Qwen3.8-27B-RAM-13GB-GGUF

Qwen3.8-27B — 13GB (GGUF)

GGUF build of Qwen/Qwen3.8-27B, capability-validated by baa.ai for faithful, retrieval-grounded reading — a full 27B reader that runs on a single 16 GB GPU (e.g. AWS g4dn.xlarge / NVIDIA T4) with a 32K-token context window at full-precision KV cache (64K and 128K with quantized KV).

An Apple Silicon build of the same allocation is at baa-ai/Qwen3.8-27B-RAM-13GB-MLX (13.0 GB, vision tower retained).

Built for the case where the retrieved documents are the source of truth and the model's job is to interpret them — not to answer from its own memory. It was selected and validated to keep that behaviour intact under aggressive compression.

v2 (2026-09-27). This release replaces the v1 IQ3_M build with a RAM per-tensor mixed-precision allocation at the same 12.6 GB: per-tensor GGUF types (Q2_K to Q8_0) with importance-matrix calibration. WikiText-2 perplexity drops from 6.34 to 6.02 (−5.0%, better on 37 of 40 paired text chunks) with no change in size, and the build passes the full five-certificate bundle below. The v1 file remains available in the repository history.

⚠️ Requires a recent llama.cpp (build b10360 / Aug 2026 or newer). Qwen3.8 is a hybrid Gated Delta Net (linear-attention) + full-attention architecture (arch: qwen35). Stable Ollama and LM Studio releases may lag; use an up-to-date llama.cpp until downstream runtimes catch up.

Files

Metrics

Validated to lose nothing on faithfulness or reading versus the full-precision model: when a retrieved passage contradicts the model's own prior, it follows the passage 100% of the time, and reads the correct answer from context 100% of the time (30-item benches each). Retrieval of a planted fact at three depths and two-hop chaining across a 30K-token document were perfect in every trial.

Context window on a 16 GB GPU

Measured with llama-server on an NVIDIA Quadro P5000 (16,384 MiB, Pascal) with all layers offloaded (-ngl 99 -fa on). VRAM is nvidia-smi memory.used after a 2K-token warm-up, measured with the v1 file of identical size; throughput at 8K, 32K and 64K is the median over the long-context retrieval runs at that length, and at 128K the 2K-token warm-up. The KV cache of this architecture is small (16 of 64 layers use full attention), so long contexts fit:

128K at f16 or q8_0 KV does not fit. Retrieval quality (needle + two-hop) stayed perfect at 32K with f16 or q4_0 KV and at 64K with q8_0 KV.

Usage

brew install llama.cpp huggingface-cli

hf download baa-ai/Qwen3.8-27B-RAM-13GB-GGUF --include "*.gguf" --local-dir ./qwen3.8-27b-ram-13gb

llama-cli -m ./qwen3.8-27b-ram-13gb/Qwen3.8-27B-RAM-13GB.gguf -p "Hello!" -n 256 -ngl 99

Or via llama-server for an OpenAI-compatible HTTP API (drop-in for a retrieval-augmented reader), with the 32K context that fits a 16 GB card at full-precision KV:

llama-server -m ./qwen3.8-27b-ram-13gb/Qwen3.8-27B-RAM-13GB.gguf --port 8080 -ngl 99 --ctx-size 32768 -fa on

For 64K add -ctk q8_0 -ctv q8_0; for 128K, -ctk q4_0 -ctv q4_0. Also works with llama-cpp-python; Ollama and LM Studio need a build that supports qwen35.

Version history

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Compressed and capability-validated by baa.ai

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