Deploy, benchmark, optimize, and compare open-source LLMs on ARM64 infrastructure. Unlock up to +169% throughput with SVE2 vector acceleration, NUMA thread pinning, and INT4 quantization.
$ armpilot benchmark --model llama-3.2-3b --threads 32
✓ Detected 64 aarch64 cores with SVE2 vector support
✓ KV cache compressed: Q8_0 page table (3.2 GB total)
✓ Peak throughput reached: 2,840 tokens/min (+2.7×)
Deploying large models on CPU-efficient ARM architectures requires balancing complex hardware interconnects, memory bandwidth saturation, thread affinity, and quantization trade-offs.
Engineered specifically for Neoverse, Graviton, Ampere, and Apple Silicon compute environments.
Run supported open-source models (Llama 3.2, Mistral 7B, Phi-3, Gemma, Qwen2.5) directly on ARM64 infrastructure with native SVE2 & NEON execution.
Measure latency percentiles (P50/P95/P99), throughput, TTFT, tokens/sec, working set memory footprint, and physical core utilization curves.
Experiment with runtime parameters, INT4/INT8 quantization, thread affinity pinning, NUMA-aware memory interleaving, and KV cache compression.
Evaluate models and configuration combinations side-by-side using reproducible data, before/after charts, and exportable executive reports.
From model selection to hardware-optimized production inference in four simple stages.
Choose from optimized open-source LLMs including Llama 3.2, Mistral 7B, Phi-3 Mini, Gemma 2B, and Qwen2.5.
Tune concurrency, thread affinity pinning, KV cache quantization, batch size, and execution backends (llama.cpp, ExecuTorch).
Execute automated micro-benchmarks with high-resolution telemetry capturing TTFT, P95/P99 latency, and interconnect load.
Diagnose memory bandwidth bottlenecks with automated AI recommendations and export reproducible benchmark reports.
Live measurements collected on 64-core Arm architecture running Llama 3.2 3B INT4.
Generation velocity achieved through 32 pinned threads & SVE2.
Prompt prefill latency reduced via quantized KV cache allocation.
95th percentile request completion time under concurrent load.
Total RAM footprint with INT4 GGUF weights + dynamic context buffer.
Sustained batch throughput across 16 concurrent requests.
High compute saturation without thermal or interconnect throttling.
As LLMs become smaller and more specialized, running cost-effective inference on ARM64 infrastructure provides distinct architectural advantages over traditional power-hungry GPU clusters.
Up to 3x higher performance-per-watt on cloud instances (AWS Graviton, Ampere Altra) reduces monthly inference compute bills.
64 to 128 physical Neoverse cores per socket allow massive concurrency without multi-GPU interconnect bottlenecks.
Run identical quantized model pipelines across edge devices, developer laptops (Apple Silicon), and enterprise servers.
Arm Scalable Vector Extension 2 (SVE2) and Dot Product instructions accelerate int4/int8 matrix ops natively in hardware.
Explore the actual application dashboard interface built for developers and ML infrastructure engineers.
Deploy models, measure latency down to the microsecond, and unlock the full potential of your ARM64 silicon today.