Qwen infrastructure
Qwen Hardware Requirements: RAM and VRAM by Model and Quantization
Qwen hardware requirements depend on the exact model, precision or quantization, context length, inference runtime, batch size, and whether the workload runs entirely on GPU, unified memory, or with CPU offload.
Start with the model-weight floor
A useful first approximation is parameter count multiplied by the number of bits used per weight. This estimates only the model weights — not the complete runtime requirement.
| Example model size | 16-bit weight floor | 8-bit floor | 4-bit floor |
|---|---|---|---|
| 7B | ~14GB | ~7GB | ~3.5GB |
| 14B | ~28GB | ~14GB | ~7GB |
| 27B | ~54GB | ~27GB | ~13.5GB |
| 32B | ~64GB | ~32GB | ~16GB |
These are simple decimal parameter-count × bit-width estimates. Real quantized files and runtimes include additional data and overhead.
How much RAM do I need for a local LLM?
Start with the model-weight footprint, then add headroom for the inference runtime, context cache, temporary buffers, and the operating system. A model that appears to fit exactly into available memory can still fail once runtime overhead is included.
Which local LLM is best for 16GB of VRAM?
The practical answer depends on model size, quantization, runtime, and required context length. Smaller models provide more headroom, while larger models may fit only with aggressive quantization and reduced context. For a 16GB GPU, plan around the full runtime requirement rather than the model file size alone.
Can I run an LLM locally without a GPU?
Yes. CPU-only inference is possible with many local-model runtimes, particularly for smaller and quantized models. It is usually slower than GPU-accelerated inference, but can be practical for experimentation, background tasks, embeddings, and low-throughput workloads.
Why the real requirement is higher
- Runtime and framework memory.
- Attention and context caches.
- Batching and concurrency.
- Multimodal components where present.
- Operating-system and application memory.
- Temporary buffers and implementation-specific allocations.
Qwen3.5-27B as a current example
Qwen3.5-27B has 27 billion parameters and a long native context window. The full-precision model alone is large enough that quantization or higher-memory hardware becomes important for local deployment.
Long-context workloads should be measured with the actual runtime rather than inferred from a generic parameter-count formula because cache behavior depends on model architecture.
Sources
- Qwen3.5-27B model card— Model size and architecture reference