EdgeFlow is the inference acceleration engine inside EdgeMatrix — SandLogic's vendor-neutral inference layer. It runs LLMs, CNNs, and Transformer models up to 10× faster than baseline, reduces compute cost by up to 70%, and cuts energy use by up to 60% versus competing inference frameworks (Groq, Together.ai, Fireworks, OctoAI). EdgeFlow is the throughput-and-economics layer; CORE is the architecture-dispatch layer beneath it.

How much faster is EdgeFlow than vLLM?

EdgeFlow v0.0.4 delivers +73% throughput versus vLLM 0.10.2 on NVIDIA L40s and +29% on NVIDIA A100, measured on the Top-25 enterprise SLMs. On Llama-3.3-70B-Instruct specifically, EdgeFlow achieves 33.48 tokens/sec on L40s (vs 19.78 baseline — 69.26% improvement, 40.91% cost saving) and 84.24 tokens/sec on A100 (vs 48.87 baseline — 72.34% improvement, 41.78% cost saving).

What hardware does EdgeFlow support?

EdgeFlow runs on the full silicon envelope enterprises actually deploy: NVIDIA GPUs (H100, A100, L40s, L4), AMD CPUs and GPUs with ROCm, Apple M-series silicon, ARM-class edge processors including Raspberry Pi, and Qualcomm QDC stack. The same compiled artifact runs across these targets — adding a new silicon platform is days of integration, not quarters of bespoke porting.

How does EdgeFlow achieve its throughput advantage?

Three engineered mechanisms compound to drive throughput. First, hybrid KV-cache reuse — prefix-level cache for shared prompts (system messages, RAG context) and entity-level cache for retrieved chunks; cache hits skip the model entirely. Second, cache-aware scheduling — routes requests based on cache locality, not round-robin. Third, dynamic compiler dispatch — just-in-time kernel selection based on batch shape, sequence length, and target device with no static configuration. Together these mechanics deliver ~20% efficiency gain at the runtime layer alone.

How many model architectures does EdgeFlow support?

EdgeFlow ships pre-tuned for 193 model architectures across eight families: Transformers (Llama, Qwen, Mistral, Shakti, Phi, DeepSeek, Gemma), VLMs (Shakti-VLM, Qwen2-VL, LLaVA), State Space Models (Mamba, Mamba-2, Jamba), Linear Attention / RWKV, Liquid Foundation Models, CNNs (ResNet, YOLO, EfficientNet, MobileNet), Mixture of Experts (Mixtral, DeepSeek-V3), and Diffusion (Stable Diffusion, SDXL, Flux). New architectures integrate in days, not quarters.

// L02 · EDGEMATRIX > EDGEFLOW

Turbocharge your LLMs & AI models.

Name: EdgeFlow — Inference Acceleration Engine
Brand: SandLogic
Availability: InStock

EdgeFlow is the inference acceleration engine inside EdgeMatrix. Achieve up to 10× faster inference, lower compute cost by 70%, and deploy real-time AI at scale — on cloud, edge, or CPU. Adaptive across CNN, RNN, and Transformer models. Optimized for NVIDIA GPUs, AMD CPUs, Apple M-series, and Raspberry Pi.

Faster inference

10×

Compute cost reduction

70%

Energy reduction

60%

Models pre-tuned

193

// SCOPE

EdgeFlow is the inference acceleration engine inside EdgeMatrix. EdgeMatrix has two layers: CORE (the compiler + runtime that dispatches architectures to silicon) and EdgeFlow (the engine that drives inference throughput and cost). This page is about the throughput-and-economics layer — how the same workload runs faster, cheaper, on fewer watts.

// WHY EDGEFLOW

Faster than the rest. On every device.

// FEATURE 01

Blazing fast inference

Accelerate token generation across LLMs, CNNs, and Transformers with minimal latency. Up to 10× faster on production workloads.

// FEATURE 02

Universal hardware compatibility

Optimized for NVIDIA GPUs (H100, A100, L40s), AMD CPUs, Apple M-series, and Raspberry Pi. Same engine, every device class.

// FEATURE 03

Enterprise-ready

Plug seamlessly into your AI workflows — chatbots, OCR, real-time document processing, voice agents, agentic pipelines.

