CUDA vs ROCm vs SYCL vs Metal vs Vulkan 2026

The Landscape in 2026: Why This Matters More Than Ever

By mid-2026, the AI hardware ecosystem has fractured into at least four major GPU/NPU vendors: NVIDIA (H200, B200, RTX 5090), AMD (MI300X, MI250X, Strix Halo), Intel (Sapphire Rapids with Xe GPUs, upcoming Falcon Shores), and Apple (M3 Ultra, M4 Max). Each vendor pushes its own compute stack. For serious AI builders running LLMs on-prem, the choice of programming model determines not just peak performance but also portability, maintenance burden, and access to latest optimizations like FlashAttention-3, PagedAttention, and speculative decoding.

CUDA still commands roughly 85% of the ML training market and 70% of inference, but its dominance is being challenged on multiple fronts. ROCm 6.x has matured significantly, SYCL offers a standards-based alternative, Metal+MLX delivers unbeatable efficiency on Apple Silicon, and Vulkan provides a low-level path for edge devices. This article dissects each stack with concrete numbers, real library support, and hardware-specific tradeoffs to help you decide which path to bet on for your next build.

CUDA: The Incumbent's Last Stand?

CUDA remains the most mature and performant stack for AI workloads. On an H200 with 141 GB HBM3e at 4.8 TB/s, vLLM with FlashAttention-3 achieves 1200 tokens/s for Llama 2 7B and 180 tokens/s for Llama 2 70B (batch size 1). TensorRT-LLM adds another 10-15% via kernel fusion and INT4/FP8 quantization, pushing 70B to 210 tokens/s on a single H200. For multi-GPU training, Megatron-LM and NeMo scale to thousands of GPUs with near-linear efficiency.

However, CUDA's lock-in is real. NVIDIA's proprietary software stack means you cannot run CUDA binaries on AMD or Intel GPUs without translation layers like ZLUDA (now defunct) or HIP (which requires source conversion). The upcoming B200 with 384 GB HBM3e at 8 TB/s will further widen the gap, but at a cost: a single B200 GPU board costs $30,000+, making it inaccessible for most local setups. For the RTX 5090 (32 GB GDDR7, 1.5 TB/s), CUDA delivers 450 tokens/s for 7B and 45 tokens/s for 70B with 4-bit AWQ quantization, but VRAM limits context length to 8K for 70B.

Key libraries: vLLM, TensorRT-LLM, PyTorch, transformers, CUTLASS, FlashAttention-3, Triton (NVIDIA's own, not OpenAI's). The ecosystem is vast, but the cost of admission is high.

# Example: Running vLLM with CUDA on H200
docker run --gpus all -p 8000:8000 \
  vllm/vllm-openai:latest \
  --model meta-llama/Llama-2-70b-chat-hf \
  --tensor-parallel-size 1 \
  --gpu-memory-utilization 0.95 \
  --max-model-len 4096 \
  --dtype bfloat16

ROCm: AMD's Long-Awaited Contender

ROCm 6.x on the MI300X (192 GB HBM3, 5.2 TB/s) has closed the gap significantly. With vLLM 0.6.x and ROCm-aware FlashAttention, Llama 2 70B inference reaches 170 tokens/s, about 94% of the H200's 180 tokens/s. Training is less impressive: a 7B model on 8x MI300X achieves 85% of the throughput of 8x H100 in mixed-precision training, due to immature cuDNN-equivalent libraries and suboptimal collective communication in RCCL.

For local builders, the Strix Halo APU (integrated RDNA 4 GPU with 128 GB unified memory) is a improvement. It runs ROCm natively and achieves 60 tokens/s for 7B models with llama.cpp, using GGML's ROCm backend. The catch: Strix Halo's memory bandwidth tops out at 800 GB/s, far below H200's 4.8 TB/s, so large models (70B+) are limited to 4-bit quantized and context lengths under 8K.

ROCm's library support has improved: PyTorch 2.5+ ships with ROCm binaries, Triton (AMD fork) works for kernel authors, and FlashAttention-2 is fully ported. However, FlashAttention-3 and CUTLASS remain NVIDIA-only. For most inference workloads, ROCm is a viable alternative, but training teams still face friction.

