Gimlet Labs announced an $80 million Series A on March 23, 2026, led by Menlo Ventures. That is no longer its latest financing: the company announced a $300 million Series B on September 4, 2026, led by Andreessen Horowitz. Gimlet is an enterprise-focused AI inference infrastructure company, not a consumer software or hardware brand.
What Gimlet Labs raised in its Series A
Gimlet Labs announced the $80 million Series A on March 23, 2026. Menlo Ventures led the round; Eclipse, Factory, Prosperity7, and Triatomic also participated. The announcement identifies the financing as a Series A, but does not disclose a valuation or detail how the funds will be used. Gimlet’s Series A announcement
The Series A was followed by a larger round
On September 4, 2026, Gimlet announced a $300 million Series B led by Andreessen Horowitz. That later financing makes the March Series A a historical milestone rather than the company’s latest funding round. Gimlet’s Series B announcement
What Gimlet Labs sells
Gimlet describes its product as an inference cloud designed for agentic workloads. Inference is the process of running a trained AI model to generate outputs, such as responses or actions. Gimlet says its software coordinates stages of AI workloads across different types of computing hardware and connects accelerators over high-speed networks.
Recommended Free Tools
#1 Best Overall
- 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
The company currently presents the offering as a managed inference API, while also describing deployment in a customer’s own data center. Its stated audience is frontier AI labs and businesses with large-scale inference needs. TechCrunch’s March 2026 coverage similarly described software for running workloads across varied hardware, including CPUs, GPUs, and high-memory systems, and said the product was not aimed at rank-and-file AI application developers. That report reflects the company’s positioning at the time, not a guarantee that its target market will never change. TechCrunch’s March 2026 coverage
What Gimlet has disclosed about customers and performance
In its Series A announcement, Gimlet said its customer base had tripled since launch and included a top frontier lab and a hyperscaler. It did not name those customers. These are company-reported claims, not independently verified customer figures.
Rank #2
- 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.
The company’s funding announcements also make broad claims about growth in agent workloads. The materials cited here do not independently establish those market-growth assertions or provide a benchmark that would support a general claim that Gimlet is faster or more efficient than alternatives. A buyer would need workload-specific evidence, including the model, hardware, comparison conditions, latency, throughput, and cost, to judge those claims.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the funding news means for buyers
The funding announcements show that Gimlet has raised substantial capital and that investors have backed its effort to serve large-scale inference workloads. They do not, by themselves, establish product performance, customer satisfaction, or savings for a particular business.
Rank #3
- ✅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
Organizations evaluating the platform should compare the managed API with the data-center deployment option, then test their own workload and model types across relevant hardware. Latency, throughput, hardware compatibility, total cost, and power use are practical evaluation criteria; the cited company materials do not provide independently verified comparative results for them.
Quick Recap
Best Value
- 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.
- Massive 32GB Memory Capacity: 32GB of GDDR6 memory on a 256-bit bus provides ample bandwidth for large AI models, 8K video editing, and complex 3D rendering.
- Advanced RDNA 4 with AI Accelerators: 64 Compute Units with 3rd Gen Ray Tracing and dedicated 2nd Gen AI Accelerators for groundbreaking AI performance and visual computing.
- Professional Blower Cooling: Efficient single blower design exhausts heat directly out of the chassis, ideal for multi-GPU workstation and server configurations.
- Enterprise-Grade Thermal Solution: Vapor chamber heatsink with industrial Honeywell PTM7950 thermal interface material ensures reliable cooling under sustained professional loads.
Rank #4
- 48GB AI graphics accelerator
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
