SambaNova SN50 (RDU 3rd Gen, 2026 Speculative)
:::warning Speculative Content
Specifications on this page are based on SambaNova Q4 2024 public statements, interviews with Kunle Olukotun's team, and roadmap synthesis. SambaNova has not yet released complete SN50 specifications. Official data is subject to the actual H2 2026 launch.
:::
Product Overview
SambaNova SN50 is SambaNova's third-generation Reconfigurable Dataflow Unit (RDU), expected to launch in H2 2026 (SN40L launched September 2024). Built on TSMC 3nm process, 256 GB HBM3E memory, Dataflow architecture (different from traditional GPU imperative architecture), 2× SN40L performance. Paired with the SambaFlow software stack (PyTorch / TensorFlow / JAX compatible).
SambaNova's strategic position: along with Groq, Tenstorrent, and Cerebras, it is known as one of the "Big Four US AI Chip Startups" (Groq LPU, Tenstorrent RISC-V, Cerebras WSE, SambaNova RDU). In a market dominated by NVIDIA CUDA, SambaNova is one of the few startups still maintaining enterprise-level commercialization (customers: PayPal, Astera Labs, Constellation, national laboratories).
Core Specifications (Speculative)
| Item | Spec |
|---|
| Architecture | SambaNova RDU 3rd Gen |
| Process | TSMC 3nm (N3) |
| HBM | 256 GB HBM3E (SN40L is 128 GB HBM3) |
| Memory Bandwidth | ~5.5 TB/s (SN40L 3.2 TB/s) |
| BF16 dense | 1.5 PFLOPS (SN40L 638 TF, 2.4×) |
| FP8 dense | 3 PFLOPS (speculative; SN40L does not support FP8) |
| INT8 | 6 POPS (speculative) |
| TDP | ~700 W |
| Form Factor | OAM / PCIe Gen5 |
| Interconnect | SambaNova Dataflow Link (proprietary) |
| Cluster | DataScale SN50 (multi-card fully connected) |
| Production | H2 2026 (speculative) |
| Unit Price | ~$40,000–50,000 (speculative) |
Comparison with SN40L
| Metric | SN50 (H2 2026 speculative) | SN40L (Sep 2024) | Improvement |
|---|
| Process | TSMC 3nm | TSMC 5nm | New generation |
| HBM | 256 GB HBM3E | 128 GB HBM3 | 2× |
| Bandwidth | 5.5 TB/s | 3.2 TB/s | 1.7× |
| BF16 dense | 1.5 PF | 638 TF | 2.4× |
| FP8 dense | 3 PF (speculative) | N/A | New |
| TDP | 700 W | 600 W | +17% |
| Cluster | DataScale 8/16/32 cards | DataScale 8/16 cards | 32 cards |
| Price (speculative) | ~$45K | ~$30K | +50% |
SambaNova Dataflow Architecture
| Dimension | Traditional GPU | SambaNova RDU |
|---|
| Execution Model | Imperative (instruction stream) | Dataflow (graph execution) |
| Parallelism | Thread-level (CUDA cores) | Operator-level (dataflow graph) |
| On-chip Cache | Shared L2 + registers | Large distributed SRAM (patented) |
| Data Locality | Limited by HBM | On-chip data movement (graph-optimized) |
| Compiler | CUDA / OpenCL | SambaFlow (specialized) |
| Advantage | General-purpose + flexible | Dataflow-optimized, low LLM inference latency |
| Disadvantage | — | Weak training ecosystem; PyTorch compatibility requires manual optimization |
Dataflow Execution
Traditional GPU:
for (i = 0; i < N; i++) {
y[i] = W * x[i]; // accesses HBM every iteration
}
RDU Dataflow:
Configure: graph W → operator → accumulator
Input x → triggers graph execution → output y
Advantage: only 1 HBM access (input) + 1 (output)
SambaFlow Software Stack
| Layer | Tool | Description |
|---|
| AI Framework | SambaFlow | PyTorch / TensorFlow / JAX compatible |
| SambaNova CoT | Compiler of Things (graph compiler) |
| Reference Models | LLaMA / Mistral / Qwen / SDXL pre-optimized |
| Compiler | CoT Compiler | Model → RDU binary |
| Runtime | SambaFlow Runtime | Multi-card orchestration |
| Enterprise | SambaNova Suite | Private cloud deployment + inference API |
| API | SambaNova API | OpenAI-compatible (partial) |
⚠️ Ecosystem limitations: Compared to CUDA's 18-year ecosystem, SambaFlow is only 5–6 years old, but SambaNova has done better at enterprise deployment than Cerebras/Groq (PayPal handles 1B+ transactions/day; Astera Labs semiconductor design verification).
