Gemma 2 9B

A compact instruction model that targets high quality within consumer GPU memory limits.

9B parameters

ArchitectureDense transformer · sliding attention

Native context8K tokens

LicenseGemma

Q4 · 8K estimate8.8 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

8.8 GiB

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

Quality-first

21.2 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

10 GiBper GPU × 1

Includes 15% operating headroom for the selected runtime profile.

Estimated allocation per GPU8.8 GiB each

Weights 4.9 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.