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Positron began with FPGA-based Atlas systems and, by 2026, had reported major funding and deployments into Oracle cloud infrastructure. Its next step is Asimov, a planned custom AI ASIC. The commercial opportunity is real, but the strongest performance claims remain company-reported rather than independently validated.
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What Positron sells
Founded in April 2023 by former Groq engineers Thomas Sohmers and Edward Kmett, Positron is building inference infrastructure rather than a general-purpose computing platform. CEO Mitesh Agrawal now leads the company.
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Its product strategy has three parts:
- Atlas: an FPGA-based appliance designed for LLM inference.
- PCIe cards: accelerator cards that can be incorporated into customer systems, including a reported order for thousands of units.
- Asimov: a planned custom ASIC intended to improve the cost and power economics of a mature inference workload.
That distinction matters. Positron is not simply selling FPGAs. It is using reprogrammable hardware as a commercial bridge toward specialized silicon, while gathering customer and workload data before committing fully to an ASIC.
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In 2025 reporting, Positron said roughly 20 potential customers were evaluating Atlas, including cloud-service providers and enterprises. The same coverage described a multimillion-dollar Tier 2 cloud-provider order. Those figures indicate commercial interest, but evaluations and purchase orders are not the same as recurring revenue or broad production adoption.
In February 2026, the company announced a $230 million Series B and a reported post-money valuation above $1 billion. Positron said it had spent $38 million and had purchase orders exceeding that amount; those statements were company-attributed and were not presented as independently audited financial results. The funding was primarily intended to develop and deploy Asimov. EE Times reported on the financing and ASIC plans.
In April 2026, Agrawal told EE Times that Positron was deploying tens of millions of dollars’ worth of systems and racks into Oracle cloud infrastructure for inference, particularly mixture-of-experts workloads. The available report did not disclose the contract terms, system quantities, utilization, pricing or independently measured performance. The Oracle deployment was covered by EE Times.
Why focus on inference instead of training?
Training and inference place different demands on hardware.
Training repeatedly updates model parameters and typically relies on large batches of data, extensive parallel computation and high-bandwidth communication among accelerators. Nvidia has a broad position in this market through GPUs, networking, software libraries and mature frameworks.
Inference is the process of running a trained model to answer user requests. It may involve smaller batches, strict latency requirements and continual movement of model weights and user-specific context through memory.
For a language model, the relevant data is not only the model weights. The system also manages the key-value, or KV, cache that stores information about the conversation and current sequence. Long prompts, long outputs and many simultaneous users can make memory capacity and memory movement important constraints.
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Positron’s thesis is that some transformer inference workloads are limited less by theoretical arithmetic throughput than by how effectively the system moves data. A GPU may have far more peak floating-point capability than an FPGA, but peak FLOPS do not automatically translate into more generated tokens if the accelerator spends much of its time waiting for data.
Positron says its systems sustain approximately 93% of theoretical memory bandwidth across its use cases, compared with substantially lower utilization on GPU inference systems. That is a company claim and should not be generalized to every model, software release or serving pattern without independent testing.
How an FPGA could compete with a GPU
An FPGA is a programmable hardware fabric. Unlike a GPU, whose basic architecture is fixed at manufacture and exposed through a large software ecosystem, an FPGA can be configured around a particular dataflow after it is produced.
For an inference appliance, that can allow the vendor to:
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- Arrange computation around the model’s actual dataflow.
- Place logic close to memory interfaces.
- Reduce unnecessary movement between a host CPU, accelerator and memory.
- Use dedicated paths for predictable operations instead of relying on a general-purpose execution schedule.
- Optimize for sustained throughput, latency or energy efficiency rather than peak arithmetic capability.
But an FPGA is not automatically faster than a GPU. The result depends on the model architecture, numerical precision, tensor shapes, sequence length, batch size, memory layout, software implementation and the percentage of the workload that remains on the accelerator.
A low-batch, latency-sensitive transformer workload may suit a specialized dataflow design. A changing workload with unusual operators, frequent model updates or substantial preprocessing may favor Nvidia’s mature GPU and software ecosystem.
Inside the reported Atlas configuration
The 2025 Atlas description was a 4U appliance containing four Altera Agilex-7M FPGAs. Each FPGA was described as having 32 GB of HBM2e, four DDR5 channels and access to up to 512 GB of additional DDR5 across the system.
