Best AI Models for AMD Instinct MI250X (128.0GB)
With 128 GB of memory, this is a high-end configuration for local AI. You can comfortably run most open-source LLMs including large 70B parameter models at good quantization levels, making it one of the best setups for serious local AI work.
At this memory tier, nearly every popular open-source model is within reach. You can run Llama 3 70B at Q4_K_M or even Q5_K_M quantization with room to spare, handle coding assistants like DeepSeek Coder 33B at high quality, and easily run any 7B–30B model at full or near-full precision. Context windows remain generous even with larger models, so multi-turn conversations and long-document processing work smoothly.
Runs Well
- 70B models (Llama 3 70B, Qwen 72B) at Q4–Q5
- 30B models at Q6–Q8 quality
- 7B–14B models at full FP16 precision
- Vision models (LLaVA, CogVLM) without compromise
Challenging
- Mixture-of-experts models like Mixtral 8x22B at higher quants
- 120B+ models still require lower quantizations
What LLMs Can AMD Instinct MI250X Run?
Showing compatibility for AMD Instinct MI250X
| Model | Quant | VRAM | Speed | Context | Status | Grade |
|---|---|---|---|---|---|---|
| Q4_K_M | 2.9 GB2% | 632.4 t/s | 131K | EASY RUN | D26 |
AMD Instinct MI250X Specifications
- Brand
- AMD
- Architecture
- CDNA 2
- VRAM
- 128.0 GB HBM2e
- Memory Bandwidth
- 3276.8 GB/s
- Stream Processors
- 14,080
- FP16 Performance
- 383.00 TFLOPS
- TDP
- 560W
- Release Date
- 2021-11-08
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Frequently Asked Questions
- Can AMD Instinct MI250X run Llama 3 8B?
Yes, the AMD Instinct MI250X with 128 GB can run Llama 3 8B at Q4_K_M quantization with good performance. At this VRAM level, you can expect smooth token generation and responsive inference for chat and coding tasks.
- Is AMD Instinct MI250X good for AI?
The AMD Instinct MI250X has 128 GB of HBM2e, making it excellent for running local LLM models. You can run most popular 7B-30B models at good quality.
- How many parameters can AMD Instinct MI250X handle?
With 128 GB, the AMD Instinct MI250X can handle models up to approximately 30-70B parameters depending on quantization. Using Q4_K_M quantization (the typical sweet spot), you can fit roughly 213B parameters.
- What quantization should I use on AMD Instinct MI250X?
For the best balance of quality and speed on 128 GB, Q4_K_M is the recommended starting point. If you have headroom, try Q5_K_M for better quality. For larger models that barely fit, Q3_K_M or Q2_K can squeeze them in at the cost of some output quality.
- How fast is AMD Instinct MI250X for AI inference?
Speed depends on the model size and quantization. With 3276.8 GB/s memory bandwidth, the AMD Instinct MI250X can typically achieve 30-50+ tokens per second on 7B models at Q4_K_M quantization, which is comfortable for interactive chat.