Benchmarking Qwen3.8 27B quantizations: 4-bit holds up, 1-bit collapses

7.1 relevance

Practical benchmarking of LLM quantizations relevant to deploying models on limited hardware.

General quesma.com

Answer the board on AI readiness

Arrive with evidence, not opinion. See how your foundation benchmarks against your peers, get a clear read, and leave with a ranked place to start.

Why It Matters

For platform engineers running local LLMs for agentic workflows, this quantifies the exact GPU RAM vs. quality tradeoff—4-bit is safe for production agentic coding, but 1-bit is unusable, and reasoning effort tuning matters more than quantization below 4-bit.

Key Takeaways

Use Q4_K_M (17 GB) quantization for Qwen3.8 27B on consumer GPUs; avoid sub-2-bit quantizations for any reasoning task, and always benchmark with xhigh reasoning effort.

Benchmarking Qwen3.8 27B quantizations: 4-bit holds up, 1-bit collapses

Summary

Benchmarking Qwen3.8 27B GGUF quantizations shows the 4-bit Q4_K_M (17 GB) matches the full BF16 model on Terminal-Bench 2.1 and GPQA Diamond, fitting on a 24 GB RTX 4090 with room for 64k tokens. The 1-bit UD-IQ1_S (6.2 GB) collapses to near-random performance on GPQA Diamond, and higher reasoning effort (xhigh) significantly impacts scores more than quantization level down to 2-bit. Results confirm that 4-bit quantization is lossless for agentic coding and reasoning tasks, while extreme compression destroys capability.

Author

Piotr Migdał