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?
127 models · 8 good
Showing compatibility for AMD Instinct MI250X
| Model | Quant | VRAM | Speed | Context | Status | Grade |
|---|---|---|---|---|---|---|
Q4_K_M·21.2 t/s tok/s·66K ctx·GOOD FIT | Q4_K_M | 85.1 GB | 21.2 t/s | 66K | GOOD FIT | A84 |
Q4_K_M·21.2 t/s tok/s·66K ctx·GOOD FIT | Q4_K_M | 85.1 GB | 21.2 t/s | 66K | GOOD FIT | A84 |
Q4_K_M·24.8 t/s tok/s·131K ctx·GOOD FIT | Q4_K_M | 72.7 GB | 24.8 t/s | 131K | GOOD FIT | A72 |
Q4_K_M·24.1 t/s tok/s·262K ctx·GOOD FIT | Q4_K_M | 74.7 GB | 24.1 t/s | 262K | GOOD FIT | A74 |
Q4_K_M·25.1 t/s tok/s·GOOD FIT | Q4_K_M | 71.7 GB | 25.1 t/s | — | GOOD FIT | A71 |
Q4_K_M·27.0 t/s tok/s·131K ctx·GOOD FIT | Q4_K_M | 66.7 GB | 27.0 t/s | 131K | GOOD FIT | A67 |
Q4_K_M·24.6 t/s tok/s·GOOD FIT | Q4_K_M | 73.3 GB | 24.6 t/s | — | GOOD FIT | A72 |
Q4_K_M·27.7 t/s tok/s·131K ctx·GOOD FIT | Q4_K_M | 65.1 GB | 27.7 t/s | 131K | GOOD FIT | A66 |
Q4_K_M·38.7 t/s tok/s·131K ctx·FAIR FIT | Q4_K_M | 46.6 GB | 38.7 t/s | 131K | FAIR FIT | B51 |
Q4_K_M·38.7 t/s tok/s·131K ctx·FAIR FIT | Q4_K_M | 46.6 GB | 38.7 t/s | 131K | FAIR FIT | B51 |
Q4_K_M·36.6 t/s tok/s·262K ctx·FAIR FIT | Q4_K_M | 49.2 GB | 36.6 t/s | 262K | FAIR FIT | B53 |
Q4_K_M·40.4 t/s tok/s·33K ctx·FAIR FIT | Q4_K_M | 44.6 GB | 40.4 t/s | 33K | FAIR FIT | B50 |
Q4_K_M·36.6 t/s tok/s·262K ctx·FAIR FIT | Q4_K_M | 49.2 GB | 36.6 t/s | 262K | FAIR FIT | B53 |
Q4_K_M·40.4 t/s tok/s·131K ctx·FAIR FIT | Q4_K_M | 44.6 GB | 40.4 t/s | 131K | FAIR FIT | B50 |
Q4_K_M·40.4 t/s tok/s·33K ctx·FAIR FIT | Q4_K_M | 44.6 GB | 40.4 t/s | 33K | FAIR FIT | B50 |
Q4_K_M·36.8 t/s tok/s·33K ctx·FAIR FIT | Q4_K_M | 49.0 GB | 36.8 t/s | 33K | FAIR FIT | B53 |
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
Get Started
GPUs to Consider Over AMD Instinct MI250X
Similar GPUs and upgrades with more VRAM or higher bandwidth for AI
NVIDIA B200
NVIDIA · Blackwell
AMD Instinct MI325X
AMD · CDNA 3
AMD Instinct MI300X
AMD · CDNA 3
NVIDIA GH200 Grace Hopper Superchip
NVIDIA · Hopper (Grace Hopper)
NVIDIA H200 NVL
NVIDIA · Hopper
NVIDIA H200 SXM
NVIDIA · Hopper
Frequently Asked Questions
- Can AMD Instinct MI250X run WizardLM 2 8x22B?
Yes, the AMD Instinct MI250X with 128 GB can run WizardLM 2 8x22B, Mixtral 8x22B v0.1, GPT OSS 120B, and 1420 other models. 3 models run at excellent quality, and 48 at good quality. Check the compatibility table above for the full list with VRAM usage and estimated speed.
- Is AMD Instinct MI250X good for AI?
The AMD Instinct MI250X has 128 GB of HBM2e, making it excellent for running local AI models. It supports 51 models at good quality or better. With 3276.8 GB/s memory bandwidth, it delivers fast token generation speeds. This is an enthusiast-grade GPU that handles most popular open-source LLMs.
- How many parameters can AMD Instinct MI250X handle?
With 128 GB, the AMD Instinct MI250X supports models from 3B to 70B+ parameters depending on quantization level. At Q4_K_M (the recommended sweet spot), you can fit roughly 213B parameters. This means 7B models at high quality (Q6/Q8) or 30B+ models at Q4.
- What quantization should I use on AMD Instinct MI250X?
For the best balance of quality and speed on the AMD Instinct MI250X, 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 Instinct MI250X for AI inference?
With 3276.8 GB/s memory bandwidth, the AMD Instinct MI250X achieves approximately 401 tokens/sec on a 7B model at Q4_K_M — that's very fast, well above conversational speed. A 14B model runs at ~200 tok/s. Token generation speed scales inversely with model size — smaller models are significantly faster.
tok/s = (3276.8 GB/s ÷ model GB) × efficiency
Smaller models = faster inference. Memory bandwidth is the main bottleneck for token generation speed.
Estimated speed on AMD Instinct MI250X
~21 tok/s~21 tok/s~25 tok/s~24 tok/sReal-world results typically within ±20%. Speed depends on quantization kernel, batch size, and software stack.
- What's the best model for AMD Instinct MI250X?
The top-rated models for the AMD Instinct MI250X are WizardLM 2 8x22B, Mixtral 8x22B v0.1, GPT OSS 120B. 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 Instinct MI250X need?
The AMD Instinct MI250X has a TDP of 560 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 1200 W PSU or larger. At this power level, a high-airflow case matters: aim for at least two front intake fans and one rear exhaust, with tidy cabling so hot air isn't trapped around the card. LLM inference sustains full GPU load continuously — longer and more consistently than most gaming workloads — so also make sure your CPU cooler can keep up under combined load.