Build a Multi-LoRA Adapter Memory and Batch Sizing Calculator

Profile VRAM usage for serving concurrent LoRA adapters on open-weight base models using vLLM. Synthesize maximum concurrency limits and memory overheads using hosted Claude to generate deployment configuration.

ML Deployment Specialist

Parse evaluation logs measuring VRAM footprint scaling per active LoRA adapter on local hardware.

Calculate maximum adapter concurrency before triggering out-of-memory errors or significant KV-cache eviction.

Prompt hosted Claude API to synthesize a deployment density report justifying adapter count limits.

Export a deployment configuration specifying max_loras and max_lora_rank for the vLLM engine.

Web dashboard showing projected VRAM usage per adapter and configuration export options.

JSON arrays containing simplified synthetic VRAM profile measurements for LoRA adapters across various ranks. Starter examples below are synthetic. They are not downloaded records.

vLLM open-model serving References vLLM multi-LoRA serving features and configuration flags.

anthropic-claude, fastapi, pydantic. Synthesizes multi-LoRA deployment strategies based on memory profiles. Hosted Claude API for technical synthesis; benchmarks focus on open-weight models.

Submit runnable source, pinned dependencies, setup commands, a working interface, example outputs, and a recorded end-to-end demo. Include tests and an action log.

Correctly computes VRAM usage per adapter rank and base model overhead.

Identifies hard limits on concurrent requests when serving heterogeneous LoRAs.

Generates ready-to-run vLLM multi-LoRA launch arguments.

Confines profiling strictly to specified open-weight model architectures.

```json [ { "input": { "base_model": "open-qwen-7b", "base_vram_gb": 14.5, "adapter_rank": 64, "vram_per_adapter_gb": 0.45, "total_vram_gb": 24, "target_adapters": 10 }, "expected_output": { "total_projected_vram_gb": 19, "viable_deployment": true, "vllm_launch_flag": "--enable-lora --max-loras 10 --max-lora-rank 64", "claude_rationale": "Base model and 10 adapters fit well within 24GB VRAM, leaving 5GB for KV cache and context generation." } }, { "input": { "base_model": "open-qwen-7b", "base_vram_gb": 14.5, "adapter_rank": 128, "vram_per_adapter_gb": 0.9, "total_vram_gb": 24, "target_adapters": 20 }, "expected_output": { "total_projected_vram_gb": 32.5, "viable_deployment": false, "vllm_launch_flag": null, "claude_rationale": "Projected VRAM (32.5GB) exceeds hardware limits. Reduce target adapters." } } ] ```

Evaluation applies exclusively to open-weight models. No access or extrapolation to proprietary Claude models. Use mock external writes. Require human review before any real deployment.