Can I Run / gemma 4 E4B it / on AMD Instinct MI250X
Can I Run gemma 4 E4B it on a AMD Instinct MI250X?
Yes
Runs at full precision (f32). Zero quality loss.
Model size
8.0B
GPU memory
128GB
Smallest quant
Q3_K_S
Best fit
f32
12 quantizations fit your 128GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| f32BEST | 33.0 GB | 34.5 GB | 1.9 GB | +95.0 GB |
| fp16 | 17.0 GB | 18.5 GB | 1.0 GB | +111.0 GB |
| Q8_0 | 9.5 GB | 11.0 GB | 8.2 GB | +118.5 GB |
| Q6_K | 7.6 GB | 9.1 GB | 7.1 GB | +120.4 GB |
| Q5_K_M | 6.7 GB | 8.2 GB | 5.5 GB | +121.3 GB |
| Q5_K_S | 6.5 GB | 8.0 GB | 5.4 GB | +121.5 GB |
| Q4_1 | 6.0 GB | 7.5 GB | 5.1 GB | +122.0 GB |
| Q4_K_M | 5.8 GB | 7.3 GB | 5.0 GB | +122.2 GB |
| Q4_K_S | 5.6 GB | 7.1 GB | 4.8 GB | +122.4 GB |
| Q4_0 | 5.5 GB | 7.0 GB | 4.8 GB | +122.5 GB |
| Q3_K_M | 4.3 GB | 5.8 GB | 4.1 GB | +123.7 GB |
| Q3_K_S | 4.1 GB | 5.6 GB | 3.9 GB | +123.9 GB |
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Full model details
gemma 4 E4B it →
All quant variants, benchmark scores, and use-case tags.
Best models for this GPU
AMD Instinct MI250X →
Top-ranked open-source models that fit in 128GB.
FAQ
Can the AMD Instinct MI250X run gemma 4 E4B it?
Yes. The AMD Instinct MI250X's 128GB of VRAM is enough to run gemma 4 E4B it at f32 quantization (33.0GB required).
What's the best quantization to use?
f32 is the highest-precision quantization that fits in your 128GB. It uses about 33.0GB of memory and 34.5GB recommended for comfortable inference.
What if I need more headroom for context length?
KV cache memory grows with context length. The numbers above assume a baseline 2K-4K context. For long-context use (32K+), add another 2-6GB depending on the model architecture.