Qwen3.5 9B VRAM requirements: can you run it locally?

Qwen3.5 9B is the small dense model in Qwen's Qwen3.5 line (February 2026), with vision input. Hugging Face lists 9,653,104,368 parameters (the card rounds to 9B), the repo was created on 2026-02-27, and the license is Apache 2.0.^[1] The card gives 262,144 tokens natively, extensible to 1,010,000.^[2] Hugging Face counts about 6,407,750 downloads in the last 30 days (checked November 3, 2026).^[1]

Memory needed at each quantization

Quantization stores each weight in fewer bits. Smaller files fit on smaller GPUs, at some cost in quality, and the cost grows below about 4 bits. Below is every GGUF build we could source, using the exact file sizes on Hugging Face (1 GB = 1,000,000,000 bytes; split files added together), largest first.^[3] Qwen has no official GGUF repo for this size; sizes are from bartowski/Qwen_Qwen3.5-9B-GGUF. The vision projector (mmproj, 0.92 GB) is a separate file and is not counted.

Which quant should you pick? Q4_K_M is the usual balance of size and quality. Use Q5_K_M or Q6_K if you have the memory, and Q8_0 if you want results close to the original. Treat 2-bit and 3-bit builds as a last resort.

Context length adds memory (the KV cache)

On top of the weights, the engine keeps a key/value cache for every token. From config.json: 32 layers, but only every fourth one (8 layers) is full attention with a growing cache; those have 4 KV heads of size 256.^[2] The other 24 are linear-attention (Gated DeltaNet) layers with a small fixed state that doesn't grow with context, which we leave out. At FP16 that is 8 × 4 × 256 × 2 (K and V) × 2 bytes = 32,768 bytes per token.

Some engines can store the cache in 8-bit to roughly halve it.

Which GPUs and Macs it fits on

Pick your GPU or Mac and a context length to see which quantizations fit. The weights plus the KV cache are compared with the device's memory.^{[4][5][6][7][8][9][10][11]}

How to read it: "Fits" means weights + cache use under 90% of memory. "Tight" means 90–100%: it may load, but the engine's own working memory or a longer context can push it over. "No" means it won't fit on the GPU alone. The 90% line is our own rule of thumb, not a vendor figure. On a Mac, macOS also needs some of the unified memory, so treat Mac rows as optimistic.

Our recommendations

These follow from the file sizes and the 90% rule, at an 8K context.^[3]

How to run it

With llama.cpp, LM Studio or Ollama, download a GGUF file from the quant list above (for example the Q4_K_M build) from bartowski/Qwen_Qwen3.5-9B-GGUF.^[3] If the model is bigger than your VRAM, these tools can keep some layers in system RAM, but every token then waits on the slower memory, so expect much lower speed.

Cheapest GPU to rent for Qwen3.5 9B

On-demand prices listed on Runpod's pricing page for cards that hold Q4_K_M with an 8K context (checked November 2, 2026; Community Cloud is cheaper but runs on shared hosts):^[12]

See all GPU rental prices for Lambda, Vast.ai and multi-GPU setups, or compare it with other models in the VRAM table.^[13]

Frequently asked questions

How much VRAM does Qwen3.5 9B need at 4-bit?

About 6.4 GB for the Q4_K_M weights (6.2 GB) plus an 8K-token FP16 KV cache (0.27 GB), before engine overhead.^[3][2]

What is the smallest GPU that runs Qwen3.5 9B?

In our list, an 8 GB card (RTX 4060) holds Q4_K_M with an 8K context under our 90% rule. Smaller cards need a lower-bit quant from the table above.^[3]

How much does the full 256K context add?

At FP16 the KV cache for 262,144 tokens is about 8.59 GB (32K tokens: 1.07 GB).^[2]

How we calculated

• Weights are the exact GGUF file sizes on Hugging Face, checked November 3, 2026.^[3]
• KV cache is calculated from config.json. We assume an FP16 cache.^[2]
• Device memory is from NVIDIA and Apple spec pages. We compare in decimal GB, so the results lean conservative.
• Rental prices are the listed on-demand Runpod rates, checked November 2, 2026.^[12]
• Not included: engine working buffers, the vision/audio projector, the OS and display. These vary by engine and settings, so leave headroom.

Sources

All sources checked on 2026-11-03.

1. Hugging Face, Qwen/Qwen3.5-9B (parameter count, license, repo date, 30-day downloads; checked November 3, 2026). https://huggingface.co/Qwen/Qwen3.5-9B
2. Hugging Face, Qwen/Qwen3.5-9B config.json (layers, KV heads, head size, context). https://huggingface.co/Qwen/Qwen3.5-9B/blob/main/config.json
3. Hugging Face, bartowski/Qwen_Qwen3.5-9B-GGUF file list (exact GGUF sizes; checked November 3, 2026). https://huggingface.co/bartowski/Qwen_Qwen3.5-9B-GGUF/tree/main
4. NVIDIA, GeForce RTX 4060 family (8 GB / 16 GB) https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4060-4060ti/
5. NVIDIA, GeForce RTX 3060 family (12 GB) https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3060-3060ti/
6. NVIDIA, GeForce RTX 3090 specs (24 GB) https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3090-3090ti/
7. NVIDIA, GeForce RTX 4090 specs (24 GB) https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4090/
8. NVIDIA, GeForce RTX 5090 specs (32 GB) https://www.nvidia.com/en-us/geforce/graphics-cards/50-series/rtx-5090/
9. NVIDIA, RTX 6000 Ada Generation (48 GB) https://www.nvidia.com/en-us/products/workstations/rtx-6000/
10. NVIDIA, A100 (80 GB) https://www.nvidia.com/en-us/data-center/a100/
11. Apple, MacBook Pro tech specs (unified memory options) https://www.apple.com/macbook-pro/specs/
12. Runpod, GPU pricing (checked November 2, 2026). https://www.runpod.io/pricing
13. VRAMNeeded comparison table (sources listed per model). https://vramneeded.com/compare/