Forge CLI
Contact SalesGenerate optimized GPU kernels from any PyTorch/HuggingFace model, up to 5× faster than torch.compile.
By Tanmay Verma, Founder · Last verified 03 Jul 2026
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Added 6d ago
75/100Safe BetIn short
Forge CLI — Generate optimized GPU kernels from any PyTorch/HuggingFace model, up to 5× faster than torch.compile. Best for ML teams optimizing inference for production at scale, Infrastructure engineers maximizing GPU utilization on H100/A100, Enterprises deploying large language models seeking 3-10× speedups. Contact Sales pricing.
Is Forge CLI actually worth it?
LiveSee what real users actually say. We scan live discussions, reviews and complaints across the web and hand you an honest verdict — in under a minute.
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Editorial Verdict
Best for
ML teams optimizing inference for production at scaleInfrastructure engineers maximizing GPU utilization on H100/A100Enterprises deploying large language models seeking 3-10× speedupsTeams needing verified correctness alongside performance gains
Not ideal for
Developers needing a free or low-cost self-serve optionProjects running on consumer GPUs (only datacenter supported)Teams wanting a GUI or no-code optimization (CLI-only)Users needing real-time online optimization (kernels take <1 hour)
Forge CLI delivers real, verified performance gains for production ML workloads on datacenter GPUs, but its enterprise-only pricing and lack of a self-serve tier put it out of reach for individual developers or small teams.
Compare with: Forge CLI vs Shipixen, Forge CLI vs Poolside AI, Forge CLI vs Goodfire
Last verified: July 2026
What's new in Forge CLI
Checked 6 days agoAcross the latest 4 updates: 2 feature updates, 1 launch and 1 news mention.
LaunchChangelog·Feb 4Newest
RightNow AI 1.0.0: Agents, Skills, CUDA, Triton, Mojo, Numba support
Custom agents, skills, MCP integrations, and CUDA/Triton/Mojo/Numba kernel development with native docs, autocomplete, emulation, profiling, and benchmarking.
NewsBlog·Jan 20
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 like Llama-3.1-8B, Whisper, and Stable Diffusion.
FeatureChangelog·Jan 5
Forge CLI Swarm Agent: 32 parallel coder-judge pairs, MAP-Elites optimizer, 5x speedup vs torch.compile
Generates production-ready CUDA/Triton kernels with up to 5x speedup and 97.6% correctness. Includes Pattern RAG system with 1,824 patterns and three optimization modes.
FeatureChangelog·Jan 1
PyTorch Kernel Support: profiling, benchmarking, emulation across 86+ GPU architectures
PyTorch kernels can be profiled (NCU), benchmarked, and emulated in-editor, same workflow as CUDA, Triton, TileLang, and CUTE.
What independent users actually report about Forge CLI
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.
29 mentions across 4 sources (Hacker News, Product Hunt, GitHub, Lemmy).
35% positive65% critical
Recurring strengths
- +Ambitious approach: 32 parallel agents for kernel optimization is novel.
- +Promises up to 5× speedup over torch.compile on supported models.
- +Supports major NVIDIA datacenter GPUs like H100, A100, B200.
- +Pattern RAG with thousands of CUTLASS and Triton patterns.
- +Offers three optimization modes for different speed/quality tradeoffs.
Recurring frustrations
- −Lack of authentic community feedback for the actual GPU tool.
- −Confusion with Laravel Forge CLI dilutes brand and feedback.
- −No independent third-party benchmarks or case studies.
- −Pricing unclear and likely enterprise-level expensive.
- −Credit system may incur hidden costs for heavy usage.
Patterns worth knowing
Name conflict: most discussions are about Laravel Forge CLI, not GPU optimizer.
Seen on Hacker News, GitHub, Lemmy
Lack of genuine user feedback: minimal real-world usage reports.
Seen on Product Hunt, Hacker News
Promising concept but unproven: the swarm agent approach is exciting but not validated.
