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SN40L is no longer SambaNova’s newest processor. The company introduced its fifth-generation SN50 in February 2026 and now emphasizes inference products such as SambaStack, SambaCloud and SambaRack. SN40L nevertheless explains the architectural strategy behind the company’s attempt to offer an alternative to GPU-centered AI infrastructure.
What SambaNova announced
The September 19, 2023 announcement combined the SN40L RDU with SambaNova Suite, which SambaNova described as a full-stack platform for training, inference, enterprise model customization, multimodal applications and long-context workloads. The company said the chip was manufactured by TSMC and designed to improve speed, model capacity, quality, deployment simplicity and total cost of ownership.
Launch claims included support for up to 5 trillion parameters and 256K-plus sequence lengths on a single system node. These figures describe system-level capabilities involving distributed memory and model placement; they should not be read as a promise that all parameters of a dense model are simultaneously computed at maximum throughput. SambaNova’s announcement is the primary source for those specifications.
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- NVIDIA Volta GV100 Architecture — 4,608 CUDA Cores, 640 1st-Gen Tensor Cores delivering 14 TFLOPS FP32 and 112 TFLOPS deep learning performance for AI training, inference, HPC, and scientific computing workloads
- 32GB HBM2 ECC Memory — 900 GB/s Bandwidth — High-bandwidth memory on a 4096-bit bus with ECC error correction provides the memory capacity and throughput required for the largest AI models, simulations, and datasets
- PCIe 3.0 x16 Interface — 250W TDP — Standard PCIe Gen3 connectivity with passive cooling designed for enterprise rack server deployment in HPE ProLiant, Dell PowerEdge, and Supermicro platforms with adequate chassis airflow
- NVLink — Scale to 96GB Unified Memory — Connect two V100 GPUs via NVLink at 300 GB/s bi-directional bandwidth to scale GPU memory from 32GB to 96GB for larger AI training and HPC workloads
- Multi-Precision Computing — Supports FP64 (7 TFLOPS), FP32 (14 TFLOPS), FP16 (112 TFLOPS) and INT8 precision modes for flexible deployment across training, inference, and scientific simulation workloads
What an RDU does differently from a GPU
RDU means Reconfigurable Dataflow Unit. A conventional GPU launches many parallel kernels and repeatedly moves weights, activations and intermediate results through memory. SambaNova instead maps a model’s computation graph onto a reconfigurable dataflow fabric. Operations can be arranged as a pipeline so results flow directly from one operation to the next, potentially reducing repeated memory traffic.
That is an execution model, not a universal performance guarantee. Results depend on the model graph, compiler support, precision, sparsity, sequence length, batch size, concurrency and the comparison system. SambaNova explains the product architecture on its RDU product page; the technical design is also described in its SN40L paper.
Why memory is central to the SN40L
For large-model serving, arithmetic is only part of the problem. Moving model data between compute units and memory can dominate latency, energy use and the time needed to load or switch models. SN40L uses three tiers:
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- High-Performance AI Processing: The MX3 is designed to handle the most demanding AI computer vision workloads, delivering exceptional performance and efficiency.
- Flexible Integration: The MX3 can be easily integrated into your existing systems via its M.2 M-key form factor and support for Linux operating systems.
- Energy Efficient: The MX3 is designed to provide high performance while minimizing power consumption.
- Comprehensive Software Development Kit (SDK): The MX3 is supported by a comprehensive SDK that simplifies development and deployment.
- Hardware compatability: The MX3 is compatible with the PCI-SIG M.2 M-key 2280 Specification. It can be used with the Raspberry Pi 5 with a M-key 2280 HAT.
- On-chip SRAM: very fast storage close to the dataflow fabric.
- HBM: high-bandwidth memory for active model data.
- DDR DRAM: larger-capacity off-package memory for models, expert modules and other data.
The combination is intended to keep more weights and model variants available, support long contexts and reduce reloads between requests. A SambaNova-authored technical paper describes distributed SRAM, on-package HBM and off-package DDR DRAM. Commercial material says a node can address terabytes of memory and support up to 5 trillion parameters, but that is a capacity statement. Whether a model is dense or sparse, sharded, expert-based or actively computing all of its parameters determines actual throughput.
What “full-stack AI platform” meant
SambaNova was not presenting SN40L as a retail PCIe card to be dropped into any server. The proposition covered the accelerator, multi-RDU systems, compiler and software tools, model optimization, serving services, enterprise management and cloud or on-premises deployment.
Later products make the layers easier to distinguish:
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- ✅Powered by 26 Tera-Operations Per Second (TOPS) Hailo-8 AI Processor. 2.5W typical power consumption
- ✅Scalable, enabling simultaneous processing of multi-streams & multi-models
- ✅Enabling real-time, low latency and high-efficiency AI inferencing on the edge devices
- ✅Supports TensorFlow, TensorFlow Lite, ONNX, Keras, Pytorch frameworks
- ✅Supports Linux and Windows. Supports the temperature range of -40°C to 85°C
- SambaCloud: hosted access to SambaNova-powered models and APIs.
- SambaStack: dedicated hardware and software packaged as a turnkey enterprise inference platform.
- SambaManaged and hosted options: deployment models intended to reduce day-to-day infrastructure operations.
