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GPU Driver Setup — CUDA vs ROCm vs Vulkan

As of CUDA 13.4 (Linux) / 13.1 (Windows): the NVIDIA driver is NO LONGER bundled with the CUDA Toolkit — install it separately. ROCm 7.2.x is the first release with official RDNA 4 support.

NVIDIA / CUDA (the default path): - Current line is CUDA 13.4 Update 1. Breaking change: the NVIDIA driver is no longer bundled with the CUDA Toolkit — on Windows since CUDA 13.1, on Linux since CUDA 13.4. You must install the driver separately from nvidia.com/drivers. - Toolkit→driver-branch mapping: CUDA 13.4 → R615, 13.3 → R610, 13.2 → R595, 13.1 → R590, 13.0 → R580. RTX Spark device needs driver ≥ 616.41 on Windows. - Minor-version compatibility: any CUDA 13.x app runs on drivers ≥ 580 (12.x needs ≥ 525 and < 580). Drivers are backward compatible, so a newer driver generally satisfies an older toolkit. - Install via RPM / Debian / Runfile / Conda depending on platform; datacenter cards follow the separate NVIDIA Data Center Driver lifecycle (New Feature Branch vs Production Branch, per docs.nvidia.com/datacenter/tesla/drivers). - Toolchain: NVCC runtime, cuBLAS 13.8, cuDNN, NCCL; profiling with Nsight Systems/Compute (2026.3.x).

AMD / ROCm (now genuinely viable for inference): - Current line is ROCm 7.2.x (7.2.4 latest patch, June 2026). ROCm 7.2 (March 2026) is the first release with official RDNA 4 (RX 9070/9070 XT, gfx1201) support and the first to claim out-of-the-box Ollama / LM Studio / llama.cpp / vLLM parity with CUDA on Radeon, shipped as one combined Windows + Linux installer. - Install on Ubuntu: amdgpu-install --usecase=rocm,hiplibsdk; verify with rocminfo; pin the 6.2/6.3 commands as a fallback if a 7.x point release regresses your card. - Official support covers RX 7900 XTX/XT/GRE (RDNA 3), RX 9070/9070 XT (RDNA 4, from 7.2), Radeon Pro W7800/W7900, Ryzen AI Max+ 395 / Strix Halo (128 GB unified, gfx1151), and CDNA (MI210/250/300X/325X). Older RDNA 2/2.5 and RDNA 3.5 APU cards work via HSA_OVERRIDE_GFX_VERSION (e.g. gfx1030→10.3.0, gfx1101→11.0.0, gfx1151→11.5.1) but are unsupported for production. - FlashAttention-2 has an official AMD port for RDNA 3 and CDNA 3; FP8 is mature on MI300X, landing for RDNA 4, absent on RDNA 3. HIP is largely CUDA-source-compatible (hipify), but hand-tuned NVIDIA kernels (FA-3, fused MoE) lag. - llama.cpp with ROCm: cmake -B build -G Ninja -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DGGML_HIP=ON -DAMDGPU_TARGETS=gfx1100 -DCMAKE_BUILD_TYPE=Release; add -DGGML_OPENMP=OFF on OpenMP link errors. Set HIP_VISIBLE_DEVICES=0|1 on multi-GPU systems. - Real numbers: RX 7900 XTX (24 GB) runs Llama 3.1 8B at ~96 tok/s ≈ 75% of an RTX 4090; MI300X (192 GB HBM3) beats H100 on long-context inference. Still lagging: training tooling parity and some framework kernels. For pure inference ROCm is a real choice in 2026; for training, NVIDIA still wins.

Vulkan (the cross-vendor fallback): - Vulkan is a cross-vendor compute backend and a practical llama.cpp GGUF path (also for Intel and older AMD cards where ROCm is unsupported), but it is not a drop-in replacement for the wider CUDA/ROCm library ecosystems (cuBLAS/cuDNN/NCCL equivalents). - llama.cpp 0.6.0 (5 Oct 2026) added sparse flash attention for quantized K/V on Vulkan, plus recent Intel Xe flash-attention kernels for Xe-LPG Plus / Xe2 / Xe3 and a Vulkan cooperative-matrix int8 matmul path lifting prompt throughput on Radeon RX 7900-class GPUs. - Benchmark both backends on supported RDNA 3/4 — kernel performance varies with model shape, quantization, context and build.