Manufacturer Info
| Item | Detail |
|---|
| Company | SambaNova Systems |
| Founders | Kunle Olukotun (Stanford professor) + Christopher Ré + Rodrigo Liang |
| Founded | 2017 |
| HQ | Palo Alto, California, USA |
| Funding | $1.1B+ (Series D Q1 2021, led by SoftBank, Intel Capital) |
| Valuation (2025) | $5B+ (unicorn) |
| 2024 Revenue | ~$80M (fast-growing) |
| Employees | ~500 |
| Fab | TSMC 5nm → 3nm |
| Customers | PayPal, Astera Labs, Constellation, US National Labs |
| Status | Private (considering 2026–2027 IPO) |
SambaNova Product Line
| Product | Released | BF16 Compute | Memory | Status |
|---|
| SN10 | 2021 | 300 TF | 320 GB DDR4 | EOL |
| SN25 | Q3 2022 | 300 TF | 320 GB DDR4 | EOL |
| SN30 | Q2 2023 | 600 TF | 1.5 TB DDR4 | In production |
| SN40L | Sep 2024 | 638 TF | 128 GB HBM3 | Current flagship |
| SN50 | H2 2026 (speculative) | 1.5 PF | 256 GB HBM3E | Roadmap |
| SN60 (speculative) | 2027+ | ? | ? | Long-term roadmap |
Big Four US AI Chip Startups
| Company | Architecture | Flagship Product | Funding | Status |
|---|
| SambaNova | Dataflow RDU | SN40L / SN50 | $1.1B+ | Commercialization leader |
| Cerebras | Wafer-scale WSE | WSE-3 | $1.5B+ | 2026 IPO |
| Groq | LPU | LPU v2 / LPX | $1B+ | 2026 NVIDIA acquisition |
| Tenstorrent | RISC-V | Wormhole / Blackhole | $700M+ | Customer development stage |
Key Features
- Dataflow Architecture: graph execution, low LLM inference latency
- SambaFlow enterprise deployment: the only AI startup with successful enterprise commercialization (PayPal $40M+ contract)
- Large SRAM: SN40L 256 MB SRAM + HBM3 128 GB
- FP8 support: SN50 adds FP8 (catching up to NVIDIA Blackwell)
- Drawbacks: weak training ecosystem, high hardware cost
Use Cases
- ✅ Large enterprise LLM deployment (PayPal, Astera Labs)
- ✅ LLM inference (Dataflow-optimized latency)
- ✅ Semiconductor design verification (in production at Astera Labs)
- ✅ Government HPC (US National Labs)
- ✅ Private cloud deployment (on-premise, enterprise data security)
- ❌ Small companies (high cost, starting at $100K+)
- ❌ AI training focus (Dataflow training is weak)
- ❌ CUDA-proprietary workloads
SambaNova DataScale Rack
| Dimension | DataScale SN40L | DataScale SN50 (speculative) |
|---|
| RDUs | 8 / 16 | 8 / 16 / 32 |
| Total Compute | 5.1 PF / 10.2 PF | 12 PF / 24 PF / 48 PF |
| Total HBM | 1 TB / 2 TB | 2 TB / 4 TB / 8 TB |
| Total SRAM | 2 GB / 4 GB | 4 GB / 8 GB / 16 GB |
| Rack TDP | 4.8 kW / 9.6 kW | 5.6 kW / 11.2 kW / 22.4 kW |
| Rack Price | ~$300K / $600K | ~$400K / $800K / $1.6M |