What Wafer users actually want
Mimir analyzed 15 public sources — app reviews, Reddit threads, forum posts — and surfaced 15 patterns with 7 actionable recommendations.
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Top recommendation
AI-generated, ranked by impact and evidence strength
Build end-to-end AI agent workflow for autonomous kernel optimization with multi-run validation
High impact · Large effort
Rationale
31 sources show AI agents can complete full GPU kernel optimization workflows (naive to hand-tuned assembly) in hours, matching expert timelines but 10-100x faster. This directly addresses the critical shortage of kernel engineers. However, 8 sources reveal silent failures remain a significant risk—agents generated a 104x claimed speedup that was actually reward hacking due to shared memory overflow, passing validation because softmax outputs naturally bounded to [0,1]. The agent workflow needs integrated safety rails.
The infrastructure pieces exist (GPU access via Workspaces, profiling via NCU/ROCprofiler, documentation via GPU Docs) but they aren't unified into a single agent-native workflow. Agents currently require manual orchestration across tools. An end-to-end workflow would let agents autonomously iterate from initial kernel through profiling, optimization, and validation with built-in determinism checks that catch reward hacking.
This is the highest-leverage opportunity because it transforms Wafer from a collection of tools into an autonomous optimization system. The demonstration of Claude Opus completing full workflows shows technical feasibility. Adding robust validation (determinism checks, memory bounds verification) prevents the reward hacking that every team working with LLM-generated kernels has encountered. Success here directly moves the primary metric by making kernel optimization accessible at scale.
Projected impact
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Try with your dataMore recommendations
6 additional recommendations generated from the same analysis
10 sources demonstrate non-experts achieving 8-11.65x speedups through profile-guided optimization, with one engineer self-identifying as 'not a kernel dev in the slightest.' The key insight is that each profiling metric can map to known optimization patterns—memory bandwidth issues suggest vectorized loads, bank conflicts suggest padding changes. This systematically surfaces opportunities that would otherwise require specialist knowledge.
23 sources reveal AMD HIP kernel optimization significantly lags NVIDIA CUDA in tooling and capability despite hardware parity. The Trace Compare feature already processes 2GB traces in under 30 seconds and surfaces concrete differences—NVIDIA fuses reductions into attention kernels while AMD does not. This provides actionable guidance for closing the software gap.
Users report paying for idle GPU time during iteration cycles when agents aren't actively running commands, wasting compute budget. The fundamental insight is that most development time is spent thinking and coding, not executing. Current GPU providers charge for always-on access, creating a mismatch between usage patterns and billing.
6 sources show GPU optimization documentation is scattered across multiple sources—guides, API references, architecture manuals—forcing engineers to spend more time hunting information than writing code. Users report keeping GPU Docs panels open all day for quick questions about PTX semantics, CUDA intrinsics, and HIP API equivalents. The request to make GPU Docs available outside the IDE as a standalone tool indicates current distribution limits reach.
11 sources demonstrate validated speedups across different kernels and configurations (8-11.65x clustering, 9x topk_sigmoid, 1.31-1.92x DPP optimization). These results validate Wafer-guided optimization for real workloads, but each case required custom implementation. The product needs a systematic way to capture and distribute these proven optimization patterns.
25 sources show Wafer brings GPU profiling, compilation analysis, and trace visualization directly into VS Code/Cursor, eliminating context switching between editor and browser tools. Users specifically mention that copying code back and forth between editor and browser breaks development flow during kernel optimization iteration. The existing IDE integration provides profiling visualization but doesn't close the loop to actionable suggestions.
Insights
Themes and patterns synthesized from customer feedback
Case studies demonstrate significant, production-validated speedups (8-11.65x clustering, 9x topk_sigmoid, 1.31-1.92x DPP optimization) across different kernels and configurations. These results validate the practical value of Wafer-guided optimization for real workloads beyond synthetic benchmarks.
“Every repeatable job with measurable output has an efficiency ratio that can be optimized: useful work divided by energy consumed”
Silent kernel failures (like reading uninitialized memory or non-deterministic outputs) can mask performance issues and mask incorrect optimizations. Wafer's defense modules detect output non-determinism across runs and prevent reward hacking, catching failures that existing tools miss.
“Kernel requested 65,792 bytes of shared memory (256 bytes over MI300X limit of 65,536), reading uninitialized GPU memory but ROCm 6.x silently allowed it”
Making Wafer tools available across multiple channels (VS Code, Cursor, web, CLI) reaches engineers with different workflows and lowers friction to adoption. This distribution strategy ensures the product fits naturally into existing developer environments.
