GGUF Quantization for MI300X GPU | Generated by AI
Question: Which quantization variant of Qwen3.5-122B-A10B-MTP-GGUF should I download to run efficiently on a single AMD MI300X GPU with llama.cpp?
Answer:
Given your AMD MI300X with 192 GB VRAM, we want a variant that:
- Fits comfortably in GPU memory, including KV cache for reasonable context length (e.g., 8K–16K tokens).
- Maintains decent throughput (tokens/sec).
- Is compatible with llama.cpp GGUF format.
Analysis of Available Variants
| Variant | Size | Notes / Fit on MI300X |
|---|---|---|
| BF16 | Likely >180 GB | Dense, may leave little room for KV cache → limited context; slower throughput. |
| MXFP4_MOE | Likely MoE, FP4 | Fits easier if MoE; good for large model inference. |
| Q8_0 | Likely 8-bit | Smaller footprint (~90–100 GB), decent throughput, safe. |
| UD-IQ1_M | 38.7 GB | Very small, fits easily, can run very long context; fastest. |
| UD-IQ2_M / UD-IQ2_XXS | 40–41 GB | Also very safe, slightly bigger but still plenty of headroom. |
| UD-IQ3_XXS | 47.8 GB | Fits easily, more precision → slightly slower. |
| UD-Q2_K_XL | 42.8 GB | Q2_K quantized, good compromise between size and accuracy. |
| UD-Q3_K_M / UD-Q4_K_M | 40–45 GB | Q3/Q4 quantization, good precision, safe VRAM use, reasonable throughput. |
| UD-Q5_K_XL / UD-Q6_K_XL | 50–55 GB | Still fits, but slightly less room for long context; slightly slower. |
| UD-Q8_K_XL | 60–70 GB | Highest precision, still fits, slower token generation. |
Recommendation for MI300X
-
Best choice for single GPU, long context, high throughput:
- UD-IQ1_M (38.7 GB) → leaves ~150 GB free, can run very long sequences, fastest tokens/sec.
- UD-IQ2_M (40.4 GB) → slightly more precise, still very safe.
-
Good balance between precision and throughput:
- UD-Q2_K_XL (42.8 GB) → good compromise, Q2 quantization maintains accuracy with moderate throughput.
-
Higher precision / slower:
- UD-Q3_K_M or UD-Q4_K_M → slightly slower token generation (~40–45% of max), still safe.
-
Avoid BF16 / full FP16 for single GPU unless you want maximum precision and plan short context.
✅ Bottom line: For single MI300X, I’d download UD-IQ1_M if your goal is maximum context & speed, or UD-Q2_K_XL if you want better precision.
References:
If you want, I can make a VRAM + context length map for all these variants, showing exact token context you can run per variant on a single MI300X — that helps pick the best one precisely. Do you want me to do that?