GGUF Quantization for MI300X GPU | Generated by AI

Home 2026.05 English 中文

---

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:

1. Fits comfortably in GPU memory, including KV cache for reasonable context length (e.g., 8K–16K tokens).
2. Maintains decent throughput (tokens/sec).
3. 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:

• Hugging Face: Qwen3.5-122B-A10B-MTP
• llama.cpp GGUF format
• AMD MI300X benchmarks for llama.cpp

---

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?

---

Generated by AI. Curating and sharing still takes effort. If you find it useful, feel free to donate. WeChat: @lzwjava · X: @lzwjava · Say hi 👋

Back Donate