FuriosaAI is live · custom pricing · last verified 1 Nov 2026 · viability healthy
FuriosaAI
FuriosaAI's RNGD inference accelerators and NXT RNGD Server serve LLMs in 3 kW air-cooled racks via OpenAI-compatible APIs.
FuriosaAI is the rare non-Nvidia accelerator with consumer-scale production proof — Daum News, Equinix Lisbon, a 15 MW Sweden site — and a $620M Series D behind it, so this is not a science project. The 3 kW NXT RNGD Server and OpenAI-compatible furiosa-llm mean you can benchmark throughput, latency and wall power without rewriting client code. Budget a real porting effort for models outside the Model Zoo, and treat the CUDA comparison as an ecosystem question, not a hardware one.
Verdict generatedVerified 4d ago · liveness 76/100 · cite: rightaichoice.com/tools/furiosaai
- Enterprises with air-cooled racks capped near 15 kW that need more LLM throughput per rack
- Teams serving LLM, multimodal or agentic inference where power and TCO dominate the decision
- Organizations with platform engineers able to port models onto a non-CUDA compiler stack
- Cloud and colocation operators packaging multi-tenant RNGD capacity with SR-IOV and Kubernetes
- Teams requiring CUDA ecosystem compatibility or a drop-in GPU swap for arbitrary workloads
- Training, fine-tuning or pre-training projects — the RNGD line targets inference
- Product teams with no platform engineering capacity for toolchain integration
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Skip FuriosaAI if you need a drop-in CUDA-compatible GPU replacement or you are training and fine-tuning rather than serving inference.
A non-CUDA stack means engineering time: models outside the Model Zoo become a porting project measured in weeks of platform-engineer hours, not a config change
FuriosaAI is an enterprise hardware buy, so the comparison that matters is tokens per rack-kilowatt against Nvidia GPU nodes and other inference accelerators, not a monthly subscription line. If your colocation bill is power-dominated and your floor is air-cooled, the 3 kW NXT RNGD Server can change the denominator. If you need liquid-cooled density or CUDA ubiquity, general-purpose GPU vendors stay cheaper overall once integration and porting effort are counted.
In short
FuriosaAI — FuriosaAI's RNGD inference accelerators and NXT RNGD Server serve LLMs in 3 kW air-cooled racks via OpenAI-compatible APIs. Best for Enterprises with air-cooled racks capped near 15 kW that need more LLM throughput per rack, Teams serving LLM, multimodal or agentic inference where power and TCO dominate the decision, Organizations with platform engineers able to port models onto a non-CUDA compiler stack. Pricing is on request (custom quotes).
No head-to-head comparison for FuriosaAI yet — the alternatives below are the closest matches we list.
What is FuriosaAI?
FuriosaAI — FuriosaAI's RNGD inference accelerators and NXT RNGD Server serve LLMs in 3 kW air-cooled racks via OpenAI-compatible APIs. Best for Enterprises with air-cooled racks capped near 15 kW that need more LLM throughput per rack, Teams serving LLM, multimodal or agentic inference where power and TCO dominate the decision, Organizations with platform engineers able to port models onto a non-CUDA compiler stack. Pricing is on request (custom quotes).
