RNGD Product Page
Powerfully efficient AI inference for Enterprise and Cloud
#1 EFFICIENT LLAMA INFERENCE
token/s/W
token/s/W
Llama 3.1 70B
2,048 input tokens / 128 output tokens / x 8 cards
FuriosaSDK / FP8 / 957.05 token/s
TensorRT-LLM 0.15.0 / FP8 / 2,064.53 token/s
TensorRT-LLM 0.15.0 / FP8 / 163.53 token/s
Llama 3.1 8B
128 input tokens / 4,096 output tokens / x 1 card
FuriosaSDK / FP8 / 3,935.25 token/s
TensorRT-LLM 0.15.0 / FP8 / 13,222.06 token/s
TensorRT-LLM 0.15.0 / FP8 / 2989.17 token/s
| RNGD | H100 SXM | L40S | |
|---|---|---|---|
| Technology | TSMC 5nm | TSMC 4nm | TSMC 5nm |
| BF16/FP8 (TFLOPS) | 256/512 | 989/1979 | 362/733 |
| INT8/INT4 (TOPS) | 512/1024 | 1979/- | 733/733 |
| Memory Capacity (GB) | 48 | 80 | 48 |
| Memory Bandwidth (TB/s) | 1.5 | 3.35 | 0.86 |
| Host I/F | Gen5 x16 | Gen5 x16 | Gen4 x16 |
| TDP (W) | 180 | 700 | 350 |
Disclaimer: Measurements by FuriosaAI internally on current specifications and/or internal engineering calculations. Nvidia results were retrieved from Nvidia website, https://github.com/NVIDIA/Tens..... /perf-overview.md, on Aug 25, 2024.
EFFICIENT AI INFERENCE IS HERE
RNGD delivers high-performance LLM and multimodal deployment capabilities while maintaining a radically efficient 180W power profile.
- 512 TFLOPS
- 64 TFLOPS (FP8) x 8 processing elements
- 48 GB
- HBM3 memory capacity
- 2 x HBM3
- CoWoS-S, 6.0 Gbps
- 256 MB SRAM
- 384 TB/s on-chip bandwidth
- 1.5 TB/s
- HBM3 memory bandwidth
- 180 W TDP
- Targeting air-cooled data centers
- PCIe P2P support for LLM BF16, FP8, INT8, INT4 support
- Multiple-Instance and Virtualization Secure boot & model encryption
Tensor contraction, not matmul
Tensor Contraction Processor (TCP)
At the heart of Furiosa RNGD is Tensor Contraction Processor architecture (ISCA 2024), specifically designed for efficient tensor contraction operations. The fundamental computation of modern day deep learning is tensor contraction, a higher dimensional generalization of matrix multiplication. However, most commercial deep learning accelerators today incorporate fixed-sized matmul instructions as primitives. RNGD breaks away from that, unlocking powerful performance and efficiency.
Advanced packaging technology
For optimal single-chip compute density, memory bandwidth, and energy efficiency.
Tensor Contraction Processor
TCP is the compute architecture underlying Furiosa accelerators. With tensor operation as the first-class citizen, Tensor Contraction Processor (TCP) unlocks unparalleled energy efficiency.
Tensor mapping for max utilization
We elevate the programming interface between hardware and software to treat tensor contraction as a single, unified operation. This fundamental design choice streamlines programming, maximizing parallelism and data reuse, while providing flexibility and reconfigurability of compute and maximizes memory resources based on tensor shapes. Furiosa Compiler leverages this flexibility and reconfigurability of hardware to select the most optimized tactics, delivering powerful and efficient deep learning acceleration for all scales of deployment.
SOFTWARE FOR LLM DEPLOYMENT
Furiosa SW Stack consists of a model compressor, serving framework, runtime, compiler, profiler, debugger, and a suite of APIs for ease of programming and deployment.
Available now.
Built for advanced inference deployment
Comprehensive software toolkit for optimizing large language models on RNGD. User-friendly APIs facilitate seamless state-of-the-art LLM deployment.
Maximizing Data Center Utilization
Ensure higher utilization and flexibility for small and large deployments with containerization, SR-IOV, Kubernetes, as well as other cloud native components.
Robust Ecosystem Support
Effortlessly deploy models from library to end-user with PyTorch 2.x integration. Leverage the vast advancements of open-source AI and seamlessly transition models into production.
