Qwen2.5 32B Instruct

A strong mid-large model that distinguishes 24 GB, 48 GB, and 80 GB deployment options.

32.5B parameters

ArchitectureDense transformer · GQA

Native context32K tokens

LicenseApache 2.0

Q4 · 8K estimate21.6 GiB

Memory envelope

What changes the fit.

Weights establish the floor. Context, concurrent sequences, cache precision, and runtime overhead determine whether the deployment remains stable.

Efficient

21.6 GiB

Useful starting point for local evaluation and cost-sensitive inference.

Quality-first

70.6 GiB

Preserves original weight precision and establishes a conservative serving baseline.

Interactive plan

The worksheet starts with this model selected. Adjust context, precision, cache, and GPU count for your workload.

Deployment worksheetMODEL / GPU FIT

Calculated estimate

Recommended VRAM

25 GiBper GPU × 1

Includes 15% operating headroom for the selected runtime profile.

Estimated allocation per GPU22 GiB each

Weights 18 GiBKV cache 2.0 GiBRuntime 2.0 GiB

Sample planning rates only—not live Flatkey quotes or availability.

Command template · vLLM

vllm serve YOUR_COMPATIBLE_QUANTIZED_MODEL_ID --tensor-parallel-size 1 --max-model-len 8192 --quantization YOUR_MATCHING_BACKEND

4-bit is a memory assumption, not a checkpoint choice. Replace both placeholders with a compatible quantized artifact and its vLLM backend.