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Positron described the memories as separate resources rather than a conventional tiered cache:
- HBM: primarily for model weights.
- DDR: for user context, KV cache and model or LoRA swapping.
This separation reflects the different kinds of data involved in serving models. Weights may need high-bandwidth access during computation, while user context and cached sequences can require considerable capacity. The exact configuration should not automatically be assumed to describe every Atlas shipment in 2026.
Positron also reportedly sold PCIe cards directly. A card is not equivalent to a finished appliance: the buyer may still need suitable servers, memory, cooling, networking, software integration and operational support.
What “no recompilation” means
One of Positron’s central usability claims is that customers can load model binaries from Hugging Face or proprietary models without recompiling them. The company described this as a “zero-step” experience intended to resemble the appliance workflow familiar to Nvidia-based cloud operators.
If the claim works as broadly as it sounds, it could remove one of the biggest barriers to specialized hardware adoption. Customers would not have to become FPGA developers or rewrite every model for a new accelerator.
However, “no recompilation” does not necessarily mean that every model runs unchanged. A buyer should ask:
- Which model architectures and operators are supported?
- Are there restrictions on quantization, tensor shapes or sequence lengths?
- Do popular serving frameworks work directly?
- Does custom-model support require engineering assistance from Positron?
- Is the customer supplying an existing binary, or does Positron generate and validate a compatible binary?
- How quickly are new operators and model architectures supported?
The available reporting does not answer those implementation questions. They belong in technical due diligence rather than being inferred from the marketing description.
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Positron versus Nvidia
| Criterion | Positron Atlas | Nvidia platforms |
|---|---|---|
| Primary target | Dedicated LLM inference | Training, inference and general accelerated computing |
| Hardware | FPGA-based appliance or cards | GPU-based servers, cloud instances and appliances |
| Programmability | Reprogrammable hardware fabric with a vendor-specific software layer | Fixed GPU architecture with a broad programmable software stack |
| Peak compute | Lower than leading GPUs according to Positron’s own explanation | Typically much higher peak arithmetic throughput |
| Software maturity | Smaller, startup-specific ecosystem requiring verification | Extensive CUDA, framework and third-party support |
| Best fit | Predictable inference where efficiency and dedicated capacity matter | Mixed workloads, training, broad model support and flexible deployment |
What Positron claims
Positron reported that Atlas delivered 70% higher tokens-per-second performance than a comparable Nvidia Hopper-based system, along with 3.5 times better performance per watt and performance per dollar.
These are not independent benchmark conclusions in the available coverage. The comparison needs to be understood in the context of its exact:
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- Model and parameter count.
- Precision and quantization format.
- Prompt length, generation length and batch size.
- Latency target and percentile.
- Definition of tokens per second.
- Power-measurement boundary.
- Hardware price, support cost, utilization and depreciation assumptions.
- Software versions and whether preprocessing and networking were included.
Without those details, the figures are useful as a reason to investigate Positron, not as proof that Atlas is broadly faster or cheaper than Nvidia.
Where the economics could work
A dedicated appliance makes the most sense when utilization is high and the workload is stable. A cloud provider serving a predictable class of models can spread system costs across many customers. A large enterprise, colocation operator or financial-services company may also value consistent latency and power consumption enough to consider specialized hardware.
Potentially suitable workloads include:
- High-volume transformer inference.
- Mixture-of-experts serving with predictable traffic.
- On-premises or colocated deployments where power and rack capacity are constrained.
- Applications requiring consistent latency rather than occasional model experimentation.
- Cloud services that can expose a dedicated inference platform to multiple users.
The economics become less attractive when demand is bursty, model requirements change rapidly or the hardware sits idle. A theoretical performance-per-dollar advantage disappears if the system cannot maintain high utilization or if it requires substantial engineering and support costs.
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FPGAs help Positron move quickly and adapt hardware after manufacture. ASICs can offer better power and cost efficiency once the workload is sufficiently understood, but they are more expensive and less flexible to develop.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsPositron’s planned Asimov platform is described as using LPDDR rather than HBM. The company’s argument is that LPDDR can provide more memory capacity per dollar and cheaper packaging, even though it offers less bandwidth than HBM. Positron believes its memory-access intellectual property can compensate by keeping utilization high and that a purpose-built ASIC can control memory-refresh behavior more tightly than an FPGA implementation.
