Running sophisticated large language models locally has transitioned from a niche developer experiment to a core pillar of modern edge infrastructure. As organizations and engineers increasingly look to execute workloads outside centralized cloud data centers, understanding edge computing versus edge AI becomes essential. Edge infrastructure refers to the compute, storage, and networking resources deployed closer to where data is generated and consumed—minimizing latency, reducing bandwidth costs, and safeguarding data privacy. In the context of enterprise environments, Edge AI—as explored across industry frameworks like IBM's definitions of edge intelligence—integrates machine learning inferencing directly onto localized hardware rather than relying solely on remote cloud APIs.
Successfully executing an ai model local deployment requires balancing raw hardware capabilities with optimized inference runtimes. With unified memory architectures and high-performance tensor frameworks like mlx-serve, running 24-billion-parameter models on consumer-grade or prosumer desktop hardware is now remarkably viable. In this analysis, we examine the empirical performance scorecard of the LiquidAI 24B 4-bit model pack (lfm25-24b-a2b-4bit-20261013b), dissecting its throughput, latency, competency profile, and operational trade-offs when operating on modern Apple Silicon.
Abstracted Setup & Environment
To maintain strict comparability and reproducibility across local inference evaluations, testing was performed under a standardized, abstracted setup:
- Hardware Platform: Apple Silicon M4 Max with 128 GB of unified memory.
- Inference Runtime:
mlx-serve(version 26.10.2). - Model Architecture: Public open-weights model IDs for LiquidAI LFM2.5-24B-A2B quantized to 4 bits.
- Timeline: October 2026 benchmark run.
This configuration isolates the runtime efficiency of local execution without introducing cloud network latency or multi-tenant GPU contention.
Edge Infrastructure Readiness for AI Model Local Deployment
When architects evaluate how to deploy AI models on edge hardware, the bottleneck is rarely just raw teraflops; it is memory bandwidth, quantization loss, and thermal dissipation—the same levers examined in practical quantization strategies for AI edge infrastructure. Edge infrastructure readiness demands that models maintain high fidelity while fitting comfortably within local memory envelopes. The LiquidAI 24B 4-bit pack represents an interesting intersection of scale and efficiency. By compressing a 24-billion-parameter architecture down to a 4-bit representation, developers can load the model into local memory without exhausting available RAM, leaving plenty of headroom for operating system tasks and concurrent applications.
Unlike cloud deployments where scaling is a matter of spinning up additional cluster nodes, edge compute relies on squeezing maximum tokens per second out of a single physical device. This makes understanding serving engines critical. Modern inference stacks designed for Apple Silicon take full advantage of unified memory bandwidth, allowing the CPU and GPU cores to share tensor data instantly without PCIe transfer bottlenecks.
Throughput and Speed Benchmarks
Raw token generation speed determines whether a local model feels sluggish or conversational. The benchmark scorecard for the LiquidAI 24B 4-bit pack evaluates performance across two distinct execution modes: plain execution and optimized "best" execution mode.
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Plain Mode:
- Decode Speed: 85.68 tokens per second
- Prefill Speed: 409.71 tokens per second
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Best Mode:
- Decode Speed: 180.45 tokens per second
- Prefill Speed: 482.32 tokens per second
The jump from plain decode (85.68 tok/s) to best decode (180.45 tok/s) highlights the profound impact of inference optimizations, KV-caching strategies, and hardware-accelerated attention kernels available in modern serving runtimes. A decode throughput exceeding 180 tokens per second on a 24B parameter model transforms local execution into a snappy, real-time experience that easily rivals remote cloud API responses—all while keeping data entirely on-device. Similarly, prefill speeds pushing up to 482 tok/s mean prompt ingestion is virtually instantaneous for typical user queries.
Quality and Competency Breakdown
Speed means very little if the model hallucinates or fails to follow instructions. The scorecard breaks down the model's competency across four core functional categories, yielding an overall quality score of 64.4 alongside a flawless 100.0 repeatability metric.
| Evaluation Category | Score |
|---|---|
| Instructions | 91.7 |
| Structured Outputs | 58.3 |
| Reasoning | 7.7 |
| Tool Use | 100.0 |
| Overall Quality | 64.4 |
| Repeatability | 100.0 |
Analyzing the Competency Profile
- Instruction Following (91.7): Scoring near the top tier in instruction adherence, this model pack excels at sticking to user constraints, formatting rules, and stylistic guidelines without drifting.
- Tool Use (100.0): A perfect score in tool use is particularly noteworthy for agentic workflows and local automation scripts. When tasked with calling functions or structuring API payloads, the model exhibits robust reliability.
- Structured Outputs (58.3): While adequate for general JSON or schema generation, structured output compliance shows room for improvement compared to its stellar instruction-following scores. Developers enforcing strict JSON schemas may want to pair this model with programmatic grammar constraints.
- Reasoning (7.7): Deep mathematical, multi-step logical reasoning scores lower on this specific benchmark subset. Highly complex theorem proving or intricate logic puzzles are not the primary strengths of this particular weight pack.
- Repeatability (100.0): Achieving 100% repeatability signifies exceptional stability across repeated runs under identical parameters—crucial for production reliability where deterministic outputs matter.
Practical Considerations for Local and Edge Workloads
Deploying a model like LiquidAI 24B locally changes how teams think about data governance and latency. Because every inference happens locally, sensitive documents, customer queries, and proprietary codebases never traverse external networks. However, running a 24B model requires respecting hardware boundaries. While 128 GB of unified memory handles the 4-bit weights comfortably, thermal throttling under sustained multi-hour batch workloads must be monitored on passively cooled or thin client enclosures.
For teams building edge solutions, the combination of 180 tok/s decode performance and perfect tool-use reliability opens up powerful possibilities for offline assistants, local developer tooling, and secure enterprise environments where cloud connectivity is restricted or undesirable. The same vendor's smaller releases for constrained devices are covered separately in Liquid AI's LFM2.5-2.6B edge model, and this pack's results can be placed alongside competing open weights in the 2026 cohort leaderboard.
Conclusion and Measurement Disclaimer
The LiquidAI 24B 4-bit pack running on Apple Silicon via mlx-serve demonstrates that high-performance local AI deployment is thoroughly practical for modern edge infrastructure. With blistering decode speeds and flawless tool-use capabilities, it bridges the gap between cloud-scale intelligence and local execution privacy.
Disclaimer: These are measurements from one machine, one server build, and one test suite on the stated date. Results vary with hardware, software version, settings, and load. They are not a general ranking or a recommendation.
