Full-stack AI acceleration platform for enterprise.
Run AI faster and smarter. Maximize AI performance across any hardware platform. Simplify AI workload deployment, scaling, and orchestration with Docker and Kubernetes.
Performance Optimization.
Optimal inference performance across any workload or hardware, with zero manual tuning.
Proven by MLPerf Benchmark.
State of the art multiple achievements in benchmarks.
Frontier Open Models.
The popular open models, optimized out of the box.
Auto tuned, Turnkey inference platform.
Covers the full inference stack. Routing, serving, KV cache tiering, and storage.
Top Application Layer
Routing Layer
Serving Engine Layer
Prefix Tiering Layer
AI Ecosystem Layer
AI Backend Storage
OS/Foundation Layer
Physical Hardware Layer
The limit isn't the hardware. It's the software.
LLMBoost™ optimizes disaggregated prefill/decode inference on AMD Instinct™ MI300X. The result clears all 60 of NVIDIA's published H100 results on InferenceX. Verified by AMD.
Generation throughput
Per GPU, at equal load. Never below 2.4× from 6 to 1,229 concurrent requests.
Speed per user
174 tok/s/user, past H100's fastest.
Lower response latency
6.0 s end to end, against H100's best.
* DeepSeek-R1-0528 FP8, 9 nodes, per GPU · H100 results as published June 25, 2026 · throughput from the 1K1K workload, per-user speed and latency from 8K1K.
Frontier Open Models. Boosted Beyond Limits.
LLMBoost serves and optimizes today's most popular open source models on AMD Instinct™ GPUs (MI300X/ MI355X), delivering high token throughput and cost efficiency per token.
* Model availability is expanding over time and may change. All product and company names, trademarks, and logos are the property of their respective owners and are used here for identification only.
What workloads is it built for?
Explore the full range of workloads Mango LLMBoost™ is built to accelerate, from AI inference and training to large-scale LLM serving and beyond.
Production LLM Serving
Run chatbots, copilots, and AI search at scale with consistent low latency and high throughput.
Enterprise Fine-Tuning
Train and fine-tune Llama-class models in minutes, not days. Built-in checkpointing included.
Multi-Modal & Reasoning
Accelerate vision-language and reasoning models with up to 43× faster throughput on Qwen2.5 and 520× faster TTFT on DeepSeek-R1.
Hybrid Infrastructure
Deploy the same stack on-prem, in cloud, or across mixed AMD/other NPU clusters with no vendor lock-in and no rewrites.
Latest from MangoBoost.
Benchmark milestones, engineering deep-dives and company announcements.






