Core Analysis¶

Problem Focus: In inference, the bottleneck is often memory transfers, activation peaks, and compute throughput rather than raw parameter count. llmfit uses a memory-bandwidth plus runtime sampling model to better capture these costs, which matters for long contexts, low-precision quantization, and multi-GPU splitting.

Technical Advantages¶

• Bandwidth sensitivity: Accounting for memory transfer costs helps predict VRAM/system swap behavior and OOM risk when activations or weight streaming dominate.

• MoE & quantization awareness: Recognizes sparse activation patterns of MoE and different memory needs from dynamic quantization, avoiding overestimation from total-parameter heuristics.

• Verifiability: llmfit info exposes estimation assumptions and llmfit bench lets you measure tok/s/TTFT on your machine and replace estimates with measured data.

When It Can Still Be Off¶

• Incomplete model metadata: Missing activation or routing cost, quantization flags, or multi-GPU split hints will cause estimation errors.

• Backend/driver differences: Variations in quantization implementations, memory layout, or kernel optimizations across runtimes can make estimates inaccurate (especially with specialized accelerators).

• Complex NUMA / special hardware: Unmodeled NUMA effects or unsupported inference accelerators require manual validation.

Important Notice: The bandwidth model improves realism but is not a substitute for local benchmarking. Use llmfit bench or small-scale runs to validate critical models.

Summary: llmfit’s bandwidth + sampling approach is materially better than parameter-count heuristics for inference, but its accuracy depends on correct metadata and backend adaptation; measure locally when in doubt.

Core Analysis¶

Problem Focus: In CI/agent contexts you need fast decisions but also to avoid OOMs. llmfit supplies recommend --json for automation and llmfit bench/doctor for verification, which fits a layered integration approach.

Practical Recommendations (stepwise)¶

1. Static pre-filter (fast): Run llmfit recommend --json at pipeline start to get hardware-aware candidate models, then sort by fit/speed.

2. Environment & provider checks: Run llmfit doctor and ensure required runtimes (llama.cpp, Ollama, Docker Model Runner, etc.) are installed and configured in CI agents.

3. Isolated benchmark verification: For the top 1–3 candidates, run llmfit bench in a containerized or dedicated test node to obtain tok/s/TTFT and detect OOMs. Store measured numbers in your decision DB.

4. Cache & fallback: If bench is not available, use estimates with small-scale smoke tests as a safety net; implement automatic fallback to the last-validated model on failures.

5. Asynchronous data return: Optionally submit trusted measurements back to the llmfit model catalog (PR) to improve global estimates.

Important Notice: Don’t push recommend results straight to production without doctor/bench validation—estimates require correct metadata and backend setup.

Summary: Use llmfit as the fast selection layer, and rely on containerized local benchmarking for final verification to balance speed and safety in automated workflows.

Core Analysis¶

Problem Focus: MoE sparse activation, multi-GPU communication/splitting, and dynamic quantization materially affect memory and bandwidth; parameter-count heuristics fail here. llmfit uses model metadata (MoE/quantization flags), a bandwidth model, and multi-GPU adapters to improve estimates—but validation is required.

Behavior & Advantages¶

• MoE-aware: If the model metadata includes expert counts and routing sparsity, llmfit estimates activation memory using active-expert ratios instead of total parameters, avoiding severe overestimation.

• Multi-GPU awareness: Supports multi-GPU split estimates that account for per-device allocations and interconnect bandwidth (PCIe/NVLink) impacts on speed and placement.

• Dynamic quantization evaluation: Can compare quantization levels for trade-offs between memory savings and speed/quality, recommending practical quantization choices for supported backends.

Common Pitfalls & Mitigations¶

• Pitfall: Treating MoE by total-parameter memory — gross overestimate.

• Mitigation: Use llmfit info "<model>" to inspect routing/sparsity assumptions and run llmfit bench on the target backend to validate.

• Pitfall: Ignoring PCIe/NVLink topology effects in multi-GPU setups.

• Mitigation: Run llmfit doctor and multi-GPU benchmarks; adjust splitting strategy or choose a different parallelization mode if communication dominates.

Important Notice: llmfit improves estimates for MoE/multi-GPU/quantization, but relies on metadata-backend consistency. Always validate critical deployments with local bench or smoke runs.

Summary: llmfit provides refined estimations for MoE, multi-GPU and quantization scenarios, but accurate metadata and local validation are essential.