FuriosaAI builds inference silicon for teams whose constraint is kilowatts per rack rather than headline FLOPS. The Furiosa RNGD is a 180W PCIe accelerator, and eight of them make up the NXT RNGD Server — 4 petaFLOPS, 384 GB HBM3, 12 TB/s memory bandwidth, all inside a 3 kW power envelope that fits ordinary air-cooled data center halls without liquid cooling. The underlying Tensor Contraction Processor architecture was published at ISCA 2024 and handles tensor contraction natively instead of fixed-size matmul primitives. The software layer is what most evaluators will actually touch. furiosa-llm installs with pip and serves pre-compiled models behind an OpenAI-compatible API on port 8000, so existing SDK clients just point at a new base_url; there is also a Python LLM class for direct prompt generation. furiosa-torch registers RNGD as a PyTorch device with eager execution for development, torch.compile for throughput, and ahead-of-time compilation for deployment. A pre-validated Model Zoo spans gpt-oss 20B and 120B, Llama 3.3 70B, Qwen3 variants including MoE 30B-A3B and Qwen3-VL, Qwen3-Coder, EXAONE 4.5 33B, Solar Open 100B, DeepSeek-R1-Distill up to 70B, and BGE-M3 embedding and reranker models. Production references are unusually concrete for a non-Nvidia vendor: AI-generated overviews for millions of Daum News users, a deployment in Equinix's Lisbon data center, a 15 MW AI data center project in Sweden with I/ONX and Velox, Korea's first domestic NPU-as-a-Service with Samsung SDS, and a Singapore hub opened in September 2026 to drive APAC rollout. FuriosaAI also closed a $620 million Series D and announced a Broadcom partnership on next-gen inference for agentic AI. Furiosa Access is the low-risk entry point: bare-metal NXT RNGD Server time measured at the wall, or a hosted OpenAI-compatible endpoint with nothing to install, available worldwide. Positioned against Nvidia GPUs, the honest framing is that RNGD competes on power efficiency and rack density
Key features
- Furiosa RNGD PCIe card: 180W inference accelerator for LLM and multimodality workloads
- Furiosa NXT RNGD Server: 8x RNGD cards, 4 petaFLOPS, 384 GB HBM3, 12 TB/s bandwidth
- 3 kW power envelope runs in standard air-cooled racks with no liquid cooling
- Claimed up to 5x more servers per rack versus 7.5 kW server alternatives
- Tensor Contraction Processor architecture with native tensor contraction (ISCA 2024)
- furiosa-llm: OpenAI-compatible inference server on port 8000, installed via pip
- Python API via furiosa_llm LLM class for batch prompt generation
- furiosa-torch: RNGD as a PyTorch device with eager, torch.compile and AOT paths
- Model Zoo with 32 pre-validated entries across text, vision, code, embedding and reranking
- Model coverage includes gpt-oss 20B/120B, Llama 3.3 70B, Qwen3-VL, EXAONE 4.5 33B, Solar Open 100B
- Hybrid batching and prefix caching for LLM serving throughput
- Native Kubernetes support and containerized deployment
- SR-IOV virtualization for multi-tenant RNGD capacity
- Furiosa Access evaluation: bare-metal NXT RNGD Server time or OpenAI-compatible API endpoint
- TCL and vISA kernel-programming layers announced for Q4 2026
Behind the verdict
Power is the axis FuriosaAI chose to compete on, and it's the right one for a lot of buyers. A 3 kW appliance holding eight 180W RNGD cards fits air-cooled halls that were never designed for the 7.5 kW boxes some GPU alternatives ship in — FuriosaAI claims up to 5x more servers per rack, and while you should verify that against your own floor plan, the arithmetic checks out at the rack level. We'd reach for this when inference is the whole workload and the constraint is kilowatts or rack slots. Agentic serving, multimodal pipelines, embedding and reranking stages all have pre-validated models in the Zoo, and the OpenAI-compatible server cuts migration to a base_url change. The Daum News deployment is the signal that matters most — AI-generated overviews at consumer scale is exactly the serving pattern this hardware is aimed at. Pass if you need CUDA compatibility for arbitrary workloads, or if your team has no platform engineer to spend on toolchain integration. The Model Zoo is generous but not infinite; anything outside it becomes a porting project through furiosa-torch or, later, the TCL and vISA kernel layers slated for Q4 2026. Training and fine-tuning aren't the RNGD line's job either. The closest alternative in practice is not another NPU — it's whatever GPU generation your colo already supports. GPUs win on ecosystem depth and library coverage; RNGD wins on watts per token and on the fact that you don't need to re-engineer cooling. If your rack budget caps near 15 kW, that difference is the entire decision. One caveat worth stating plainly: production references in Korea and Europe are strong, but the developer community around this stack is small compared to CUDA. Expect fewer Stack Overflow answers and more reliance on Furiosa's own docs and forums.
Models under the hood
as of 2026-10-31
Limitations
- FuriosaAI sells inference hardware — the RNGD PCIe card and the multi-card NXT RNGD Server — plus the furiosa-llm and furiosa-torch software stack to run models on it.
- Access runs through hardware procurement or the Furiosa Access evaluation programme (bare-metal NXT RNGD Server time or an OpenAI-compatible API endpoint); you request access rather than installing it yourself.
- Integration is via the furiosa-llm OpenAI-compatible server on port 8000 or the Python furiosa_llm API.
- Models outside the Model Zoo or outside clean PyTorch become a porting project, and the TCL and vISA kernel-programming layers that would help with that are documented as coming Q4 2026.