// FEATURE 04

Adaptive model coverage

CNN, RNN, and Transformer architectures handled by the same dispatch pipeline. 193 model architectures pre-tuned for fastest inference.

// FEATURE 05

Intelligent memory reuse

Hardware-aware graph optimization and memory traffic minimization compound to drive throughput up while pushing energy use down.

// FEATURE 06

Kernel-free compilation

No bespoke kernel writing per model or per silicon target. Compile once; deploy across the supported hardware envelope.

// COST REDUCTION

Reduce inference costs by over 70%.

Llama-3.3-70B-Instruct production benchmarks. Same model, same hardware, same workload — different inference engine.

Model (context size)	Hardware	Without EdgeFlow (tok/s)	With EdgeFlow (tok/s)	Improvement	Cost saving
Llama-3.3-70B-Instruct · 42.5 GB	NVIDIA L40s (48 GB)	19.78	33.48	69.26%	40.91%
Llama-3.3-70B-Instruct · 42.5 GB	NVIDIA A100 (80 GB)	48.87	84.24	72.34%	41.78%

Methodology: production deployment benchmarks. Token throughput measured under sustained load; cost savings computed against same-workload baseline. Detailed methodology and reproducibility kit available under NDA.

// PERFORMANCE COMPARISON

Outperforms Groq, Together.ai, OctoAI.

Token throughput on representative LLM workloads, normalized to EdgeFlow on H100 (100). EdgeFlow leads the hosted-inference category and stays ahead on smaller silicon. Energy use down up to 60% over the same comparison set.

Relative throughput, indexed to EdgeFlow on H100 (100). Comparison set: representative open-weight LLM under sustained agentic load. Energy comparison: −60% on the same set.

// THE INTELLIGENCE BEHIND THE SPEED

Hardware-aware. Memory-aware. Architecture-aware.

EdgeFlow leverages hardware-aware graph optimization, intelligent memory reuse, and adaptive precision to maximize model throughput across the silicon envelope — without bespoke per-target engineering.

Faaast

Adaptive to CNN, RNN, and Transformer models.

Powerful

Intelligent memory reuse.

Precision support

FP16 · INT8 · INT4.

Kernel-free compilation

No bespoke kernel writing per silicon target.

// UNIVERSAL HARDWARE COMPATIBILITY

Same engine. Every device class.

EdgeFlow runs across the silicon envelope customers actually buy — from data-center NVIDIA to AMD CPUs to Apple M-series workstations to Raspberry Pi class. One engine. One deployment story.

NVIDIA GPUs

H100 · A100 · L40s · L4

AMD

CPUs · GPUs · ROCm stack

Apple silicon

M-series workstation

Edge & embedded

Raspberry Pi · ARM · NPU

// ENTERPRISE-READY

Plug seamlessly into your AI workflows.

EdgeFlow is the inference layer beneath the workflows enterprises already run — chatbots, OCR, document processing, voice agents, agentic pipelines, multimodal applications.

CHATBOTS

Sustained agentic loads with low-latency turn-taking. EdgeFlow keeps response times within real-time conversation bounds even under concurrent fleets.

OCR & DOCUMENT AI

High-throughput page processing for invoices, contracts, KYC, claims. CNN preprocessing pipeline + LLM understanding in the same engine.

VOICE AGENTS

ASR + LLM + TTS in one inference pipeline. Time-to-first-token measured in milliseconds, not seconds.

AGENTIC AI

Multi-step tool invocations, RAG retrieval, deterministic dispatch. EdgeFlow keeps the cost of multi-step agent chains tractable at scale.

REAL-TIME ANALYTICS

Streaming inference for fraud detection, log analysis, anomaly detection. Throughput sustained on commodity silicon.

MULTIMODAL

VLM, speech, and document pipelines on the same engine. No separate inference stack per modality.

// RELATED SURFACES

Where EdgeFlow sits in the stack.

/edgematrix — the umbrella product. EdgeFlow is the inference engine; CORE is the compiler + runtime engine.
/any-ai — CORE's architectural-flexibility story. The 8 architecture families and the dispatch matrix.
/token-economy — what the inference acceleration translates to in enterprise economics: ~23% token leakage prevention, 30–40% structural cost reduction.
/krsna — the in-house silicon EdgeFlow runs on natively, alongside the third-party silicon listed above.

// LET'S BUILD

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