apex tune
General performance optimization — automatic rewriting for CPU-GPU sync reduction, memory layout, torch.compile, and more.
Common Challenges
Sound familiar?
- Inference is too slow to meet real-time requirements on the target device.
- Quantization and pruning lead to unacceptable drops in model precision.
- The model exceeds the device's limited memory or power budget.
- High-level frameworks fail to convert or utilize specialized NPU/GPU cores.
- It is unclear if the optimization effort will yield a meaningful speedup.
Fixstars solves these with 20 years of embedded acceleration experience and an AI-native development environment.
Service
From port to peak performance
We take your AI model from "initial assessment" to "guaranteed hardware-limit performance." With packages starting at $5,000 and a 5% performance guarantee, we ensure your vision models, LLMs, and VLMs are production-ready on your target silicon.
Port
Get your model running on the target hardware. We work with chip-specific SDKs and toolchains to adapt the model to its new environment.
Optimize
Quantization, kernel optimization, memory layout tuning, and processor task allocation — striking the right balance among accuracy, latency, and power.
Validate
Real-hardware benchmarks, accuracy validation, and latency measurement to confirm you have hit the spec.
Improve
As models, chips, and toolchains evolve, we keep performance moving in the right direction over time.
Quality / Code
Optimization
High-performance output code with APEX
APEX (Agentic Performance Engineering eXperience) is our framework that captures 20 years of Fixstars performance-engineering know-how in a form AI agents can leverage autonomously. With this optimization know-how built into the framework, the code AI produces achieves performance on par with veteran engineers.
Capabilities
apex convert
TensorRT acceleration for PyTorch inference — automatic, functionally-equivalent rewrites that avoid graph breaks.
apex tune-discover
Discover new optimization patterns — the LLM autonomously finds and accumulates novel optimizations not in the existing playbook.
* More capabilities are added on an ongoing basis.
Performance gains from autonomous optimization
ResNet-50 Training
bf16 mixed precision, NHWC memory format fix, torch.compile applied
x2.11
SparseDrive Training
CPU-GPU sync reduction, CPU affinity tuning
x2.04
DETR Inference
TensorRT compatibility, dynamic shape removal, TensorRT backend
x2.12
Optical Flow
OpenCL UMat enabled, persistent UMat
x7.67
* Measured on Fixstars internal benchmarks (reference values).
Technology
Agents in the
Agents in the
optimization loop
Our optimization pipeline is driven by AI agents — and the agents carry 20 years of embedded acceleration knowledge. Chip-specific patterns, quantization strategies, lessons from past projects. The agents consult all of it when making optimization decisions.
Work engineers used to do by hand now runs on agents. The result: hardware-level performance, in a fraction of the time.
For Your Team
Explore Secure AI Environment
Want this stack for your own team?
End-to-end support for a secure AI dev environment — infrastructure that keeps code in-house, AI coding tools, internal knowledge integration, and adoption training.
Platforms
Works on your silicon
We port and optimize across a wide variety of processors, including the targets below. Each gets architecture-tailored optimization, and next-generation processors come online as they ship.
SoC platforms
-
NVIDIA DRIVE Thor
-
NVIDIA DRIVE Orin
-
Renesas R-Car
-
Qualcomm Snapdragon Ride & Cockpit
-
MediaTek Dimensity Auto
-
NXP S32
DSPs
-
Synopsys ARC VPX DSP
-
Cadence Vision DSP
-
CEVA Vision AI DSP
-
Texas Instruments DSP
Other processors? Get in touch.
Security
Your code stays yours.
Your code stays yours.
Performance stays peak.
Choose on-premises or dedicated cloud — whichever matches your security policy. Either way, your code and models never leave your perimeter.
On-premises
Open LLMs like Gemma and Qwen, all inside your network. Code and data don't leave the building.
- Code and data stay inside your network
- Open LLMs (Gemma, Qwen, and others)
- Even the strictest policies, handled
Dedicated cloud
Use the latest API-based LLMs like Claude Code in a dedicated cloud environment. Your input and output never feed model training.
- Access to the latest LLMs (Claude Code and others)
- Input and output never used for training
- Performance and flexibility, together
Pricing
Starting at $5,000
Pricing details
- Initial model and hardware assessment
- Profiling and bottleneck analysis
- One optimization trial for the agreed target runtime or device
- Before/after benchmark report
- Recommendations for next-step production optimization
* Typical timeline: 4–8 weeks
Risk-free performance guarantee
If the agreed primary performance metric does not improve by at least 5% under the mutually defined benchmark conditions, we will refund the service fee in full.
Notes & conditions
- Scope and environment will be determined through consultation.
- Client provides: target hardware access, model files, runtime environment, and benchmark criteria.
- Joint case study or white paper may be discussed upon significant speedup.
Why Fixstars?
Fixstars' Strengths
100+ clients. 20 years. 99% come back.
We have helped over 100 clients across industries ship faster software. They keep coming back — 99%+ continued-engagement rate.
Learn more
To speed up software, know the hardware
CPU, GPU, FPGA, DSP, SoCs — we have shipped optimization work on all of them.
Learn more
AI-native, with proprietary knowledge
20 years of acceleration knowledge, built into the development environment. Runs on-prem so your code never leaves your infrastructure.
Learn more
Get Started
Talk to an engineer
Let's talk
Tell us about your model, your target, and your performance goals.