QuantX helps AI hardware teams evaluate MXFP, BFP, NVFP, and custom quantization formats against real LLM and VLM workloads. Make data-driven numeric format decisions, accelerate validation, and reduce pre-silicon risk.
Hardware-Aware Quantization Platform for AI Inference Silicon
Inference IP Architects · Numeric Format Exploration · Hardware Validation
🚀 Request Evaluation · 🌐 Website · 📬 Contact
QuantX is a hardware-aware quantization platform built specifically for teams designing AI inference silicon.
Most quantization frameworks help machine learning engineers deploy models onto existing hardware. QuantX addresses a fundamentally different problem:
Which numeric format should be implemented in silicon before tape-out?
QuantX enables architecture teams to evaluate custom numeric formats against production-scale Large Language Models (LLMs) and Vision Language Models (VLMs), measure accuracy degradation before hardware implementation, and establish a software golden reference for RTL and silicon validation.
Built by 10xEngineers for AI accelerator companies, RISC-V processor vendors, inference IP teams, and custom AI hardware startups.
| Target Users | AI Inference IP Architects |
| Primary Use Case | Numeric Format Design-Space Exploration |
| Model Range | 1B – 14B Parameters |
| Supported Formats | MXFP · BFP · NVFP · AMXFP · FP16+INT |
| Optimization Engine | Automated Multi-Level Bit Allocation |
| Validation Flow | Golden Reference for RTL & Silicon |
| Deployment | Existing SDK Integration or Custom SDK Development |
| Roadmap Growth | ~1 New Model Added Per Month |
Numeric Format Choice
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RTL Development
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Verification
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Tape-Out
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Run Real LLMs/VLMs
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Discover Accuracy Loss
❌ Too Late To Change Numeric Format
Select Numeric Format
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QuantX Simulation
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Run Real LLMs & VLMs
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Evaluate Accuracy / Memory / Compression
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Compare Candidate Formats
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Make Silicon Decision With Data
✅ Before RTL Freeze
HuggingFace Models
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Calibration Dataset
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Meta Optimization Engine
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Feature Selection
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Quantization Engine (RTN / GPTQ / GPTAQ)
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Evaluation Layer (LM-Eval / LMMS-Eval)
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Benchmark & Validation Reports
Evaluate numeric formats before hardware implementation.
- MXFP exploration
- BFP exploration
- NVFP exploration
- AMXFP exploration
- Proprietary custom formats
- Accuracy vs memory tradeoff analysis
- Pareto frontier generation
Use QuantX as a software golden reference model.
- RTL verification
- Datapath validation
- Silicon bring-up
- Numerical accuracy closure
- Regression testing
When RTL output diverges from QuantX output, engineers immediately know where to investigate.
Deploy QuantX-compressed models into production environments.
- Existing inference SDK integration
- Custom runtime development
- Compiler stack integration
- Hardware-specific deployment optimization
| Format | Description |
|---|---|
| FP16 + INT | FP scaling with integer elements |
| MXFP | OCP Microscaling Format |
| BFP | Block Floating Point |
| NVFP | FP8 scale with FP4 elements |
| AMXFP | Dual-scale Microscaling Format |
Additional formats can be added through QuantX's modular architecture.
Rounding Methods
- RTN (Round-to-Nearest)
- GPTQ
- GPTAQ
Outlier Reduction
- Scaling
- Clipping
- Rotation
- Reordering
Structural Optimization
- Mixed Precision
- Tensor Granularity Control
- Per-Channel Quantization
- Per-Group Quantization
- CodeBook Quantization
The QuantX Meta-Optimization Engine automates what traditionally requires weeks of manual experimentation.
Scores transformer blocks based on:
- Cosine similarity
- Token similarity
- Span score
- Z-score analysis
- Domain expertise priors
High-importance blocks receive more precision.
Independent scoring of:
- Query projections
- Key projections
- Value projections
- Output projections
- FFN Up
- FFN Gate
- FFN Down
Automatically activates compatible algorithms depending on:
- Numeric format
- Model architecture
- Memory constraints
- Hardware limitations
Language Models
| Llama Family | Qwen Family |
|---|---|
| Llama 2 | Qwen 2 |
| Llama 3.1 | Qwen 2.5 |
| Llama 3.2 | Qwen 3 |
Vision Language Models
- Qwen 3 VL
- Llava-Next 1.6 7B
- SmolVLM
- SmolVLM2
Generative & Legacy Models
- CLIP
- OPT
- Stable Diffusion 1.5
- Stable Diffusion 3.5
- Stable Diffusion XL
| Model | Format | Result |
|---|---|---|
| Llama 3.2 1B | INT + FP5 | 8% accuracy-gap recovery vs RTN |
| Llama 3.1 8B | BFP | Surpasses baseline GSM8K score |
| Qwen 3 VL 2B | INT + FP5 | 4% accuracy-gap recovery |
| Qwen 2.5 VL 7B | BFP | Near-baseline ChartQA accuracy |
Across all evaluated configurations, QuantX consistently outperforms vanilla RTN quantization.
QuantX integrates directly with Baltoro, 10xEngineers' AI compiler stack.
Pretrained Model
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QuantX
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Compressed Model
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Baltoro
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Optimized Machine Code
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Custom AI Hardware
Together they provide a complete path from model compression to deployment on custom silicon.
| Traditional Challenge | QuantX Solution |
|---|---|
| Numeric format selected using intuition | Data-driven format exploration |
| Accuracy validated after hardware exists | Accuracy validated before tape-out |
| Weeks of manual quantization sweeps | Automated optimization engine |
| No software golden reference | Hardware validation workflow |
| Limited support for custom formats | Native MXFP, BFP, NVFP, AMXFP support |
- Schedule a technical discussion with the QuantX team
- Share your target hardware architecture
- Define candidate numeric formats
- Run QuantX exploration on representative workloads
- Review accuracy, memory, and implementation tradeoffs
- Select the optimal format before silicon commitment
| Website | https://10xengineers.ai |
| Request Evaluation | https://10xengineers.ai/contact-us |
| contact@10xengineers.ai | |
| Location | San Diego, CA, USA |
| https://linkedin.com/company/10x-engineers | |
| YouTube | https://youtube.com/@10xengineers |
| Product | Description |
|---|---|
| Baltoro | MLIR-based AI compiler stack |
| Compiler & Toolchain Services | Custom compiler development |
| AI Infrastructure Software | End-to-end inference software |
| RISC-V AI Solutions | Hardware-software co-design services |
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