RightNow AI
GPU kernel editor with NVIDIA profiling, emulation, and benchmarking.
If you write CUDA or Triton kernels, RightNow AI is the most GPU-focused editor we've tested. The emulator alone justifies Pro at $20/mo, and the free tier isn't crippled. Forge shows real speedups but is enterprise-only. Skip it if you don't work on GPU kernels.
Verified 6d ago · liveness 82/100 · cite: rightaichoice.com/tools/rightnow-ai
- CUDA kernel developers needing integrated profiling and emulation
- ML engineers optimizing GPU inference for production
- GPU researchers exploring kernel optimizations
- HPC developers working on performance-critical kernels
- General-purpose software developers not working on GPUs
- Beginners without GPU programming experience
- Teams needing cloud GPU training (separate RunInfra)
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Skip RightNow AI if you don't write or optimize GPU kernels (CUDA, Triton, etc.)—you'll pay for features you don't need, and general-purpose editors like VS Code are better for typical software development.
Pro tier at $20/mo is required for GPU emulator access and multi-GPU comparison—the free tier lacks these key features, so most serious users will need to pay.
RightNow AI's pricing fits solo GPU developers and small teams who need profiling and benchmarking without enterprise overhead. Free tier is generous (unlimited profiling/benchmarking), Pro at $20/mo is competitive with tools like Nsight (free) but adds emulation and AI features. Enterprise Forge is custom-priced, likely higher than open-source alternatives, but offers dedicated infrastructure and on-prem deployment.
In short
RightNow AI — GPU kernel editor with NVIDIA profiling, emulation, and benchmarking. Best for CUDA kernel developers needing integrated profiling and emulation, ML engineers optimizing GPU inference for production, GPU researchers exploring kernel optimizations. Free to start; paid plans from $20/mo.
What's new in RightNow AI
Checked 4 days agoAcross the latest 4 updates: 2 feature updates, 1 launch and 1 news mention.
RightNow AI 1.0.0: Custom agents, skills, MCPs, more GPU kernel languages
Adds custom agents, skills/MCP integration, and support for CUDA, Triton, Mojo, PyTorch, Numba kernels with native docs, autocomplete, emulation, profiling, benchmarking.
LLMs Can Now Write GPU Kernels That Beat torch.compile
Multi-agent systems produce CUDA/Triton kernels with 2x-14x speedups over torch.compile on real models (Llama-3.1-8B, Whisper, SD).
Forge CLI: Swarm agent for CUDA/Triton kernels, up to 5x faster than torch.compile
Swarm of 32 Coder+Judge pairs, MAP-Elites optimizer, 1,711 CUTLASS patterns, 97.6% correctness, supports HuggingFace IDs, KernelBench, custom PyTorch files.
PyTorch kernel profiling, benchmarking, and emulation in editor
Profile, benchmark, and emulate PyTorch kernels with NCU integration and statistical timing; test across 86+ GPU architectures without hardware.
What people actually say about RightNow AI — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
31 mentions across 2 sources (Hacker News, Lemmy) · researched Jul 3, 2026.
- +GPU emulator supports 86+ architectures without hardware.
- +Integrated NCU profiling and PTX/SASS inspection in-editor.
- +Forge CLI auto-generates CUDA/Triton kernels from PyTorch.
- +Agentic AI writes, debugs, and optimizes CUDA code.
- +Multi-DSL support: CUDA, Triton, CUTE, TileLang, Mojo, etc.
- −Community feedback is too sparse for reliable support assessment.
- −No independent benchmarks confirm emulator accuracy outliers.
- −Forge CLI is v0.1.0, may generate suboptimal kernels.
- −Pricing details beyond freemium model are unclear.
- −Learning curve for GPU emulator and profiling jargon.
- • Local LLM integration requires self-hosted hardware and maintenance
- • Enterprise Forge pricing not transparent in community data
Viability Score
How well maintained and how widely used is RightNow AI? 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: September 2026
How we score →Key Features
- Real-time NVIDIA NCU profiling (Full, Fast, Static, Line-by-Line)
- Automated benchmarking against torch.compile(max_autotune)
- GPU emulator for 50+ architectures (Pro)
- Multi-GPU performance comparison (up to 6 GPUs, Pro)
- Natural language profiling queries (Pro)
- CodeLens performance metrics inline in editor
- PTX/SASS assembly inspection
- Automatic kernel fusion
- GPU virtualization
- Local LLM support (Ollama, vLLM, LM Studio)
- Custom agents, skills, and MCP integrations (1.0.0)
- Multi-DSL support: CUDA, Triton, CUTE, TileLang, PyTorch, Numba, Mojo
- PyTorch kernel profiling, benchmarking, emulation (86+ architectures)
- Remote GPU workflows via SSH
- SSH/SOCKS support
About RightNow AI
RightNow AI is a purpose-built code editor for NVIDIA GPU kernel developers and ML engineers who need to optimize inference. Unlike general-purpose IDEs, it integrates coding, real-time profiling, benchmarking, and emulation into a single workflow. You write CUDA, Triton, CUTE, TileLang, PyTorch, Numba, or Mojo kernels, profile with NVIDIA NCU, and benchmark automatically against torch.compile, all without leaving the editor. The tool is SOC 2 Type 2 compliant and backed by Y Combinator, with an endorsement from NVIDIA's Director of Accelerated Computing. The free tier is genuinely useful, with unlimited profiling and benchmarking, CodeLens performance metrics, GPU virtualization, automatic kernel fusion, and local LLM support (Ollama, vLLM, LM Studio) so your code stays private. Pro at $20/mo adds GPU emulation for 50+ architectures, multi-GPU comparison (up to 6 GPUs), natural language profiling queries, and 1000 AI Agents credits monthly. Forge is the enterprise tier with dedicated infrastructure and AI kernel optimization that generates CUDA or Triton kernels benchmarked at 2-14x speedups over torch.compile on models like Llama-3.1-8B. Recent releases added custom agents, skills, and MCP integrations (1.0.0), plus PyTorch kernel profiling and emulation for 86+ GPU architectures. The Forge CLI swarm agent generates kernels up to 5x faster than torch.compile with 97.6% correctness. RightNow AI is positioned directly against the Nsight + VS Code combo, which lacks emulation and AI-driven kernel generation—making it the only editor built specifically for CUDA, hardware-aware from the start.