Sources

  • https://huggingface.co/docs/hub/en/local-apps — Hugging Face's official "Use AI Models Locally" docs (llama.cpp, Ollama, Jan, LM Studio one-command flows)
  • https://huggingface.co/learn/llm-course/en/chapter1/1 — Hugging Face LLM Course overview (structure, prerequisites, prerequisites, notebooks)
  • https://huggingface.co/papers — Hugging Face Daily Papers (arXiv ranked by community upvotes)
  • https://www.reddit.com/r/LocalLLaMA/ — r/LocalLLaMA, the local-inference community hub
  • https://subriff.com/guides/best-subreddits-for-ai — r/LocalLLaMA membership/growth stats (844,249 members, ~60 posts/day, Sep 2026)
  • https://prowlo.com/tools/subreddit-stats/localllama — Independent r/LocalLLaMA stats (820,305 members, 9 Sep 2026 crawl)
  • https://dupple.com/learn/ai-news-for-developers — 2026 developer AI news reading list (Simon Willison, Latent Space, The Batch, HN, Daily Papers)
  • https://aiwiki.ai/wiki/open_weight_license_comparison — Open-weight LLM licence comparison, verified July 2026 (per-model table: Apache-2.0/MIT vs Llama 700M MAU vs Gemma/MRL/OpenRAIL)
  • https://opensource.org/ai/open-source-ai-definition — OSI Open Source AI Definition 1.0 (weights + architecture + usage info under OSI terms)
  • https://ai-act-service-desk.ec.europa.eu/en/ai-act/article-53 — EU AI Act Article 53 text incl. the 53(2) open-source exemption
  • https://ai-act-service-desk.ec.europa.eu/en/ai-act/faq/how-does-ai-act-apply-general-purpose-ai-models-released-open-source — Official FAQ on the open-source exemption's limits (no systemic-risk models; copyright/training-data duties survive)
  • https://digital-strategy.ec.europa.eu/en/faqs/guidelines-obligations-general-purpose-ai-providers — Commission GPAI guidelines (provider duties, fine-tuning/modification triggers)
  • https://corp-intl.com/news/what-is-the-timeline-for-implementing-the-eu-ai-act — Post-omnibus EU AI Act timeline, 16 Sep 2026 (Digital Omnibus 2026/1744, in force 27 Jul 2026; high-risk pushed to Dec 2027 / Aug 2028)
  • https://www.europarl.europa.eu/legislative-train/package-digital-package/file-digital-omnibus-on-ai — Parliament legislative train on the Digital Omnibus on AI (7 May 2026 trilogue agreement)
  • https://www.promptquorum.com/local-llms/local-llm-security-privacy-checklist — 12-point local LLM security checklist (telemetry defaults per tool, SHA-256 verification, localhost binding, pf/ufw egress blocking, GDPR/HIPAA/APPI/PIPL notes)
  • https://safeguard.sh/resources/blog/model-supply-chain-poisoning-detection-2026 — 2026 model supply-chain threat model (pickle, trust_remote_code, weight backdoors, dataset poisoning, Sigstore signing, MITRE ATLAS, 9 HF takedowns in Q1 2026)
  • https://docs.nvidia.com/cuda//cuda-toolkit-release-notes/index.html — CUDA 13.4 U1 release notes (driver no longer bundled since 13.4 Linux / 13.1 Windows; toolkit→R-branch table; minor-version compatibility)
  • https://docs.nvidia.com/datacenter/tesla/drivers/latest/pdf/NVIDIA_Datacenter_Drivers.pdf — NVIDIA datacenter driver lifecycle (New Feature Branch vs Production Branch)
  • https://rocm.blogs.amd.com/artificial-intelligence/language-models-locally/README.html — AMD's first-party "Practical Guide to Running LLMs on AMD Radeon GPUs" (19 Jun 2026)
  • https://localaimaster.com/blog/amd-rocm-local-llm-setup — ROCm 7.2.x state-of-play, supported/unsupported GPU table, HSA_OVERRIDE_GFX_VERSION, ROCm vs CUDA comparison, ~96 tok/s on 7900 XTX
  • https://rocm.docs.amd.com/projects/ai-ecosystem/en/latest/inference/vllm.html — Official vLLM-on-ROCm setup (prebuilt Docker image, ROCm 7.x)
  • https://d-central.tech/cuda-vs-rocm-local-inference/ — CUDA vs ROCm vs Vulkan backend comparison for local inference
  • https://freedom.tech/posts/2026-10-05-llama-cpp-0-6-0/ — llama.cpp 0.6.0 release notes (5 Oct 2026)
  • https://machinelearning.apple.com/research/exploring-llms-mlx-m5 — Apple ML research: MLX on M5 Neural Accelerators (TTFT up to 3.97×, generation 1.19–1.27×, macOS 26.2+ requirement)
  • https://developer.apple.com/videos/play/wwdc2026/232/ — Apple WWDC26: local agentic AI on the Mac with MLX
  • https://codersera.com/blog/apple-silicon-llms-complete-guide-2026/ — Apple Silicon LLM guide (MLX vs vllm-mlx vs oMLX vs Ollama, unified-memory constraints)
  • https://www.iunera.com/kraken/enterprise-ai/top-20-tools-to-run-llms-locally-in-2026-ollama-anythingllm-open-webui-lm-studio-vllm-and-every-real-alternative-compared/ — 20-tool local LLM comparison (difficulty, open source, enterprise readiness)
  • https://presenc.ai/research/local-llm-vs-cloud-api-cost-2026 — Local vs cloud cost/TCO and breakeven analysis, updated October 2026
  • https://www.promptquorum.com/local-llms/local-llms-vs-cloud-apis — Local vs cloud 8-factor comparison (privacy, cost, speed, quality, regional compliance)
  • https://opentelemetry.io/blog/2026/genai-observability/index.md — OTel GenAI semantic conventions walkthrough (span attributes, metrics, Aspire Dashboard)
  • https://signoz.io/docs/open-webui-monitoring/ — Open WebUI observability with OpenTelemetry
  • https://docs.openwebui.com/features/administration/analytics/ — Open WebUI built-in analytics (usage, token consumption, per-model/per-user)
  • https://github.com/prove-ai/observability-pipeline/blob/main/docs/guides/vllm-guide.md — vLLM + Prometheus GPU monitoring guide
  • https://artificialanalysis.ai/hardware-inference-stack/laptops-workstations — Artificial Analysis local inference benchmark leaderboard
  • https://artificialanalysis.ai/articles/aa-agentperf-local — AA-AgentPerf-Local: open-source local agent benchmark tool (29 Sep 2026)
  • https://simonwillison.net/tags/local-llms/ — Simon Willison's local-LLM blog archive (165+ posts)
  • https://www.turingpost.com/p/tools-for-model-deployment — Turing Post: 2026 open-source model deployment tooling overview
  • https://www.datacamp.com/tutorial/gguf-format-a-complete-guide — GGUF quantization and sizing reference (7B FP16 ~14 GB vs Q4_K_M ~4–5 GB)

Date: 2026-10-09