Local AI hardware guide

What machines can deploy Qwen locally?

Updated July 28, 2026 · HiRAM Deals

For the small stack, start with Qwen 3.5 9B on a 32 GB machine. Ollama’s current quantized package is about 6.6 GB and advertises a 256K context window, leaving the rest of system memory for the context cache, runtime, and operating system.

The short answer: Qwen 3.5 9B plus 32 GB RAM is our recommended compact local stack. It gives a 100K-token experiment substantially more headroom than a 16 GB machine. Choose 64 GB if long context is central to your workflow rather than an occasional test.

The recommended small stack: Qwen 3.5 9B

Qwen 3.5 9B is a multimodal model that accepts text and images. Ollama’s default 9B package is about 6.6 GB with a 256K advertised context window. That combination makes it a much more realistic fit for a 32 GB desktop than the 27B-and-larger class.

32 GB RAM

Recommended for Qwen 3.5 9B. It leaves meaningful capacity beyond the 6.6 GB quantized weights for a large KV cache and normal system overhead.

64 GB RAM

The safer long-context workstation. Choose this when you expect 100K-token prompts frequently, keep development tools open, or want larger quantizations.

96–128 GB RAM

Useful for 27B-class models, multiple concurrent models, CPU-first deployments, or avoiding aggressive memory compromises.

Can 32 GB handle a 100K-token context?

Qwen 3.5 9B is the specific case where 32 GB is a reasonable recommendation for trying it. The model package is relatively small and advertises up to 256K context in Ollama. Still, context support is not the same as guaranteed memory fit: cache format, image inputs, runtime version, thinking output, and concurrent applications change the footprint.

Use 32 GB as the value-oriented target for a single-user 100K-token experiment. Use 64 GB when you need that context length reliably, want fewer tuning compromises, or plan to run other memory-heavy software at the same time.

Install with Ollama

Install Ollama, then run the 9B model explicitly:

ollama run qwen3.5:9b

Review the exact download size and context limit before selecting a tag in the Qwen 3.5 library on Ollama.

Hardware priorities

RAM capacity: 32 GB recommended for the Qwen 3.5 9B small stack; 64 GB for dependable long-context work.
GPU VRAM: more layers on GPU generally means faster responses.
Memory bandwidth: particularly important when weights spill into system RAM.
Cooling and power: long generations can sustain CPU or GPU load.
Upgrade paths: extra DIMM slots make inexpensive workstation platforms attractive.