Qwen3.5 0.8B
0.8BAlibaba Qwen3.5
The smallest Qwen3.5, for phones, single-board computers and as a fast draft or routing model next to a bigger one. At Q4_K_M it needs about 0.6 GB at 4K context and under 1 GB at 32K. Its full 256K window needs about 3.8 GB, almost all of it cache, so very long contexts cost more than the weights here. Sizes use the calculator's generic rates, since no GGUF file size could be checked, and no per-level quality loss has been published.
262K
Max Context
2
Quant Variants
GGUF Q8_0
Best Quality
—
Accuracy Retained
Quantization Variants
Per-quant VRAM, quality loss, and inference speed on RTX 4090
Measured = run on this site · Estimated = not run here: calculated, or a figure this site has not verified · Community = public reports
Similar models
Compare with Qwen3.5 2BQwen3.5 2B
Alibaba Qwen3.5
The 2B size of Qwen3.5, small enough for almost anything: old cards, base-model Macs, mini PCs and CPU-only servers. Only 6 of its 24 layers keep a growing KV cache, with 2 KV heads each. At Q4_K_M it needs about 1.5 GB at 4K context, 1.9 GB at 32K and 4.8 GB at its full 256K window. Even Q8_0 stays around 2.2 GB at 4K. Reasoning is off by default on the small Qwen3.5 sizes. Sizes come from the published GGUF file sizes, and no per-level quality loss has been published.
Qwen3.5 9B
Alibaba Qwen3.5
The newest small Qwen and a common pick for 8 to 16 GB cards. Only 8 of its 32 layers keep a growing KV cache, so it can use a long context on modest hardware. At Q4_K_M it needs about 6.6 GB at 4K and 7.5 GB at 32K, comfortable on an 8 GB card. Its full 256K window needs about 15.2 GB. Q8_0 needs about 10.4 GB, comfortable from 12 GB up. Reasoning is off by default on the small Qwen3.5 sizes, and it reads images through a separate vision encoder. Sizes come from the published GGUF file sizes, and no per-level quality loss has been published.
Qwen3.5 4B
Alibaba Qwen3.5
The 4B size of Qwen3.5, for 4 to 8 GB cards, base-model Macs and CPU-only machines. Only 8 of its 32 layers keep a growing KV cache. At Q4_K_M it needs about 3.3 GB at 4K context and 4.3 GB at 32K, so a 6 GB card is enough for long chats; its full 256K window needs about 12 GB. Reasoning is off by default on the small Qwen3.5 sizes, and it reads images through a separate vision encoder. Sizes come from the published GGUF file sizes, and no per-level quality loss has been published.
Llama 3.2 3B Instruct
Meta Llama 3.2
Tiny but capable. Runs on 4GB VRAM or 8GB RAM, even on phones via llama.cpp.
Running Qwen3.5 0.8B locally
At Q4_K_M and 4K of context, Qwen3.5 0.8B needs about 0.6 GB — 0.5 GB of weights, 0.05 GB of KV cache and a 0.1 GB activation buffer. The smallest cards in this index that clear that comfortably are 8 GB ones — 11 of them, from the Mac M1 8G up — and 78 of the 78 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 0.33 GB, taking the total to 0.9 GB. That is gentler than the parameter count suggests: only 6 of 24 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 4 GB.
Which build to download
This index only tracks Qwen3.5 0.8B in GGUF, across 2 levels (Q4_K_M, Q8_0). 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 — CUDA only, and now archived, so local use rather than serving.
Common questions
- How much VRAM does Qwen3.5 0.8B need?
- About 0.6 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 0.9 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 Qwen3.5 0.8B run on a 8GB GPU?
- Yes — at Q4_K_M and 4K context it needs about 0.6 GB, which leaves 5.4 GB spare on a Mac M1 8G, or on any of the other 8 GB cards here (11 in all). That is the smallest size in this index that clears it comfortably; 78 of 78 do.
- Which quantization of Qwen3.5 0.8B 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 2 levels in the index are Q4_K_M, Q8_0.
Where this model fits
Sized at Q4_K_M with a 4K context window, smallest card first. Comfortable means the estimate uses at most 88% of the memory.
- Runs comfortably on
- Mac M1 8G8GB · +5.4Mac M3 8G8GB · +5.4RTX 40608GB · +7.4RTX 4060 Ti 8G8GB · +7.4Radeon RX 9060 XT 8G8GB · +7.4
- Ships in
- GGUF
- Try it yourself
- Size it in the calculatorGenerate the run command
How to actually run this
Deployment guides for this model and this class of hardware.
This page's figures change when the model or the runtime does.Last updated 2026-10-11 RSS → /feed.xml