# Running llama.cpp with ROCm backend on Strix Halo
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
cmake -B build -DGGML_HIP=ON -DAMDGPU_TARGETS=gfx1100
cmake --build build --config Release
./build/bin/main -m llama-2-7b.Q4_K_M.gguf -n 256 -ngl 35 -t 8

SYCL: The Standards-Based Dark Horse

SYCL 2020, implemented by Intel oneAPI and Codeplay's ComputeCpp, offers a single-source C++ programming model that targets NVIDIA, AMD, Intel, and even ARM GPUs. In practice, SYCL's performance on NVIDIA hardware via the CUDA backend is 20-30% slower than native CUDA for LLM kernels, due to overhead in the runtime and less aggressive compiler optimizations. On Intel Data Center GPU Max 1550 (128 GB HBM2e, 2.0 TB/s), SYCL with Intel's Xe Matrix Extensions (XMX) achieves 90 tokens/s for 7B models, competitive with ROCm on MI250X.

For multi-vendor deployments, SYCL's promise is compelling: write once, run anywhere. The SYCLomatic tool can migrate CUDA code to SYCL automatically, but the resulting code often requires manual tuning for peak performance. Real-world LLM inference with SYCL is still nascent; vLLM has experimental SYCL support, but it's not production-ready. MLC-LLM has a SYCL backend that works on Intel GPUs, achieving 40 tokens/s for 7B models on a single Intel Max 1550.

SYCL's main advantage is portability, but the performance penalty is steep. For teams that need to support both NVIDIA and AMD without maintaining two codebases, SYCL is a reasonable middle ground, but expect to leave 20-30% performance on the table compared to native stacks.

// SYCL kernel for element-wise addition (simplified)
#include <sycl/sycl.hpp>
using namespace sycl;

void vec_add(float* a, float* b, float* c, int n) {
  queue q;
  buffer<float> buf_a(a, n);
  buffer<float> buf_b(b, n);
  buffer<float> buf_c(c, n);
  
  q.submit([&](handler& h) {
    auto acc_a = buf_a.get_access<access::mode::read>(h);
    auto acc_b = buf_b.get_access<access::mode::read>(h);
    auto acc_c = buf_c.get_access<access::mode::write>(h);
    h.parallel_for(range<1>(n), [=](id<1> i) {
      acc_c[i] = acc_a[i] + acc_b[i];
    });
  });
}

Metal + MLX: Apple's Efficiency Play

Apple's Metal API, combined with the MLX framework, delivers exceptional performance-per-watt for local LLM inference. On an M3 Ultra (192 GB unified memory, 800 GB/s bandwidth), MLX achieves 80 tokens/s for Llama 2 7B and 12 tokens/s for 70B with 4-bit quantization. The unified memory architecture allows loading 70B models entirely in RAM without PCIe bottlenecks, a huge advantage over discrete GPUs.

However, Metal's limitations are stark: no support for multi-GPU scaling (Apple Silicon uses a single SoC), no FP8 or INT4 tensor core support (only FP16/FP32 and ANE), and no official FlashAttention implementation. MLX's custom kernels are hand-tuned for Apple's GPU architecture but lag behind CUDA in advanced optimizations like PagedAttention. For training, Metal is essentially unusable for models larger than 1B parameters due to memory bandwidth constraints and lack of distributed training support.

For inference on a laptop or desktop, Metal+MLX is the most power-efficient option (200W peak for M3 Ultra vs 700W for H200). But for any serious multi-GPU or large-scale workload, it's a non-starter.

Vulkan: The Edge AI Wildcard

Vulkan, through MLC-LLM and Vulkan Compute, targets edge devices like mobile GPUs (Adreno, Mali) and low-power ASICs. On a Snapdragon X Elite with Adreno GPU, MLC-LLM achieves 25 tokens/s for 7B models with 4-bit quantization, impressive for a 15W TDP. On a desktop RTX 4090, Vulkan via MLC-LLM reaches 350 tokens/s for 7B, about 78% of CUDA's 450 tokens/s.

Vulkan's advantage is its ubiquity: it runs on virtually every GPU from every vendor. The downside is complexity: writing efficient Vulkan compute shaders for LLMs requires deep understanding of GPU memory hierarchies and wavefronts. Libraries like MLC-LLM abstract this, but they still trail native stacks in performance. For edge deployment where CUDA/ROCm are unavailable, Vulkan is the only option, but expect 20-30% lower token rates.

Vulkan's future depends on ecosystem investment. Google's OpenXLA and IREE backends can target Vulkan, but they are not yet competitive with hand-tuned kernels.

