NeuralDrift Local AI Benchmark Methodology

Most local AI benchmarks report a single number: tokens per second (tok/s). In real-world local AI deployments—whether running an autonomous coding agent, an on-device function-calling workflow, or a local Retrieval-Augmented Generation (RAG) assistant—tokens per second alone frequently fails to predict which system actually finishes work faster.

Inspired by modern workload-centric benchmarking standards (such as the edge-agent and RAG directions introduced in MLPerf Inference v6.1), the NeuralDrift Local AI Benchmark Framework evaluates real, multi-stage local workloads while strictly separating raw inference throughput from end-to-end task execution.

Methodological Boundary & Disclosure: NeuralDrift benchmarks are independent NeuralDrift Lab workload benchmarks. While we adopt rigorous edge-agent and RAG testing concepts from industry standards, NeuralDrift benchmarks are not official MLCommons MLPerf benchmarks, nor does NeuralDrift claim MLPerf compliance or certification.

#1. The Two Benchmark Paradigms

NeuralDrift never collapses multi-dimensional performance into a single misleading composite score. We measure two separate paradigms:

Paradigm A: Raw Inference Benchmarks

Measures the underlying model and runtime execution speed in isolation:

• →Time to First Token (TTFT): The latency from prompt transmission until the first generation token is emitted. Captures prompt prefill efficiency and context processing.
• →Prompt Processing Throughput (prompt tok/s): The rate at which prompt tokens are ingested into the key-value (KV) cache.
• →Generation Throughput (gen tok/s): The sustained token generation speed during auto-regressive decoding.
• →Model Load Latency: Wall-clock time required to load weights from disk into active VRAM.
• →Peak Hardware Telemetry: Maximum dedicated VRAM allocation, system RAM, and GPU compute utilization during the run.

Paradigm B: End-to-End Workload Benchmarks

Measures whether the local AI system completes useful work under real-world task constraints:

• →Task Completion Time: Total elapsed duration from user prompt until verified task delivery.
• →Tool Selection & Invocations: Total tool calls attempted, valid tool calls, and syntax errors.
• →Tool Call Latency: Elapsed time between the model emitting a tool invocation, local execution resolving, and the result returning to context.
• →Retries & Recovery: Number of correction turns required when an argument schema fails.
• →Final Task Success: Objective validator confirming whether the output achieved the stated goal.

#2. Standardized Workload Suite

NeuralDrift version-controls all workloads to guarantee historical reproducibility:

Future extensions expand this suite into Creator Workloads (image synthesis steps, video generation via MiniMax H3 / FastH3, ComfyUI graph execution) and Multi-Machine / Hybrid inference.

#3. Hardware Profiles & Environmental Capture

A benchmark is only as trustworthy as the exact environment that generated it. NeuralDrift captures hardware telemetry programmatically at execution time:

• →GPU Architecture: Name, microarchitecture (e.g. Blackwell, Ada Lovelace, RDNA3), core count, and form factor.
• →Memory Subsystem: Dedicated physical VRAM (bytes) and total installed system RAM.
• →Driver & Compute Stack: NVIDIA display driver, CUDA user-mode driver (UMD), ROCm version, and operating system build.
• →Runtime Stack: Execution engine (LM Studio, Ollama, llama.cpp, vLLM, OpenVINO) and runtime version.

Our primary local testbench operates an NVIDIA GeForce RTX 5080 (16GB GDDR7 VRAM on a 256-bit bus) paired with an AMD Ryzen 9 9950X3D CPU and 64 GB DDR5 memory.

#4. Deterministic Configuration Fingerprinting

To prevent unscientific comparisons between runs with differing variables, every execution generates a deterministic SHA-256 Configuration Fingerprint:

$$\text = \text(\text(\text + \text + \text + \text + \text + \text + \text))$$

NeuralDrift's comparison engine validates this fingerprint. Two runs that differ in workload version or sampling parameters are marked Incomparable or flagged with Material Configuration Warnings.

#5. Objective Validation Over Subjective Review

Human "looks good to me" scoring introduces variance and bias. NeuralDrift workloads enforce automated, objective validation methods:

1. →JSON Schema Enforcement: Strict validation of required keys, types, and values.
2. →Tool Call Assertions: Verification that the model selected the exact required tool name and passed valid strongly typed parameters.
3. →Factual String Assertions: Exact or token-containment verification on known reference solutions.
4. →Failure Transparency: When a model hallucinates, fails tool schema parsing, or exceeds memory limits, the run is immutably logged as FAIL or ERROR. Failures are never hidden or substituted with synthetic passes.

#6. The Evidence Hierarchy

Every claim in the NeuralDrift catalog and Lab sits on an explicit evidence tier:

1. →Upstream Claim: Published specification or model card claim.
2. →Community Result: External report from a credible source without local reproduction.
3. →NeuralDrift Documented: Architectural requirements and dependencies reviewed by NeuralDrift.
4. →NeuralDrift Execution Tested: Installed and executed successfully on Lab hardware.
5. →NeuralDrift Benchmarked: Executed under versioned workload protocols with recorded telemetry and raw trace evidence.
6. →NeuralDrift Verified: Current, hash-matched, reproducible execution meeting top-tier standards.

Review live benchmark records and compare runs directly in the NeuralDrift Lab.