AMD Radeon RX 6800
LLM Inference Performance
| Model | Tokens / sec | Local Fit |
|---|---|---|
| Mistral 7b Q4 | 16 tok/s | fits · single GPU |
| Llama 3 8b Q4 | 15 tok/s | fits · single GPU |
| Llama 3 13b Q4 | 8 tok/s | fits · single GPU |
| Llama 3 70b Q4 | — OOM — | OOM / offload |
Local Model Compatibility
Spec Sheet
Analysis notes
Quick Summary
AMD Radeon RX 6800 is a 16GB AMD card for local AI workloads. It uses RDNA 2, draws about 250W, and can run many 13B quantized models locally. For AI buyers, the main questions are VRAM ceiling, ROCm support, memory bandwidth, and used-market price.
Specs That Matter for AI
The 16GB VRAM pool sets the practical model-size limit. Sixteen gigabytes or more gives room for 7B models, many 13B quantized models, and heavier image-generation workflows. Memory bandwidth is listed at roughly 512 GB/s, which helps token generation when the whole model fits on card.
AI Workload Fit
ROCm is the platform note to verify first. ROCm support can be strong on Linux, but app support and version matching need more care than CUDA. The card does not have enough VRAM for comfortable 70B 4-bit inference.
Verdict
AMD Radeon RX 6800 starts as a 3.2/5 AI-value candidate in this seed catalog. That rating should be refined after Playwright harvest pulls rendered review pages, benchmark tables, and firsthand reports into the evidence corpus.
Frequently Asked Questions
- Can the AMD Radeon RX 6800 run local LLMs?
- Yes. With 16GB of VRAM, it can run 7B quantized models locally and many 13B quantized models with practical settings.
- Is the AMD Radeon RX 6800 good for AI inference?
- It can work well with ROCm-supported stacks, especially on Linux, but compatibility should be checked per tool.