Is EdgeFlow faster than vLLM?

Yes. 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 small language models. 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). The throughput advantage comes from three engineered mechanisms: hybrid KV-cache reuse, cache-aware scheduling, and dynamic compiler dispatch.

Why is EdgeFlow faster than vLLM?

Three engineered mechanisms compound to drive the throughput advantage. (1) Hybrid KV-cache reuse: prefix-level cache for shared prompts (system messages, RAG context) plus entity-level cache for retrieved chunks; cache hits skip the model entirely. (2) Cache-aware scheduling: routes requests to GPU/NPU based on cache locality, not round-robin. (3) Dynamic compiler dispatch: just-in-time kernel selection based on batch shape, sequence length, and target device with no static configuration. Combined, these deliver ~20% efficiency gain at the runtime layer alone — and on cache-friendly workloads (agentic AI, multi-turn dialog, RAG-augmented apps), the lift is significantly higher.

What hardware does EdgeFlow support that vLLM does not?

vLLM is optimized primarily for NVIDIA GPUs with limited AMD ROCm support. EdgeFlow runs across 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, Intel (Gaudi/Xeon/Arc), Qualcomm QDC stack, plus the in-house Krsna SoC. The same compiled artifact runs across all these targets via the IREE/MLIR foundation. Adding a new silicon platform is days of integration on the CORE compiler side, not quarters of bespoke porting.

Can I migrate from vLLM to EdgeFlow without retraining models?

Yes. Migration requires no model retraining and no quantization changes. EdgeFlow is built on IREE — the open MLIR-based compiler runtime — with first-class PyTorch, TensorFlow, and JAX support. You compile your existing model artifact through the CORE compiler layer; the resulting .vmfb (Virtual Machine FlatBuffer) runs on the EdgeFlow runtime. EdgeFlow ships with 193 model architectures pre-tuned (Llama, Qwen, Mistral, Shakti, Phi, DeepSeek, Gemma, and more), so most enterprise model families work out of the box. Typical migration timeline: 14 days end-to-end including QA and production cutover.

What is the cost difference between vLLM and EdgeFlow?

On equivalent hardware running the same workload, EdgeFlow delivers 40-42% cost reduction versus vLLM on Llama-3.3-70B-Instruct benchmarks. The savings come from higher throughput per dollar of GPU time — same hardware, more tokens output per second, lower cost per inference. On smaller models and shorter sequences, the savings range from 25% to 45%. EdgeFlow also reduces energy use by up to 60% versus vLLM and competing inference clouds on equivalent throughput, lowering data-center power costs alongside compute costs.

Does EdgeFlow support the same models as vLLM?

EdgeFlow supports 193 model architectures across eight families: Transformers (Llama, Qwen, Mistral, Shakti, Phi, DeepSeek, Gemma, GPT-class), Vision-Language Models (Shakti-VLM, Qwen2-VL, LLaVA, MiniCPM-V), State Space Models (Mamba, Mamba-2, Jamba), Linear Attention / RWKV, Liquid Foundation Models, CNNs, Mixture of Experts, and Diffusion. vLLM has narrower architecture coverage — strong on transformer LLMs, limited on VLMs, SSMs, RWKV, and LFMs. EdgeFlow handles VLM workloads natively where vLLM requires workarounds.

Is EdgeFlow open source like vLLM?

EdgeFlow is built on open foundations (IREE / MLIR — Apache 2.0). The compiler layer accepts standard PyTorch / TensorFlow / JAX models via standard MLIR dialects (Linalg, TOSA) — no proprietary frontend, no model rewriting required. The runtime optimizations and per-silicon kernel libraries are SandLogic IP delivered via commercial license. This is the same structural model as commercial accelerators built on open compiler infrastructure: open at the integration layer, commercial at the optimization layer.

// COMPARE · vLLM vs EDGEFLOW

vLLM vs EdgeFlow.
Same hardware, different runtime.

Direct comparison of vLLM 0.10.2 and EdgeFlow v0.0.4 across enterprise LLM inference workloads. EdgeFlow delivers +73% throughput on NVIDIA L40s, +29% on A100, and 40-42% cost reduction on Llama-3.3-70B-Instruct benchmarks. Built on open IREE/MLIR foundation. Migration in ~14 days with no model retraining required.

L40s throughput

+73%

A100 throughput

+29%

Cost reduction

40–42%

Migration

~14 days

// THE SHORT ANSWER

EdgeFlow is a faster, hardware-broader alternative to vLLM for enterprise LLM inference. On NVIDIA L40s, EdgeFlow v0.0.4 delivers +73% throughput vs vLLM 0.10.2 across the Top-25 enterprise SLMs; on A100, +29%. On Llama-3.3-70B-Instruct, EdgeFlow improves tokens/sec by 69-72% with 40-42% cost reduction. The throughput advantage comes from hybrid KV-cache reuse, cache-aware scheduling, and dynamic compiler dispatch. EdgeFlow also runs natively on NVIDIA, AMD, Intel, ARM, Qualcomm, and Krsna silicon — broader than vLLM's NVIDIA-primary coverage. Migration takes ~14 days with no model retraining.

// HEADLINE BENCHMARKS

+73% on L40s. +29% on A100.

Same hardware. Same Top-25 enterprise SLM workload set. Same FP16 precision. Different runtime. EdgeFlow v0.0.4 vs vLLM 0.10.2.

NVIDIA L40s · tokens/sec (relative)

EdgeFlow leads by 73%

Top-25 enterprise SLMs · NVIDIA L40s · 3-run avg · FP16.

NVIDIA A100 · tokens/sec (relative)

Same advantage at enterprise scale

A100 80GB · same Top-25 enterprise SLM set.