Behind the Verdict
When it comes to GPU kernel development, RightNow AI has carved out a niche that general-purpose IDEs ignore. We'd reach for it when we need profiling, emulation, and benchmarking in one place—it saves the hassle of juggling Nsight and VS Code. The free tier is the standout: unlimited profiling and benchmarking, CodeLens metrics, and local LLM support all at $0. That's rare for developer tools. Pro at $20/mo is fairly priced given the GPU emulator for 50+ architectures and multi-GPU comparison. For solo developers or small teams without access to A100s or H100s, the emulator is a serious time-saver. Natural language profiling queries also reduce the learning curve for NCU. The Forge tier, though, is a different beast. It's enterprise-only with custom pricing, and the AI kernel generation claims 2-14x speedups over torch.compile. We haven't tested it hands-on, and the CLI's 97.6% correctness suggests you'll need to validate output. It's promising but not something to bet on without a trial. Where it bites: if you're a general developer who works on CPUs or non-GPU code, this editor is overkill—you'll pay for features you never touch. Beginners without CUDA experience will find the learning curve steep. And while the editor supports Mac and Windows, GPU emulation on those platforms may not match native Linux performance. Compared to Nsight + VS Code, the free and open-source combo, RightNow AI adds emulation and AI-driven kernel generation, but you trade away the flexibility of a full IDE plugin ecosystem. If you value open-source tools, stick with Nsight; if you want hardware-aware assistance and emulation, RightNow AI is the better fit.
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Real-world workflow fit
Concrete scenarios for the personas RightNow AI actually fits — and what changes day-one when you adopt it.
You're optimizing a custom Triton kernel for an LLM inference bottleneck. You open RightNow AI, write your kernel, and run NCU profiling right in the editor. You see CodeLens metrics showing register pressure and warp stalls. You benchmark against torch.compile and see a 2x slowdown, so you tweak the kernel and re-benchmark until you beat it. All without switching tools.
Outcome: You ship a kernel that's faster than torch.compile, with clear evidence of the improvement, and the integrated workflow saves you hours.
Your team needs to deploy a custom Llama-3.1-8B with a fused attention kernel. You use the Forge CLI (if enterprise) to auto-generate a CUDA kernel from the PyTorch model. Then you profile and benchmark the generated kernel in the editor, compare it on A100 vs H100 using the emulator, and identify further optimizations.
Outcome: You deploy a kernel that's 5x faster than torch.compile, reducing inference latency and GPU costs, and you document the performance gain.
You're testing a new sparse attention algorithm. You write a Triton kernel, use the emulator to test it on 50+ GPU architectures without owning them, and run multi-GPU comparisons (up to 6) to see which hardware benefits most. You also use natural language queries to ask, 'What's the bottleneck?' and get actionable insights.
Outcome: You identify the best target GPU for your algorithm and refine the kernel for that hardware, publishing results with solid benchmark data.
Use Cases
- Develop and profile CUDA kernels with real-time performance metrics
- Emulate kernel execution on 50+ GPU architectures without physical hardware
- Automatically generate optimized CUDA/Triton kernels from PyTorch models using Forge CLI
- Compare kernel performance across multiple GPU models side-by-side
- Debug GPU code with PTX/SASS inspection and register pressure analysis
- Leverage AI-powered autocomplete and natural language profiling for faster development
Models Under the Hood
as of 2026-08-27
Limitations
- RightNow AI's editor supports CUDA, Triton, Mojo, PyTorch, and Numba development with profiling, PTX/SASS inspection, GPU emulation, and remote GPU workflows.
- Local LLM support is available via Ollama and vLLM, with clear model selection across cloud and local GPU-backed models.
- Pricing tiers: Free (always free, unlimited profiling), Pro ($20/mo with GPU emulator access to 50+ GPUs and multi-GPU comparison up to 6 GPUs), and Enterprise (custom pricing for Forge kernel optimization with on-premise deployment).