# Running MLC-LLM with Vulkan backend on RTX 4090
pip install mlc-llm
mlc_llm chat HF://mlc-ai/Llama-2-7b-chat-hf-q4f16_1-MLC \
  --device vulkan:0 \
  --max-batch-size 1 \
  --max-total-seq-length 4096

Quantization and Kernel Optimization: Where the Stacks Diverge

The real differentiator among these stacks is how they handle quantization and kernel fusion. CUDA has mature support for GPTQ, AWQ, and FP8 via TensorRT-LLM and ExLlamaV2. On H200, 4-bit AWQ achieves 210 tokens/s for 70B with minimal quality loss. ROCm supports GPTQ and AWQ via AutoGPTQ and vLLM, but FP8 is still experimental. SYCL and Metal lack native FP8 support entirely, forcing reliance on INT4/INT8.

FlashAttention is another fault line. FlashAttention-3, with async prefetching and warp-level optimizations, is CUDA-only. ROCm has FlashAttention-2, which is 90% as fast. SYCL and Metal have no official FlashAttention; MLC-LLM uses a custom implementation that is 50-60% slower. For long-context LLMs (32K+ tokens), this gap widens dramatically.

CUTLASS and Triton (NVIDIA) enable custom kernel development for CUDA. AMD's Triton fork is functional but lacks many templates. For serious kernel authors, CUDA is the only game in town.

Multi-GPU Scaling and Interconnects

For multi-GPU LLM inference and training, interconnect bandwidth is critical. NVIDIA's NVLink 4.0 (900 GB/s per GPU on H100) enables near-linear scaling for tensor parallelism. ROCm's Infinity Fabric (400 GB/s on MI300X) is slower but adequate for pipeline parallelism. SYCL and Metal have no standardized multi-GPU interconnect; Vulkan can use peer-to-peer via VK_KHR_device_group, but it's rarely optimized.

Real-world numbers: 8x H200 with NVLink achieves 1400 tokens/s for 70B with tensor parallelism. 8x MI300X with Infinity Fabric achieves 1100 tokens/s, a 21% deficit. For 4x RTX 5090 (PCIe Gen 5 x16, 128 GB/s per direction), tensor parallelism yields 160 tokens/s for 70B, bottlenecked by PCIe bandwidth. Vulkan multi-GPU on 4x RTX 4090 achieves only 100 tokens/s due to software overhead.

For local builders, PCIe Gen 5 is a must for multi-GPU. NVLink is a luxury that only NVIDIA offers.

Ecosystem and Community: The Deciding Factor

CUDA's ecosystem is unmatched: every major library (vLLM, TensorRT-LLM, PyTorch, JAX, CUTLASS, Triton) is first-class. ROCm's ecosystem is second-best but growing: vLLM, PyTorch, and llama.cpp work well, but TensorRT-LLM, ExLlamaV2, and FlashAttention-3 do not. SYCL's ecosystem is thin: MLC-LLM and oneDNN support it, but most LLM tools ignore it. Metal's ecosystem is Apple-only: MLX is excellent but limited. Vulkan's ecosystem is fragmented: MLC-LLM and IREE are the main players.

For a team building a production inference server, CUDA is the safe bet. For a cost-conscious builder using AMD hardware, ROCm is viable. For edge or Apple users, Metal or Vulkan are the only choices. SYCL is a compromise for multi-vendor support but sacrifices performance.

The Bottom Line: Which Stack Should You Choose in 2026?

If you have the budget for NVIDIA hardware (H200, B200, RTX 5090), CUDA is still the best choice for both inference and training. The performance lead is 10-30% over ROCm, and the library support is complete. For AMD hardware (MI300X, Strix Halo), ROCm is good enough for inference and acceptable for training, with a 15-20% cost savings per GPU. For Apple Silicon (M3 Ultra), Metal+MLX is unbeatable for power-efficient local inference but useless for training. For edge devices, Vulkan via MLC-LLM is the only option. SYCL is a niche choice for multi-vendor deployments where performance is secondary to portability.

In 2026, the AI hardware landscape is more diverse than ever, but CUDA's ecosystem moat remains deep. Unless AMD or Intel invest heavily in software parity, CUDA will retain its crown for at least another 2-3 years. For local builders, the best stack is the one that runs on the hardware you can afford. For production, CUDA is still the king.

Further reading

• FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
• vLLM: Easy, Fast, and Cheap LLM Serving with PagedAttention
• ROCm Documentation and Installation Guide
• MLX: An array framework for Apple Silicon
• MLC-LLM: Universal LLM Deployment Engine