---
title: "AMD R9700 AI Pro 32GB Local LLM Benchmark — A Real-World Comparison Against the RTX 4060"
date: 2026-09-23
time: "22:30"
model: admin
category: knowhow
summary: "A local LLM benchmark comparison between the 32GB VRAM AMD R9700 AI Pro and the 8GB RTX 4060. It analyzes, with measured data, the Vulkan vs ROCm performance gap, whether a 27B model is practical, and the bottleneck of running two cards."
tags: AMD,R9700,AI Pro,local-LLM,VRAM,RTX4060,benchmark,local-AI
---

# AMD R9700 AI Pro 32GB — A Local LLM Fight Against the RTX 4060

## Why This Combination

In an era where "AI = NVIDIA" is the accepted formula, is there any reason to deliberately use an AMD graphics card for local LLMs? I ran a direct benchmark against an RTX 4060 (8GB) to see whether the generous 32GB of VRAM and the reasonable price could be a realistic alternative.

## Test Environment

| Item | Spec |
|------|------|
| OS | Pop!_OS (based on Ubuntu 22.04) |
| CPU | Intel Core i5 |
| RAM | 64GB DDR5 |
| Power | 750W |
| GPU 1 | NVIDIA RTX 4060 (8GB VRAM) |
| GPU 2 | AMD R9700 AI Pro (32GB VRAM, ASRock) |
| Backend | llama.cpp (Vulkan / ROCm) |
| Prompt | "Write a 5,000-character story about a dog and a frog who meet a dragon and become statisticians" |

## Benchmark Results

### Mistral Nemo Instruct 12B (Q4_K_M)

| Item | RTX 4060 (8GB) | R9700 AI Pro (32GB) | Improvement |
|------|----------------|---------------------|-----------|
| Prompt processing | 607 tok/s | 1,000+ tok/s | **1.6x** |
| Token generation | 15 tok/s | 64 tok/s | **4.2x** |

A 12B model does run, at least, on the RTX 4060. But the R9700 AI Pro is more than 4x faster. On 8GB VRAM the KV cache was tight, capping it at 15 tok/s; on 32GB it comfortably produces 64 tok/s.

### Qwen 3.6 / 3.8 27B (Q6)

| Item | RTX 4060 (8GB) | R9700 AI Pro (32GB) | Improvement |
|------|----------------|---------------------|-----------|
| Prompt processing | 73 tok/s | 638 tok/s | **8.7x** |
| Token generation | 4.5 tok/s | 26 tok/s | **5.8x** |

**Key indicator**: On the RTX 4060, a 27B model runs at 4.5 tok/s, effectively unusable. CPU offloading kicks in and the perceived speed is so slow that conversation is impossible. The R9700 AI Pro, by contrast, does 26 tok/s for stress-free conversation.

The criterion for whether a 25B-35B class model is "actually usable locally" is **20 tok/s**. The R9700 AI Pro meets that bar; the RTX 4060 does not even reach half of it.

## Vulkan vs ROCm — Running AI on AMD

When running a local LLM on an AMD GPU, you normally use ROCm. But a surprising result came out.

### LFM 2.6B (Q8) Test

| Backend | Token generation speed | Note |
|--------|---------------|------|
| **Vulkan** | **133 tok/s** | Overwhelming |
| ROCm | 26.7 tok/s | **5x slower** |

With ROCm, the speed dropped to a fifth or less. In a llama.cpp-based environment, the result is that **the Vulkan backend is far more efficient**. ROCm still looks under-optimized, and the gap can be large depending on the environment.

**Practical advice**: When running a local LLM on the R9700 AI Pro, always test Vulkan first. A 5x performance gap over ROCm cannot be ignored.

## The Trap of Two Cards — A Mixed RTX 4060 + R9700 AI Pro Setup

Thinking "8GB + 32GB = 40GB, isn't that the strongest?", I installed both cards at once.

**Result: it was 50% slower than running the R9700 alone.**

| Setup | 12B model token generation |
|------|-------------------|
| R9700 AI Pro alone | **64 tok/s** |
| RTX 4060 + R9700 mixed | ~32 tok/s |

**Cause**: The RTX 4060's low bandwidth and VRAM become the bottleneck and drag down the fast R9700's performance. Mixing GPUs from different manufacturers also causes compatibility problems between CUDA/ROCm/Vulkan.

**Conclusion**: Mixing AMD and NVIDIA is pointless. Running the R9700 AI Pro alone is the answer.

## Heat, Power, Noise

| Item | Measurement |
|------|--------|
| Power at full load | ~300W |
| Peak temperature | 80°C (ASRock cooling) |
| Noise | Much quieter than an RTX 3090 at full load |

A 750W power supply is sufficiently stable. Even used on a desktop at a 45cm distance, the noise is not unpleasant.

## Who Should Buy It

### Recommended For

- **Privacy-conscious users**: those who do not want to send data to external cloud AI
- **Large-model users**: those who want to run 27B-35B models comfortably locally
- **Multi-model concurrent use**: simultaneous loading that uses the 32GB VRAM, such as LLM plus speech recognition (Whisper)
- **Linux environments**: Linux users with good Vulkan support

### Not Recommended For

- **Users of only small models up to 12B**: a used RTX 3060 12GB is 3x cheaper
- **Windows environments**: Vulkan/ROCm driver compatibility is unstable
- **Value-for-money first**: a used RTX 3090 24GB (600,000-700,000 KRW) is better value at 24GB

## Final Assessment

| Item | AMD R9700 AI Pro | NVIDIA RTX 4060 |
|------|-----------------|-----------------|
| VRAM | **32GB** | 8GB |
| 12B model TPS | **64 tok/s** | 15 tok/s |
| 27B model TPS | **26 tok/s** | 4.5 tok/s (unusable) |
| Price | ~1.3-2.2M KRW | ~370K KRW |
| Local AI recommendation | **Strongly recommended** for 25B+ models | For small models up to 8B only |

32GB of VRAM lowers the barrier to running 27B-35B models locally without quantization. It is expensive, but the answer to the question "can you actually use a 27B model locally?" is clearly **"yes."**

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*These measurements were taken in a single operator environment (Pop!_OS, Intel i5, 64GB DDR5), and actual performance may vary with the hardware configuration.*