Large Language Model Acceleration
Optimized LLMs - Delivered Over Standard APIs
Deploy LLMs tuned for VectorPath AI cards with lower TCO vs. H100, low latency, high throughput, and API level simplicity. Your teams integrate via standard APIs — no FPGA expertise required.
TCO advantages are based on Llama 3.1 8B for interactive use cases. Results may vary.
Why run LLMs on VectorPath AI Cards?
VectorPath AI cards pair FPGA flexibility with LLM optimizations. The result is predictable, lower total cost of ownership, low-latency inference and efficient use of hardware resources-exposed through APIs your teams already understand.
Key Benefits for LLM Workloads
Lower Total Cost of Ownership
For many LLM workloads, FPGA-based inference can deliver favorable TCO compared to traditional GPU-only deployments — especially when utilization, power, and scaling behavior are considered holistically.
Low Latency for Interactive Use Cases
Optimizations for KV cache handling, batching strategies, and FPGA-friendly kernels help reduce time-to-first-token, enable high throughput and maintain smooth token streaming — critical for chatbots, copilots, and real-time assistants.
API Simplicity, Hardware Efficiency
Your teams call standard APIs. Under the hood, LLMs are compiled, optimized, and scheduled to run efficiently on VectorPath AI cards, so you benefit from the hardware without changing your development mod
Designed for Real LLM Workloads
Real‑Time Conversational AI
Serve customers with responsive, context-aware assistants that maintain low latency even under high concurrency, thanks to optimized LLM inference on VectorPath AI cards.
Analytics, Reporting, and Insights
Generate narratives, commentary, and explanations for dashboards or reports, backed by high-throughput LLM inference optimized for batch workloads.
Document Summarization and RAG
Combine retrieval-augmented generation with LLMs running on VectorPath AI cards to summarize, answer questions, and generate insights across large internal corpora.
AI-enhanced SaaS Features
Embed generation, rewriting, smart search, and recommendations into SaaS products while maintaining control over latency and serving costs.
Operational Copilots
Support operations, SRE, and incident response teams with assistants that can summarize alerts, logs, and documentation in real time.
Contact an AI Inference Specialist
Request access to the Achronix AI Console for performance evaluation or a tailored cost model for your LLM workloads
FAQs
Everything you need to know about LLMs optimized for VectorPath AI Cards.
Do I need FPGA expertise to use these LLMs?
No. The platform is designed so that teams interact only through standard APIs. FPGA-specific compilation, scheduling, and optimizations are handled underneath the API layer.
How are these LLMs different from GPU-hosted LLMs?
These models are profiled and tuned specifically for VectorPath AI Cards, with an emphasis on deterministic performance and efficient resource usage resulting in significant TCO advantages for many LLM workloads. The API experience, however, is kept familiar and comparable to other LLM providers.
Can I bring my own models?
Yes. We support architectures of all leading models. Support for unique LLM architectures depends on the compatibility and tooling available for compiling them to run efficiently on VectorPath AI Cards. In all cases, models based on unique architectures can be evaluated for support.
How is security and data privacy handled?
LLMs optimized for the VectorPath AI cards are deployed within your chosen environment (on-premises, colocation, or hybrid) so data remains in your controlled infrastructure. You can integrate with existing security, networking, and compliance controls.
What support options are available?
Support tiers can be tailored to your deployment model and criticality. Typical offerings include assistance with benchmarking, rollout planning, and ongoing performance tuning.
Can I monitor latency or throughput?
Yes. Integrations with logging and observability tools can expose metrics for requests, latency, tokens-per-second so engineers can operate the stack confidently.