- Model coverage and compiler maturity should be verified per-checkpoint during evaluation; the Model Zoo is the authoritative list.
as of 2026-10-23
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Is FuriosaAI still active?
FuriosaAI is live · custom pricing · last verified 1 Nov 2026 · viability healthy.
Viability score
How well maintained and how widely used is FuriosaAI? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: November 2026
How we score →What's new in FuriosaAI
Checked yesterdayAcross the latest 4 updates: 1 changelog entry and 3 news mentions.
FuriosaAI closes $620 million Series D to scale efficient, production-ready AI inference
FuriosaAI announced it closed a $620 million Series D round to scale production-ready AI inference.
Furiosa SDK 2026.4: Enhanced MoE performance, hierarchical caching, expanded model coverage
Furiosa SDK 2026.4 adds enhanced MoE performance, hierarchical caching and broader model coverage.
FuriosaAI establishes Singapore hub to drive APAC expansion and accelerate global RNGD deployment
FuriosaAI opened a Singapore hub targeting APAC expansion and faster global RNGD deployments.
White Paper: Benchmarking RNGD on Backend.AI for 1.3–1.5x greater efficiency in enterprise AI inference
FuriosaAI published a white paper benchmarking RNGD on Backend.AI, reporting 1.3–1.5x greater efficiency in enterprise AI inference.
What do users say about FuriosaAI?
What people actually say about FuriosaAI — is it worth it?
We scanned public community sources for FuriosaAI on Oct 9, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Only 0 of the posts we fetched could be positively tied to FuriosaAI. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
How we verified this
Every listed fact about FuriosaAI — pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 1 Nov 2026. Analysis is drafted with AI models and checked against our editorial standards before publishing; see how we verify.
Verification history
We have re-verified FuriosaAI 22 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — changed our verdict, who it suits, who should skip it, description, features, pricing tiers, summary
- — changed pricing page
- — changed SSL certificate
- — changed limitations, who should skip it, summary, our verdict, features, setup time, hidden costs, who it suits, pricing fit, use cases, switching in, switching out, description, pricing page
- — changed who it suits, our verdict, features, description, who should skip it, summary
- — changed limitations, pricing page
Showing the 6 most recent of 22 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does FuriosaAI cost in 2026?
FuriosaAI: Pricing is on request (custom quotes). Pricing last verified 1 Nov 2026.
Plans compared
Every published FuriosaAI tier, its price as the vendor publishes it, who it fits, and what it adds over the tier below. The 12-month cost projection follows.
| Plan | Price | Billing | Who it's for |
|---|---|---|---|
| Enterprise | Custom | quote | Enterprises, colocation operators and sovereign-AI programmes procuring RNGD cards or NXT RNGD Servers, usually after a Furiosa Access evaluationAdds: Single enterprise track covering hardware procurement plus the furiosa-llm and furiosa-torch software stack, with evaluation access via bare metal or an OpenAI-compatible endpoint |
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Where the pricing makes sense
The company stage and team size where FuriosaAI's pricing actually pencils out — and where peers do it cheaper.
FuriosaAI is an enterprise hardware buy, so the comparison that matters is tokens per rack-kilowatt against Nvidia GPU nodes and other inference accelerators, not a monthly subscription line. If your colocation bill is power-dominated and your floor is air-cooled, the 3 kW NXT RNGD Server can change the denominator. If you need liquid-cooled density or CUDA ubiquity, general-purpose GPU vendors stay cheaper overall once integration and porting effort are counted.
Has FuriosaAI's pricing changed?
No plan price for FuriosaAI has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick FuriosaAI?
Pick FuriosaAI if you are Enterprises with air-cooled racks capped near 15 kW that need more LLM throughput per rack, Teams serving LLM, multimodal or agentic inference where power and TCO dominate the decision, Organizations with platform engineers able to port models onto a non-CUDA compiler stack, Cloud and colocation operators packaging multi-tenant RNGD capacity with SR-IOV and Kubernetes.
Look elsewhere if you are Teams requiring CUDA ecosystem compatibility or a drop-in GPU swap for arbitrary workloads, Training, fine-tuning or pre-training projects — the RNGD line targets inference, Product teams with no platform engineering capacity for toolchain integration, Deployments already built around liquid-cooled, high-power racks where density buys less.
Skip it if Skip FuriosaAI if you need a drop-in CUDA-compatible GPU replacement or you are training and fine-tuning rather than serving inference.
