Qwen3.8 27B
27BAlibaba Qwen3.8
Hybrid attention: only 16 of its 64 layers keep a KV cache, which is why a 27B model holds a 262K context in about 16GB of cache rather than 64GB. Sizing it as a conventional stack overstates the cache fourfold — the estimates here count the 16 and match published measurements at 8K, 32K and 262K. Quality loss per level has not been published.
262K
Max Context
1
Quant Variants
GGUF Q4_K_M
Best Quality
—
Accuracy Retained
Quantization Variants
Per-quant VRAM, quality loss, and inference speed on RTX 4090
Measured = site benchmarks · Estimated = formula · Community = public reports
Similar models
Compare with Seed-OSS 36BSeed-OSS 36B Instruct
ByteDance Seed
Dense 36B with a native 512K context. Q4 weights are ~22GB, which lands on a 24GB card or a 2×16GB split — but the context is the real cost: 512K of KV cache is roughly 128GB on its own, so budget context first and weights second.
Qwen2.5 32B Instruct
Alibaba Qwen2.5
Near-GPT-4 reasoning on a 24GB VRAM card (Q4_K_S). Groundbreaking value.
DeepSeek-R1-Distill-Qwen-32B
DeepSeek
R1 distilled to 32B. Near-frontier reasoning on a single 24GB card (Q3/Q4).
Qwen3 32B Instruct
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Qwen3 dense 32B — successor to Qwen2.5-32B with stronger reasoning and thinking mode.
Running Qwen3.8 27B locally
At Q4_K_M and 4K of context, Qwen3.8 27B needs about 17.5 GB — 15.6 GB of weights, 0.25 GB of KV cache and a 1.6 GB activation buffer. The smallest card in this index that clears that comfortably is the Radeon RX 7900 XT at 20 GB, and 30 of the 61 cards here do. These are calculated figures, not measurements: the estimate stops counting a card as comfortable at 88% of its VRAM, which is roughly the room a desktop session needs.
What longer context costs
Going from 4K to 32K adds about 1.75 GB, taking the total to 19.4 GB. That is gentler than the parameter count suggests: only 16 of 64 layers keep a KV cache that grows with context, so the cache scales at a fraction of the usual rate. The model's native window is 256K; holding all of it at Q4_K_M would need about 35 GB.
Which build to download
This index only tracks Qwen3.8 27B in GGUF, across 1 level (Q4_K_M). No per-level perplexity sweep has been published for this model, so the quality column is empty rather than estimated — within one model, more bits per weight is the only ordering the data supports. No throughput has been measured for this model on this site's hardware. GGUF runs on llama.cpp and Ollama across NVIDIA, AMD and Apple silicon; AWQ and GPTQ target vLLM on CUDA and ROCm; EXL2 is ExLlamaV2 and CUDA only.
Common questions
- How much VRAM does Qwen3.8 27B need?
- About 17.5 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 19.4 GB at 32K. That figure is weights plus KV cache plus a 10% activation buffer, calculated from the model's architecture rather than measured on a card.
- Will Qwen3.8 27B run on a 20GB GPU?
- Yes — at Q4_K_M and 4K context it needs about 17.5 GB, which leaves 2.5 GB spare on a Radeon RX 7900 XT. That is the smallest card in this index that clears it comfortably; 30 of 61 do.
- Which quantization of Qwen3.8 27B should I use?
- No published quality comparison exists for this model, so pick by footprint: Q4_K_M is the level most people run, and a higher bits-per-weight level is more faithful. All 1 levels in the index are Q4_K_M.
How to actually run this
Deployment guides for this model and this class of hardware.