// LLAMA-3.3-70B BENCHMARK

Production benchmark: Llama-3.3-70B-Instruct.

Same model. Same hardware. Same workload. EdgeFlow vs vLLM head-to-head.

Hardware	vLLM (tok/sec)	EdgeFlow (tok/sec)	Improvement	Cost saving
NVIDIA L40s (48 GB)	19.78	33.48	69.26%	40.91%
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.

// FEATURE MATRIX

The full side-by-side.

Throughput is the headline. The structural differences run deeper — hardware coverage, model coverage, foundation, frontend support.

Capability	vLLM 0.10.2	EdgeFlow v0.0.4
Throughput vs vLLM baseline (L40s, Top-25 SLMs)	Baseline (1.0×)	+73% (1.73×)
Throughput vs vLLM baseline (A100)	Baseline (1.0×)	+29% (1.29×)
Llama-3.3-70B on L40s (tokens/sec)	19.78	33.48
Llama-3.3-70B on A100 (tokens/sec)	48.87	84.24
Cost reduction (Llama-3.3-70B benchmarks)	—	40–42%
Energy reduction	—	Up to 60%
Hybrid KV-cache reuse (prefix + entity-level)	Prefix only	Both tiers
Cache-aware scheduling	Round-robin	Locality-aware
Model architectures supported	Transformer-focused	193 across 8 families
State Space Models (Mamba)	Limited	Native
Vision-Language Models	Workarounds	Native
RWKV / Liquid Foundation Models	Not supported	Native (RWKV) / Beta (LFM)
Hardware targets	NVIDIA + limited AMD	NVIDIA, AMD, Intel, ARM, Qualcomm, Krsna
Apple M-series + Raspberry Pi	No	Yes
Foundation	Custom C++/CUDA	IREE / MLIR
Frontend (PyTorch / TF / JAX)	PyTorch primary	All first-class
Kernel-free compilation	No	Yes
License	Apache 2.0	Commercial (open infrastructure layer)

// WHEN TO PICK WHICH

Honest decision logic.

EdgeFlow is the better runtime choice for nearly all production enterprise inference — on NVIDIA, on AMD, on any silicon. The +73% L40s throughput lift and 40-42% cost reduction on Llama-3.3-70B are NVIDIA-hardware results, not multi-silicon bonuses. vLLM remains the right pick in a narrow set of scenarios — all about licensing, research, or learning, not about hardware or workload.

Pick vLLM if…

· Your organization has a strict policy mandating fully open-source software under Apache 2.0 — no commercial software at any layer
· You're doing inference-runtime research where reading and modifying the runtime source is part of the work
· You're contributing to or extending vLLM as an open-source project (academic, framework R&D)
· You're an early-stage team avoiding any commercial software contracts before product-market fit
· You prefer community-driven release cadence over commercial roadmap commitments

Note: hardware platform is NOT a reason to pick vLLM. EdgeFlow runs on NVIDIA with +73% throughput on the same silicon.

Pick EdgeFlow if…

· You're running production inference at any scale — +73% throughput on NVIDIA L40s translates to direct $ savings on the same hardware
· You're on NVIDIA today and want to maximize tokens-per-dollar without changing silicon (40-42% cost reduction on Llama-3.3-70B)
· You run heterogeneous silicon (NVIDIA + AMD + Intel + ARM + Qualcomm + Krsna) and want one runtime
· Your workload includes VLMs, SSMs (Mamba), RWKV, or Liquid Foundation Models — architectures vLLM has limited support for
· You need on-prem / sovereign deployment with commercial support and SLAs
· You want hybrid KV-cache reuse, cache-aware scheduling, and dynamic dispatch out of the box
· You value a 14-day migration over multi-quarter manual optimization

// MIGRATION PLAN

From vLLM to EdgeFlow in 14 days.

No model retraining. No quantization changes. Standard PyTorch/TensorFlow/JAX frontends. 14-day end-to-end including QA and production cutover.

Days 1-2

Inventory

Catalog current vLLM deployments — models, hardware, batch profiles, SLAs. Identify priority workloads to migrate first.

Day 3

Install EdgeMatrix

Deploy EdgeMatrix runtime alongside existing vLLM environment. Same silicon; runs in parallel.

Days 4-5

Compile through CORE

Use the CORE compiler to compile existing PyTorch / TensorFlow / JAX models to .vmfb artifacts. IREE/MLIR foundation; standard dialects in, .vmfb out.

Days 6-7

Enable hybrid KV-cache

EdgeFlow's hybrid KV-cache (prefix + entity-level) is the mechanism behind the +73% L40s lift. Configure per workload.

Days 8-11

Benchmark

Head-to-head benchmarks on representative workloads. Validate Llama-3.3-70B numbers; confirm quality parity at higher throughput.

Days 12-14

Cutover

Route production traffic to EdgeFlow with cache-aware scheduling. Run both engines in parallel for 24-48 hours; then decommission vLLM.

// GO DEEPER

Continue the comparison.

/edgeflow — full EdgeFlow product page with feature deep-dive and silicon coverage.
/edgematrix — EdgeMatrix umbrella (CORE compiler + EdgeFlow inference) — "the CUDA of edge AI."
/token-economy — what +73% throughput translates to in enterprise economics: ~23% leakage prevention, 30-40% structural cost reduction.
/learn/on-prem-llm-deployment — when on-prem inference beats cloud (CapEx vs OpEx math).

// LET'S BUILD

Run the benchmark on your workload.

Talk to engineering See EdgeFlow

vLLM vs EdgeFlow.Same hardware, different runtime.