GeneratedReal-world workflow fit
Concrete scenarios for the personas FuriosaAI actually fits — and what changes day-one when you adopt it.
Request Furiosa Access bare-metal time on a dedicated NXT RNGD Server, load your production prompt mix through furiosa-llm on port 8000, and measure throughput, latency and power at the wall under real user patterns
Outcome: You get a power-and-throughput comparison against your existing GPU nodes that reflects your own SLOs, not a vendor benchmark
Add RNGD as a device with furiosa.torch, run the model in eager mode to confirm correctness, then compile it with torch.compile or build it ahead of time for deployment
Outcome: A runnable RNGD deployment path for a model that is not in the Model Zoo, without a full compiler rewrite
Point an existing OpenAI SDK client at an OpenAI-compatible RNGD endpoint during Furiosa Access and run the evaluation suite against it
Outcome: Output-quality, latency and compatibility answers without installing anything or changing client code
Use cases
- Serve gpt-oss, Llama 3.3 70B or Qwen3 models in production behind an OpenAI-compatible endpoint without rewriting client code
- Run vision-language inference such as Qwen3-VL or EXAONE 4.5 33B on RNGD
- Add LLM serving capacity inside air-cooled racks where power is capped near 15 kW
- Build sovereign AI appliances for governments and enterprises that require local data processing
- Port a custom PyTorch model to RNGD with furiosa-torch when it is not in the Model Zoo
- Package multi-tenant RNGD inference capacity for cloud or colocation customers using SR-IOV and Kubernetes
- Run domestic NPU-as-a-Service offerings through the Samsung SDS partnership
- Benchmark throughput, latency and wall power against your own SLOs during a Furiosa Access evaluation
Setup time & first value
How long it actually takes to get something useful out of FuriosaAI — broken out by persona, not the marketing-page minute.
Application validation via the OpenAI-compatible endpoint is quick — change one base_url and run your eval suite. Bare-metal evaluation takes longer: you schedule NXT RNGD Server access, then instrument power and latency under your own traffic, typically a multi-week exercise. Porting a model outside the Model Zoo with furiosa-torch is the long pole; plan weeks, not hours.
Switching to or from FuriosaAI
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Nvidia GPU inference: repoint OpenAI SDK clients at the furiosa-llm endpoint on port 8000 and confirm which of your models are already in the Model Zoo
- →From Nvidia GPU inference with custom models: move the model onto RNGD with furiosa-torch — eager first for correctness, then torch.compile or AOT for performance
- →From hosted inference APIs: validate output quality and latency against an OpenAI-compatible RNGD endpoint during Furiosa Access before moving traffic
- →From CPU inference: pick a pre-compiled Model Zoo checkpoint (gpt-oss, Llama 3.3 70B, Qwen3) and serve it with a single furiosa-llm serve command
- ↗To Nvidia GPUs: return to vLLM or TensorRT-LLM and revalidate any model ported to RNGD, since furiosa-torch kernels do not transfer
- ↗To hosted inference APIs: point OpenAI SDK clients back at the provider base_url; the OpenAI-compatible interface makes this the cheapest exit
- ↗To another inference accelerator: expect a fresh porting cycle for models you moved off the Model Zoo
Integrations
Resources & guides
Tutorials & Learning
YouTube returned 6 videos for “FuriosaAI”, and we withheld 6: 6 could not be judged, because “FuriosaAI” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about FuriosaAI.
Official links
What are the best FuriosaAI alternatives?
The closest alternatives to FuriosaAI we list are DeepInfra, Together AI and Vllm. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| DeepInfra | Paid | Pricing not published | — |
| Together AI | Freemium | Pricing not published | — |
| Vllm | Free | Free | — |
Alternatives to FuriosaAI
View all
DeepInfra
Serverless AI inference on 100+ open models via an OpenAI-compatible API, billed per token with no GPU overhead.
Together AI
Run open-weight model inference on Together AI — serverless APIs, batch jobs, and GPU clusters.
Tools that pair well with FuriosaAI
Common stack mates teams adopt alongside FuriosaAI, with the specific reason each pairing earns its keep.
DeepInfra
Serverless AI inference on 100+ open models via an OpenAI-compatible API, billed per token with no GPU overhead.
Together AI
Run open-weight model inference on Together AI — serverless APIs, batch jobs, and GPU clusters.
Vllm
vLLM turns open-weight LLMs into high-throughput, OpenAI-compatible inference endpoints on your own GPUs.
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