AMD Radeon GPU Workloads

AMD Radeon GPU Workloads | Generated by AI

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Question: What AI workloads can run on this AMD Radeon Cloud GPU server (gfx1100, ~48GB VRAM)?

Answer:

Your server is basically a Radeon Pro W7900-class / Navi 31 RDNA3 GPU environment:

GPU: gfx1100
VRAM: ~48GB (likely 2×24GB memory devices exposed as one pool, or a special cloud partition)
ROCm: 6.16
CPU: 128 threads EPYC
RAM: 503GB

This is a very capable single-GPU inference + small/medium training machine.

1. LLM inference

Easily runs

Model	Params	Quant	VRAM
Llama 3.1 8B	8B	FP16	~16GB
Qwen3 8B	8B	FP16	~16GB
Qwen3 14B	14B	FP16	~28GB
Qwen3 30B-A3B	MoE	FP16	~35-45GB
DeepSeek-R1-Distill-Qwen-32B	32B	Q4/Q5	~25GB
Llama 3.1 70B	70B	Q4	~40-45GB

You should be able to run:

vllm serve Qwen/Qwen3-32B \
  --dtype float16 \
  --gpu-memory-utilization 0.9

or:

vllm serve deepseek-ai/DeepSeek-R1-Distill-Qwen-32B

For 70B:

vllm serve meta-llama/Llama-3.1-70B-Instruct \
  --quantization awq

2. ThinkingMachines Inkling

The model you linked:

Thinking Machines Lab Inkling

If it is a smaller reasoning model / research checkpoint, this GPU is enough.

Typical mapping:

7B-14B:
    FP16 inference ✅

30B:
    FP16 borderline
    INT8/Q8 easy

70B:
    quantized only

3. Image generation

Very suitable.

Stable Diffusion

Runs easily:

pip install diffusers transformers accelerate

Examples:

SD 1.5 ✅
SDXL ✅
FLUX.1-dev ⚠️
FLUX.1-schnell ✅

VRAM:

Model	VRAM
SD1.5	6GB
SDXL	12-16GB
FLUX schnell	20-24GB
FLUX dev	32GB+

Your 48GB gives plenty of room.

4. Video generation

Possible:

Good

Stable Video Diffusion
AnimateDiff
CogVideoX-5B

Example:

CogVideoX-5B
FP16:
~24GB VRAM

Hard

HunyuanVideo
Wan 2.1 14B

Need optimization:

FP8
CPU offload
attention slicing

5. Fine-tuning / LoRA

This machine is very good for LoRA.

Examples:

Qwen3-8B LoRA

model:
8B

training:
bf16

batch:
2-4

context:
8k-16k

Easy.

Qwen3-32B LoRA

Possible:

QLoRA 4bit
+ bitsandbytes
+ PEFT

Example:

model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen3-32B",
    load_in_4bit=True
)

6. Train models from scratch

Compared with your RTX 4070 (12GB):

This is a huge upgrade.

Your 4070:

VRAM:
12GB

GPT-2 124M:
easy

GPT-2 760M:
painful

This AMD:

VRAM:
48GB

GPT-2 1.5B:
possible

Llama-style 3B:
possible

7B:
possible with tricks

Example:

Model        Feasible
--------------------------------
GPT-2 124M   🚀
GPT-2 355M   🚀
GPT-2 774M   🚀
GPT-2 1.5B   ✅
Llama 3B     ✅
Llama 7B     ⚠️

7. Embedding / RAG workloads

Overkill.

Can run:

BGE-large
E5-large
Qwen embedding models

with huge batch sizes.

8. Agent server

Actually a very good use case.

Example stack:

                    AMD GPU
                       |
                vLLM ROCm
                       |
             OpenAI-compatible API
                       |
       +---------------+--------------+
       |                              |
    OpenClaw                       Hermes
       |
    Tools / Browser / Code

You can host:

local coding agent
RAG system
multimodal assistant
image generation API

ROCm compatibility notes

The main limitation is not VRAM, it is software compatibility.

Check:

rocminfo | grep gfx

You should see:

gfx1100

For PyTorch:

python - <<'PY'
import torch
print(torch.cuda.is_available())
print(torch.cuda.get_device_name())
PY

Expected:

True
AMD Radeon ...

My ranking for this machine

Given your AI lab workflow:

Workload	Rating
Qwen3 / DeepSeek inference	⭐⭐⭐⭐⭐
vLLM server	⭐⭐⭐⭐⭐
LoRA fine tuning	⭐⭐⭐⭐⭐
Stable Diffusion / FLUX	⭐⭐⭐⭐⭐
Video generation	⭐⭐⭐⭐
GPT-style training	⭐⭐⭐⭐
70B inference	⭐⭐⭐⭐ (quant only)
Large-scale pretraining	⭐⭐

This is a very nice AMD playground. The sweet spot is probably:

Qwen3-32B reasoning server
FLUX image generation
LoRA training experiments
agent backend with OpenAI-compatible API

For a hackathon, this single GPU is enough to build something that looks like a small AI cloud.

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