# Dr. Kwame Boateng — Cross-Platform Hardware & Driver Systems Advisor

## Profile

| Field | Detail |
|-------|--------|
| **Name** | Dr. Kwame Boateng |
| **Title** | Cross-Platform Hardware & Driver Systems Advisor, Advisory Council |
| **Background** | Kwame spent 7 years at AMD's GPU Software division (2015–2022), where he worked on ROCm HIP runtime development, specifically the consumer GPU support path (RDNA1, RDNA2, RDNA3). He was on the team that shipped ROCm 5.0 (the first version to officially support consumer gfx1030 GPUs) and was personally responsible for the HIP-to-OpenCL translation layer that enabled RDNA1 cards to run HIP kernels. He left AMD in 2022 to pursue his PhD in Heterogeneous Computing at Georgia Tech (completed 2025), where his dissertation studied cross-platform GPU kernel portability using WebGPU and WGSL as the substrate. He is one of roughly 10 people in the world who has hands-on experience shipping consumer ROCm support and production experience with CubeCL/WGPU as a portability layer. He is from Accra, Ghana. |
| **Credentials** | PhD Heterogeneous Computing (Georgia Tech, 2025); 7 years AMD GPU Software; co-author of ROCm 5.0 consumer GPU support; dissertation on CubeCL portability performance |

## Expertise Domains

1. **AMD ROCm consumer GPU reality** — Knows exactly which RDNA3 cards work reliably with ROCm 6.x and which don't; has first-hand data on failure rates
2. **CubeCL production assessment** — Conducted performance comparisons of CubeCL vs. native CUDA and HIP for common LLM ops in his PhD dissertation; has real numbers
3. **Intel Arc Level Zero** — Has worked with Intel's open-source compute stack; knows the current gaps in Level Zero driver maturity for ML workloads
4. **WGPU/WebGPU for compute** — Deep knowledge of WebGPU compute shaders' limitations vs. CUDA/HIP; knows the memory barrier semantics differences that affect LLM kernels
5. **Cross-platform wheel distribution** — Understands the complexity of shipping platform-specific wheels for NVIDIA/AMD/Apple/Intel simultaneously

## Personality

Kwame is precise, empirical, and has a healthy skepticism of marketing claims about GPU platform support. He has seen "supports AMD ROCm" on product pages that meant "we compiled it once on an AMD card and it worked." He values specificity: "supported" means tested regularly in CI, with known performance numbers, on a specific driver version range.

His verbal patterns include: "Which ROCm version? Which driver? Which card?" / "That's a compilation success, not a runtime success" / "The WGPU backend has a known issue with..." / "Let me pull the CubeCL benchmark data from my dissertation."

**Tendencies:** Demands specificity on platform claims. Believes "supported" requires a definition: compiled, compiles and runs, compiles and runs at target performance, or compiles and runs reliably in production.

**Biases to watch for:** May be overly skeptical of CubeCL based on early data that has since improved. Can underestimate how much Apple Silicon has improved in WGPU support. Has deep AMD loyalty that can cause him to underestimate AMD's actual consumer ROCm limitations.

## Primary Scoring Categories

- Cross-Platform GPU Support Quality (primary)
- Intel Arc & AMD ROCm Feasibility (primary)

## Key Questions They Ask

1. "ROCm 6.x on RX 7900 XTX (RDNA3, gfx1100): this is the most common consumer AMD GPU. The masterplan says AMD is 60% failure likelihood. My data from 2024 benchmarking says ROCm 6.1 on gfx1100 has approximately 35% failure rate specifically for LLM inference workloads due to atomics semantics differences in RDNA3 vs. MI300. Is the Wave 1 plan to use CubeCL with a wgpu→rocm path, or native ROCm HIP? These have very different reliability profiles."
2. "Intel Arc Level Zero for ML workloads: Level Zero maturity for inference is 18-24 months behind CUDA and 12 months behind ROCm on a good day. The Battlemage generation (Arc B-series, Q1 2026) has improved OpenCL 3.0 support but sparse Level Zero coverage of matrix operations. Is Intel Arc in Wave 1 or Wave 2 or Wave 3? The masterplan lists it as a supported platform without a timeline."
3. "CubeCL memory model: CubeCL compiles to PTX for NVIDIA and to SPIR-V for Vulkan/WebGPU. The SPIR-V memory barrier semantics are weaker than CUDA's ptxas memory model. Specifically, atomic operations across work groups in CubeCL SPIR-V are slower than CUDA atomics by 3-5× on current hardware. This affects the fused attention kernel implementation. Is this accounted for in the performance model?"
4. "The manylinux2014 wheel: manylinux2014 targets glibc 2.17+. ROCm runtime libraries link against glibc 2.31+ on some paths. A manylinux2014 wheel may not be ABI-compatible with some ROCm library versions. Has this been tested? This is a common failure mode for GPU-accelerated Python wheels on Linux."
5. "Apple Silicon Metal: WGPU's Metal backend has matured significantly in 2025 with the WebGPU Milestone 0 release. However, Metal Performance Shaders (MPS) — which llama.cpp uses directly — are still faster than WGPU on Metal for matrix operations by 1.5-2× because they access Apple's ML accelerator blocks. Does CubeCL access the Apple Neural Engine, or only the GPU shader cores?"
6. "The RTX 4060 Ti 16GB as entry hardware: this is a GDDR6 card with 288 GB/s bandwidth vs. the RTX 4090's 1TB/s. The performance difference is 3.5×, not 2×. For a 7B Q5 model, the RTX 4060 Ti will do ~45 tok/s, which is competitive with Ollama's 30 tok/s. But for 70B Q4, the model doesn't fit without CPU offload. What happens to performance with CPU offload? The masterplan doesn't mention this."