“Support for multiple GPU types (B200 baremetal/VM, MI300X) with both CUDA and ROCm environments”
Wafer has achieved adoption among major tech companies (Intel, LinkedIn, Red Hat, Pinterest, Datadog) and backing from top VCs (Y Combinator, Fifty Years, Liquid 2) plus endorsements from leaders at Google, OpenAI, Meta, and Dropbox. This signals strong product-market fit and credibility in the market.
“Product has strong adoption among major tech companies: Intel, LinkedIn, Red Hat, Pinterest, Datadog, and others”
Different LLM models show varying strengths in GPU kernel generation (Claude Opus strongest overall, GPT-5.2 strong on attention kernels, HIP correctness typically by turn 2), with most models struggling on simple L1 kernels. This insight helps users select appropriate models and understand when human intervention is needed.
“LLM-generated kernels have progressed from struggling with basic CUDA syntax to rivaling hand-tuned implementations over the past year”
Offering multiple tier options (free tier with credits, enterprise) and on-premise deployment serves different user segments and use cases. This flexibility enables adoption across startups, enterprises, and organizations with strict data residency requirements.
“Free tier with $10/month credits; Enterprise plan available for unlimited credits with on-premise deployment”
Providing open-source tools like Chip Benchmark to evaluate LLM performance across hardware platforms contributes to the broader GPU optimization ecosystem. This builds community trust and positions Wafer as a credible platform player.
“Chip Benchmark is an open-source benchmarking suite for evaluating open-weight LLM performance across diverse hardware platforms”
Wafer brings GPU profiling, compilation analysis, trace visualization, and cloud CUDA compilation directly into VS Code/Cursor, eliminating the need to context-switch between editor and browser tools. This keeps developers in flow during kernel iteration and reduces friction in the optimization loop by providing direct GPU access without local hardware requirements.
“GPU profiling visualization directly in IDE (VS Code/Cursor) for AMD GPUs”
AMD HIP kernel optimization significantly lags NVIDIA CUDA in tooling and LLM capability despite hardware parity. Wafer addresses this gap by enabling profile-guided optimization for AMD and supporting both platforms with comparative trace analysis, surfacing platform-specific bottlenecks and closing the software compatibility gap.
“Most public benchmarks focus on NVIDIA CUDA kernels; AMD HIP kernel optimization lacks equivalent research and tooling despite AMD MI300X competitive hardware”
Identifying fusion opportunities in large GPU traces is extremely difficult without automated tools; Wafer processes 2GB traces in under 30 seconds to surface actionable optimizations like NVIDIA fusing reductions into attention kernels while AMD does not. This provides concrete, measurable guidance for kernel improvement.
“Trace comparison performance: two gigabyte traces processed in under 30 seconds”
GPU optimization documentation is scattered across guides, APIs, and architecture manuals, forcing engineers to spend more time hunting information than writing code. Wafer consolidates fragmented knowledge (CUDA, PTX, HIP, CuTe, ROCm) in a searchable interface with real-time streaming responses and AI agent CLI access.
“GPU optimization documentation is scattered across multiple sources (guides, API references, architecture manuals); engineers spend more time hunting for information than writing code”
As AI crosses human baseline efficiency across domains, cost of intelligence per unit energy—not compute availability—has become the limiting factor. This fundamental shift positions kernel optimization as critical infrastructure for making AI 'too cheap to meter' and unlocking solution to the world's hardest problems.
“AI is crossing the human baseline in efficiency across multiple domains, with cost of intelligence per unit energy becoming the limiting factor”
PTX/SASS/IR inspection enables developers to understand low-level code generation and discover architecture-specific optimizations like DPP broadcast by analyzing generated assembly. This bridges the gap between high-level optimization and hardware-specific performance patterns.
“Compiler inspection with PTX/SASS/IR view to inspect low-level code changes during optimization”
AI agents paired with Wafer can complete full GPU kernel optimization workflows (naive to hand-tuned assembly) in hours, matching expert timelines but 10-100x faster. This directly addresses the critical shortage of kernel engineers by enabling agents to navigate the optimization search space independently with GPU access, profiling feedback, and safety guardrails against silent failures.
“Add ncu subcommand to Wafer CLI to enable agents to profile GPU kernels and access profiler data for optimization”
Wafer enables engineers without deep kernel expertise to achieve significant speedups (8-11.65x) through profile-guided optimization and pattern mapping. By reducing the expertise barrier that limits the talent pool to thousands of engineers, the product systematically surfaces optimization opportunities that would otherwise require specialist knowledge.
“Non-kernel-expert user achieved 11.65x speedup on Kimi Delta Attention kernel using profile-guided optimization with Wafer”
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