Run your entire LLM fleet from one air-gapped control plane.
FlowServe unifies inference, fine-tuning, and scheduling across NVIDIA, AMD, and Intel — on your hardware, in your network, with zero cloud dependency and zero vendor lock-in.
Running an LLM fleet shouldn't require five disconnected tools.
A scheduler, an inference server, a fine-tuning runtime, an adapter registry, and a dashboard — each with its own API, config format, and failure mode. FlowServe collapses the stack.
FlowServeUnified control for heterogeneous infrastructure.
Six capabilities, one plane. Deploy on bare metal, EKS, MAAS, vSphere — or a fully disconnected network.
Unified control plane
One API and console for training and inference. OpenAI-compatible endpoints out of the box. Fully sovereign — no cloud dependency.
Smart scheduling
Gang + memory-aware GPU placement that prevents VRAM OOM before it happens. Clean resource management across mixed workloads.
Fine-tuning engine
SFT and KL-anchored ASFT. PEFT/LoRA with Flash Attention 2. Auto-catalog tracks every adapter version — no drift, no orphans.
Inference optimizations
vLLM-based engine. TurboQuant KV-cache (1–4 bit, up to 16×, runtime-toggleable). DDTree speculative decoding for lower TTFT.
Hardware agnostic
NVIDIA H100/A100, AMD Instinct, Intel Gaudi 3. Switch silicon without rewriting a line of code.
Air-gapped deployment
Proven on fully disconnected networks. No data egress, no external dependencies. Built for sovereign-AI integrators and regulated industries.
Engineered for the bottlenecks that actually matter.
Memory bandwidth and time-to-first-token — the two failure modes where standard vLLM leaves performance on the table.
TurboQuant KV-cache
1–4 bit KV-cache quantization delivers up to 16× compression, runtime-toggleable without redeployment — improving TTFT, TPOT, and throughput on memory-bound hardware, without accuracy loss.
DDTree speculative decoding
Generates candidate token trees the main model verifies in parallel — near-draft-model speed with full-model accuracy. Built for latency-sensitive workloads where first-token speed is what users feel.
Gang-aware scheduling
Eliminates VRAM leaks and OOM crashes before production. Memory-aware placement keeps throughput and inter-GPU network efficiency predictable at scale.
Hardware-agnostic by design.
No NVIDIA lock-in. No rewrite when you switch silicon. Choose the best hardware for the workload — FlowServe runs natively on all of it.
Deployed with the teams building sovereign AI.
Pilots across energy, transportation, retail, and IT services — from edge devices to national infrastructure.
We publish the research behind the product.
Quantization, speculative decoding, anchored fine-tuning — written up as papers, engineering notes, and reproducible benchmarks.
TurboQuant: 1–4 bit KV-cache quantization
Why KV-cache is the bottleneck for quantized serving — and how runtime-toggleable 1–4 bit compression holds accuracy at up to 16×.
DDTree: block-diffusion speculative decoding
Candidate token trees the target model verifies in parallel — near-draft speed with full-model accuracy for latency-sensitive chat.
Compression vs. accuracy at 16×/8×/5.3×/4×
The accuracy/footprint curve across TurboQuant levels — with a methodology you can reproduce on your own hardware.
The sovereign AI stack is being decided now.
Every nation and regulated enterprise needs to run frontier models on their own hardware, in their own networks. FoundationFlow builds the control plane that makes that practical — and we're already in paid pilots with the organizations defining the category.