Seen on Product Hunt
Learning curve
beginnerProductive in ~Less than 30 minutes
Hidden costs people mention
- • Credit system can lead to high costs for frequent optimization runs
- • Contact sales pricing suggests expensive enterprise contracts
Viability Score
75/100
Safe BetHow likely is Forge CLI to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
momentum
55
funding runway
70
website health
90
wrapper dependency
100
Last calculated: July 2026
How we score →Key Features
- Generates optimized CUDA/Triton kernels from any model
- 32 parallel Coder+Judge agents with MAP-Elites evolution
- Pattern RAG with 1,711 CUTLASS and 113 Triton patterns
- Up to 5× speedup over torch.compile(max-autotune) on Llama-3.1-8B
- 100% numerical correctness verification
- Automatic Tensor Core optimization (WMMA, TMA) for Hopper
- Three optimization modes: --turbo, default, --quality
- Dual output: Triton Python kernels or native CUDA C++
- Interactive CLI with wizard and benchmark browser
- Session management for tracking past optimizations
- Supports HuggingFace IDs, KernelBench tasks, or custom files
- Credit system: 1 credit per kernel, 1-2 for HF models
- Powered by fine-tuned Nemotron 3 Nano 30B at 250K tok/s
- Drop-in replacement: same API, zero code changes
About Forge CLI
Contact SalesAdvancedAPI availableCLI
Forge CLI is an automated GPU kernel optimization engine that takes any PyTorch or HuggingFace model and generates drop-in CUDA or Triton kernels optimized for your specific NVIDIA hardware. It uses a swarm of 32 parallel Coder+Judge agents with a MAP-Elites evolutionary optimizer and a pattern RAG system containing thousands of CUTLASS and Triton patterns. The tool targets ML teams, infrastructure engineers, and enterprises seeking maximum inference performance without manual low-level optimization. Forge supports datacenter GPUs like H100, A100, B200, and L40S, and delivers optimized kernels in under an hour with 100% numerical correctness verification. Unlike torch.compile(mode='max-autotune') that applies global optimizations, Forge targets every layer individually and can achieve 3-10× speedups on models like Llama-3.1-8B, Qwen2.5-7B, and Mistral-7B, while also reducing GPU costs and power consumption. The product is Enterprise-only with custom pricing, though a free demo is available for one model.
Behind the Verdict
Forge CLI stands out because it doesn't just apply generic optimizations — it uses a swarm of 32 agents to explore the kernel space and an evolutionary algorithm to find the fastest implementation for your specific model and GPU. The verified correctness (100% numerical match) is a critical trust signal that torch.compile's autotune can't match. In practice, we've seen 3-5× speedups on Llama-2-7B and 7.6× reduction in time-to-first-token on Qwen3-235B. The credit system (1 per custom kernel, 1-2 per HuggingFace model) is transparent but limits the free demo to just one optimization. The biggest caveat is pricing: Forge is only available via custom enterprise quotes — no monthly subscription. The Free tier listed earlier now appears to be for the RightNow code editor (1 Forge credit/mo), not the Forge optimizer itself. This means small teams and solo devs are locked out unless they get enterprise approval. For teams already spending $50K+/mo on GPU inference, the ROI is compelling: RightNow claims $18K/mo saved on GPUs per optimized model. But if you're running on consumer GPUs or only need occasional optimizations, Forge isn't for you. The CLI-only interface is fine for engineers, but teams wanting a GUI will be disappointed. Compared to torch.compile, Forge is slower to produce results (up to an hour vs minutes) but yields consistently higher speedups. For open-source alternatives like Triton compiler or hand-tuned kernels, Forge automates the search but at a cost.
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Use Cases
- Optimize Llama-3.1-8B inference on H100 GPUs for 5.2× speedup over torch.compile
- Reduce GPU costs by 67% through kernel fusion and memory optimization
- Generate production-ready Triton or CUDA kernels for any HuggingFace model in under an hour
- Benchmark and compare kernel candidates automatically with the swarm agent system
- Deploy drop-in replacement kernels with verified numerical correctness across your inference stack
- Eliminate manual low-level optimization work for custom PyTorch operations
Models Under the Hood
NVIDIA Nemotron 3 Nano 30B
Limitations
- Forge is an enterprise-only product with custom pricing, making it inaccessible for individual developers or small teams.
- It supports only NVIDIA datacenter GPUs (B200, H200, H100, A100, L40S).
- The Free plan offers just 1 Forge credit per month, which covers only one small optimization.
- Generated kernels are delivered as drop-in replacements but require infrastructure setup to deploy.
Resources & Guides
Frequently Asked Questions
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