Qwen3.8-27B on 16GB VRAM: Optimized Inference Recipe
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Qwen3.8-27B on 16GB VRAM: Optimized Inference Recipe A detailed technical recipe for running a 27B model with 100K context on a 16GB consumer GPU, covering selective quantization, KV cache compression, and speculative decoding.
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Model: Qwen3.8-27B
GPU: RTX 4070 Ti SUPER 16GB
Context: 100K tokens
Decode speed: 47-50 tok/s
VRAM usage: 15.93GB
Weights (13.5GB hybrid GGUF):
- Attention: IQ4_XS
- FFNs: IQ3_S
KV cache (KVarN compressed):
- K: kvarn5
- V: kvarn4
- Most recent 1,024 KV tokens at higher precision
Speculative decoding: MTP-2
Key change: kvarn5/kvarn5 -> kvarn5/kvarn4 freed ~6% VRAM, increased usable context from ~88K to 100K tokens.
Implementation notes:
- Uses BeeLlama.cpp (llama.cpp fork), not stock llama.cpp or standard LM Studio
- Community-reported performance; actual results vary with hardware, prompt length, workload, and configuration
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