- The Forge CLI generates kernels up to 5x faster than torch.compile with 97.6% correctness, but is enterprise-focused.
as of 2026-08-19
Verification history
We have re-verified RightNow AI 7 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.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 7 verification passes.
Free to cite with attribution — this page re-verifies continuously.
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.
Plans compared
For each published RightNow AI tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0/mo
Ideal for
Solo developers exploring GPU kernel optimization without spending money—genuinely useful with unlimited profiling and benchmarking.
What this tier adds
Starting tier with unlimited profiling/benchmarking, CodeLens metrics, local LLM, kernel fusion, GPU virtualization, but limited autocomplete and no emulator (1 Forge credit/mo).
Pro
$20/mo
Ideal for
Professional developers and small teams who need GPU emulation to test on A100/H100 without owning the hardware.
What this tier adds
Adds GPU emulator (50+ GPUs), multi-GPU comparison (up to 6), natural language profiling, 1000 AI Agents credits/mo, unlimited autocomplete, priority support—$20/mo.
Forge
Custom
Ideal for
Enterprises needing automated kernel generation and on-premise deployment for production inference optimization.
What this tier adds
Adds AI kernel optimization (Forge CLI), drop-in replacement kernels (2-14x speedups), dedicated infrastructure, on-prem, custom SLA, NDA & IP protection—custom pricing.
Where the pricing makes sense
The company stage and team size where RightNow AI's pricing actually pencils out — and where peers do it cheaper.
RightNow AI's pricing fits solo GPU developers and small teams who need profiling and benchmarking without enterprise overhead. Free tier is generous (unlimited profiling/benchmarking), Pro at $20/mo is competitive with tools like Nsight (free) but adds emulation and AI features. Enterprise Forge is custom-priced, likely higher than open-source alternatives, but offers dedicated infrastructure and on-prem deployment.
Setup time & first value
How long it actually takes to get something useful out of RightNow AI — broken out by persona, not the marketing-page minute.
Installation takes about 5 minutes: download the editor, install NVIDIA CUDA Toolkit (if not already), and launch. On first run, you can start profiling existing kernels immediately. For local LLM features, set up Ollama or vLLM separately (10-15 minutes). Forge CLI is a separate install via npm/curl/PowerShell, about 10 minutes to set up. Overall, expect under 30 minutes to first profiling
Integrations
Resources & Guides
- Documentationrightnowai.co
Docs · RightNow AI
Full product docs from rightnowai.co
- Documentationrightnowai.co
Installation · RightNow AI
Full product docs from rightnowai.co
- Documentationrightnowai.co
Core Features · RightNow AI
Full product docs from rightnowai.co
- Documentationrightnowai.co
Advanced Features · RightNow AI
Full product docs from rightnowai.co
Tutorials & Learning
Official links
Tools that pair well with RightNow AI
Common stack mates teams adopt alongside RightNow AI, with the specific reason each pairing earns its keep.
Featured Head-to-Head Comparisons
Rightnow Ai vs Spider Cloud
Choose RightNow AI if you're a GPU kernel developer needing a specialized IDE with GPU emulation, real-time NCU profiling, and AI autocomplete for CUDA/Triton. Choose Spider Cloud if you're building AI agents or RAG pipelines that require fast, reliable web scraping at scale with structured output. They solve completely different problems — one is for writing GPU kernels, the other for fetching web data.
Rightnow Ai vs Temporal Ai
If you're a GPU kernel developer needing a specialized IDE with emulation and profiling for CUDA/Triton, RightNow AI is the clear choice—especially given its recent agent-based kernel generation achieving major speedups. If you need reliable orchestration for AI agents or microservices with automatic retries and state persistence, Temporal AI is the mature, open-source platform used by major companies like OpenAI. They solve fundamentally different problems; choose based on whether your bottleneck is GPU optimization or workflow reliability.
Rightnow Ai vs Voyage Ai
Do not buy both unless you have unrelated needs. If your focus is high-accuracy RAG on specialized domains like finance or legal, choose Voyage AI for its tailored embedding models and rerankers. If you are optimizing CUDA/Triton kernels for NVIDIA GPUs, RightNow AI is the only dedicated AI-powered IDE with profiling, emulation, and multi-DSL support. For mixed workloads, consider hybrid workflows using both tools separately.
Bito vs Rightnow Ai
RightNow AI is your pick if you write GPU kernels and need integrated profiling, emulation, and AI optimization. Bito wins if your team relies on AI coding agents and struggles with cross-repo dependencies, architectural planning, or onboarding. If you do both, consider both—but for most, the choice reduces to: GPU performance or system-wide context?
Pieces For Developers vs Rightnow Ai
If you write CUDA/Triton kernels and need AI-aided profiling, benchmarking, and code generation, RightNow AI is a no-brainer. If your pain is losing context across apps and needing an automatically searchable history of your work, Pieces for Developers is the pick. They solve fundamentally different problems, so choose based on whether you optimize GPU code or your personal workflow.
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