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This is a trade-off, not a universal claim that LPDDR is superior to HBM. A real design must balance:
- Capacity and bandwidth.
- Latency and access patterns.
- Power consumption.
- Packaging cost and complexity.
- Yield and manufacturing risk.
- Model size and serving concurrency.
Earlier roadmap reporting projected a future system at five times the performance of Nvidia Blackwell. That was a projection, not a demonstrated specification. The available reporting does not verify that Asimov had sampled, entered production or achieved that projected performance by August 2026.
Networking and scaling claims
Positron said the Agilex FPGAs provide three 400-Gbps networking transceivers per device and that its architecture could connect as many as 256 FPGAs point-to-point without additional switches.
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A high-speed link does not by itself demonstrate application-level throughput or production-scale reliability. A practical deployment also needs topology design, routing, synchronization, orchestration, cooling, monitoring and failure handling. Nvidia’s comparison must account for its wider networking ecosystem, including dedicated networking hardware and switches.
What the 2026 developments prove—and what they do not
The 2026 funding and Oracle reports make Positron’s story more substantial than a typical pre-product chip startup. They suggest that the company has progressed from evaluations toward larger commercial infrastructure deployments and has attracted enough investor interest to finance custom silicon.
They do not establish that Positron has defeated Nvidia, that Atlas wins across AI workloads or that Asimov is already a shipping product. They also do not disclose enough information to calculate a buyer’s actual total cost of ownership.
The commercial offering may take several forms: a complete appliance, a PCIe card, hosted capacity or a reserved infrastructure commitment. These options have very different economics. Public reporting available for this article did not provide standard Positron list pricing, support costs, warranty terms or a generally available Oracle price for Positron-backed capacity.
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- Benchmark selection: A system can win on one model, precision, sequence length or batch size and lose on another.
- Software compatibility: “No recompilation” may still involve supported-operator, quantization or shape restrictions.
- Utilization: Efficiency claims may depend on sustained, high-volume workloads.
- Roadmap execution: The strongest future-performance claims concern planned systems or Asimov rather than independently tested shipping hardware.
- Supply: The 2025 report said relevant Agilex parts were not yet generally available and that Positron was the only authorized shipper at that time. That was a historical statement and should not be treated as current availability in 2026.
- Vendor concentration: A customer may exchange dependence on Nvidia for dependence on Positron, Altera, a particular foundry or Positron’s software stack.
- ASIC risk: Custom silicon brings nonrecurring engineering costs, verification and yield risks, packaging constraints and reduced flexibility.
- Model drift: An architecture optimized for current transformer serving may lose its advantage as model structures and serving methods change.
- Commercial verification: Purchase orders and reported deployments are meaningful signals, but they are not equivalent to audited revenue, public references or independent performance tests.
Buyer’s checklist
A cloud provider, financial firm or enterprise considering Positron should request:
- A supported-model and supported-operator matrix.
- Results for its own models, including p50 and p99 latency.
- Prompt and output lengths, batch sizes, precision and concurrency used in benchmarks.
- The complete power-measurement boundary.
- All-in hardware, software, support, installation and maintenance costs.
- The minimum utilization required to meet the promised economics.
- Support SLAs, replacement policies and warranty terms.
- The process for updating models without vendor intervention.
- A contingency plan if the Asimov roadmap slips.
- Any migration path from Atlas to Asimov.
- References from customers running comparable production workloads.
Bottom line
Positron’s strongest case is not that FPGAs are better than GPUs. It is that a specialized, appliance-oriented system may serve selected inference workloads more efficiently than a general-purpose GPU platform.
The company has reported meaningful commercial progress: a $230 million Series B, purchase orders exceeding its reported spending, and systems and racks being deployed into Oracle cloud infrastructure. But the performance and cost advantages remain dependent on workload conditions and company-reported comparisons. Until buyers can verify independent benchmarks, pricing, availability, software coverage and production references, Positron should be treated as a promising specialized alternative to evaluate—not as a proven replacement for Nvidia.
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