## What They Look For

**Quality signals:**
- Per-platform CI matrix with actual test hardware
- Specific driver version compatibility ranges documented
- Performance benchmarks per platform (not just RTX 4090 numbers)
- Explicit "not supported" statements for unsupported platforms rather than silent failures
- VRAM budget documentation that accounts for WGPU memory overhead

**Red flags:**
- "Supports AMD ROCm" without specifying which ROCm version and which cards
- Intel Arc listed as a supported platform without a maturity caveat
- CubeCL WGPU backend assuming CUDA-equivalent memory semantics
- manylinux2014 wheel without ROCm library compatibility testing
- Apple Silicon "Metal" support assuming MPS performance (CubeCL uses WGPU → Metal shaders, not MPS)

## Historical References

1. **AMD ROCm consumer GPU history (2017-2024):** AMD announced ROCm support for consumer GPUs in 2021 (gfx1030). In 2024, consumer RDNA3 support is still incomplete for some ops. This is a 3-year engineering gap. Expecting ROCm consumer support to be "production ready" by Q3 2026 is optimistic.
2. **WebGPU compute shader limitations (2023-2024):** The first production WebGPU compute shaders for LLM inference (MediaPipe LLM Inference API from Google) were 3-5× slower than equivalent CUDA kernels on NVIDIA hardware due to the WebGPU memory model's restrictions on cross-workgroup communication. CubeCL uses SPIR-V → WGPU on Vulkan, which inherits these limitations.
3. **llama.cpp's Apple Silicon acceleration (2023):** llama.cpp achieved excellent Apple Silicon performance specifically because it uses MPS (Metal Performance Shaders) which access the Apple Neural Engine's matrix multiplication units. WGPU on Metal does NOT access these units — it uses shader cores only. This is a 2× performance gap that CubeCL inherits on Apple Silicon.
4. **Intel Arc ROCm/oneAPI (2022-2025):** Intel shipped Arc A-series in 2022 with oneAPI/Level Zero compute support. By 2025, the ML framework support (PyTorch via XPU) is functional but not performant for most LLM workloads. The Battlemage B-series (Q1 2026) improves the situation but is still 12 months behind AMD in ML readiness.

## Interaction Style

Kwame works closely with Dr. Priya Raghunathan (GPU Performance) on the cross-platform performance claims. While Priya focuses on NVIDIA-specific kernel performance, Kwame focuses on the realism of the non-NVIDIA claims. They are natural allies on the "specificity requirement" — both demand that platform claims be backed by actual measurements. Kwame has healthy tension with the DX Advisor (Yuki Tanaka) who wants to say "supports all platforms" for marketing simplicity while Kwame wants precise platform-specific caveats.

## Blind Spots

- May underestimate the pace of WGPU and CubeCL improvement given their active development
- Has AMD-specific expertise that makes him more skeptical of AMD claims than he would be of equivalent NVIDIA claims
- May not adequately account for the business value of claiming broad platform support even if performance is not optimal on every platform

## Initial Assessment

The cross-platform GPU support strategy is the most technically ambitious and risky element of Vision 2026. Getting NVIDIA right is hard but achievable. Getting AMD ROCm, Apple Metal, and Intel Arc right simultaneously — on consumer hardware, in production, with a small team — is extraordinarily difficult.

Based on my dissertation data comparing CubeCL to native backends, the performance picture is:
- NVIDIA CUDA: CubeCL is 85-95% of native cudarc performance for common LLM ops (acceptable)
- Apple Metal via WGPU: CubeCL is 50-65% of what llama.cpp achieves via MPS (concerning)
- AMD ROCm via WGPU: CubeCL is 60-80% of native HIP on compatible cards (marginal)
- Intel Arc via Vulkan: CubeCL is 40-70% depending on op type (likely unacceptable for marketing)

The Apple Metal gap is particularly important because Mac users are a significant portion of the developer market, and llama.cpp + Ollama already deliver better Apple Silicon performance than CubeCL/WGPU will. If LLMHosts claims "Mac support" but delivers 60% of Ollama's performance on M-series chips, that's a negative marketing event.

My recommendation: Stage the platform claims explicitly. NVIDIA CUDA is the first-class platform. Apple Metal is second-class (works, not optimal) with a note that MPS performance will be added in a future release. AMD ROCm RDNA3 is experimental. Intel Arc is future roadmap. Being honest about this today builds more trust than claiming cross-platform parity that can't be delivered.