Integration can reduce the work of assembling accelerators, drivers, kernels and serving software, but it does not eliminate enterprise requirements. Customers still need networking, storage, identity and access management, monitoring, security integration, capacity planning and operational staff.
Which enterprise problems the design targets
| Problem | SN40L/SambaNova response |
|---|---|
| Large model capacity | SRAM, HBM and DDR memory tiers keep more model data available. |
| Repeated memory traffic | Dataflow pipelines can pass intermediate results directly between operations. |
| Frequent model or expert switching | Larger memory capacity can keep multiple models or expert modules resident. |
| Long-context inference | Additional capacity helps hold weights and context-related data, subject to workload limits. |
| Infrastructure integration | Hardware, compiler, model tooling and deployment services are sold as one stack. |
These mechanisms address bottlenecks; they do not guarantee lower cost or higher throughput for every application.
What the performance evidence actually shows
A 2024 paper by SambaNova researchers describes a Composition-of-Experts system with 150 experts and approximately one trillion total parameters running on an eight-socket RDU deployment. For the tested workloads, it reports 2× to 13× speedups against an unfused baseline, up to 19× lower machine footprint, 15× to 31× faster model switching, and aggregate speedups of 3.7× over a DGX H100 and 6.6× over a DGX A100. The paper is available through arXiv and has an IEEE record.
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Those are valuable primary technical results, but they are not independent competitive testing. They cover selected Composition-of-Experts workloads, specific baselines and stated software conditions. A buyer should request the exact checkpoint, precision, quantization, context length, batch size, concurrency, input/output token mix, time-to-first-token, inter-token latency, power boundary and total system cost before generalizing the figures.
SN40L versus a conventional GPU platform
| Category | SN40L/RDU approach | Conventional GPU approach |
|---|---|---|
| Design emphasis | Model dataflow and integrated serving | Broad parallel compute through kernels |
| Memory strategy | SRAM, HBM and DDR tiers | Typically HBM plus host or system memory |
| Software | SambaNova compiler and integrated stack | CUDA and a broad framework, library and tooling ecosystem |
| Flexibility | Strongest on supported SambaNova paths | Broad support for custom kernels and third-party software |
| Procurement | Integrated systems, cloud or dedicated services | Chips, servers, cloud instances and software from multiple suppliers |
GPU platforms remain the safer default for arbitrary scientific computing, graphics, highly customized CUDA kernels and applications that depend on a large third-party ecosystem. SambaNova may be more attractive when high-throughput inference, long contexts, frequent model switching and an integrated private deployment matter more than maximum software portability.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Deployment realities and trade-offs
SambaStack documentation identifies customer-managed dependencies such as authentication or OIDC, DNS and NTP. On-premises buyers must also provide suitable power, cooling, networking, storage and operational support. More memory does not automatically produce more tokens per second: compute capacity, interconnects, compiler scheduling and model structure still matter.
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- Professional AI & Creator Workstation: AMD Radeon AI PRO R9700 GPU with 32GB GDDR6 is engineered for AI development, professional content creation, and compute-intensive workloads.
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- Enterprise-Grade Thermal Solution: Vapor chamber heatsink with industrial Honeywell PTM7950 thermal interface material ensures reliable cooling under sustained professional loads.
Public pricing for SN40L or SambaStack was not stated in the reviewed official material; the current product page directs prospects to Talk to an Expert. Enterprise quotes are likely to vary with model support, throughput, deployment mode, support and capacity. Be cautious with power language: kW measures power, while kWh measures energy.
How SambaNova’s product story changed
- September 19, 2023: SN40L and SambaNova Suite were announced.
- May 13, 2024: the SambaNova-authored Composition-of-Experts paper was published.
- 2025: SambaStack was positioned as a turnkey enterprise inference platform using SN40L hardware.
- February 24, 2026: SambaNova announced SN50, described as its fifth-generation chip, alongside an Intel collaboration, SoftBank deployment and more than $350 million in financing.
The company’s current positioning is increasingly inference- and agent-focused. Its SN50 announcement makes clear that SN40L is an earlier generation, while the current SambaNova portfolio includes SambaStack, SambaCloud, SambaRack and SambaOrchestrator.
Questions enterprise buyers should ask
- Does the target model, fine-tuning workflow and serving API have a supported SambaNova path?
- Is the workload primarily prefill, decode, long-context or agentic?
- What throughput and latency are delivered at the required concurrency and service-level objective?
- Are benchmark results independently reproduced, or are they vendor-authored?
- What hardware, software, networking, storage, monitoring and support are included in the quote?
- Can the organization operate the required identity, DNS, NTP and security integrations?
- How portable are models and applications if the hardware or cloud provider changes?
- What are the power, cooling, rack and data-center requirements?
Bottom line
SN40L’s importance is less about one headline speed number than about its systems approach: dataflow execution, a three-tier memory hierarchy, compiler technology and deployment services designed together. The architecture is credible for memory-intensive, model-switching and large-scale inference workloads, and SambaNova’s paper provides workload-specific evidence of substantial gains. It is not proof that SN40L universally beats GPUs, nor is it the company’s current flagship. In 2026, evaluate it as the foundation of SambaNova’s full-stack strategy while treating SN50 and inference-focused products as the current commercial context.
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