+ GB, you have the headroom to run 7B models at Q5_K_M or even Q6_K for noticeably better output quality. For larger 30B models, Q4_K_M remains the sweet spot.

How fast is AMD Radeon PRO W7800 for AI inference?

With 576.0 GB/s memory bandwidth, the AMD Radeon PRO W7800 achieves approximately 77 tokens/sec on a 7B model at Q4_K_M — that's very fast, well above conversational speed. A 14B model runs at ~38 tok/s. Token generation speed scales inversely with model size — smaller models are significantly faster.

tok/s = (576 GB/s ÷ model GB) × efficiency

Smaller models = faster inference. Memory bandwidth is the main bottleneck for token generation speed.

Estimated speed on AMD Radeon PRO W7800

Real-world results typically within ±20%. Speed depends on quantization kernel, batch size, and software stack.

Learn more about tok/s estimation →

What's the best model for AMD Radeon PRO W7800?

The top-rated models for the AMD Radeon PRO W7800 are Gemma 4 26B A4B IT, Qwen3.8 27B, Muse Glimmer 30B. The best choice depends on your use case: coding assistants benefit from code-tuned models, while general chat works well with instruction-tuned models like Llama or Qwen.

What power supply and cooling does AMD Radeon PRO W7800 need?

The AMD Radeon PRO W7800 has a TDP of 260 W. A good rule of thumb is to provide at least double the GPU's TDP to cover the rest of the system — that means a 550 W PSU or larger. A mid-tower case with one intake and one rear exhaust is usually sufficient. Keep dust filters clean, as sustained inference generates continuous heat rather than the brief spikes typical of gaming.