SaaS Metrics
Gemini Executive Synthesis
Request for aarch64 CUDA support for Ollaya, specifically for NVIDIA GB10 GPUs on DGX Spark platforms.
Technical Positioning
Ollaya aims to provide GPU acceleration across diverse architectures, but prioritizes verification on real hardware. It leverages existing `llama.cpp` CUDA builds for GGUF models (e.g., `winnow`) as a realistic first step, while acknowledging the greater effort required for ONNX models due to lack of official aarch64 CUDA builds from Microsoft. The core principle is rigorous "parity gate" testing before shipping.
SaaS Insight & Market Implications
This issue exposes a critical developer pain point: deploying high-performance decision models on specialized, emerging hardware architectures like aarch64 with NVIDIA GB10 GPUs. While Ollaya supports GPU acceleration, the fragmentation of CUDA builds across different model engines (GGUF vs. ONNX) and the lack of official support for certain architectures (ONNX Runtime on aarch64 CUDA) create significant integration challenges. Ollaya's commitment to a "parity gate" on real hardware before shipping, though ensuring reliability, slows adoption on new platforms. This highlights the market's demand for robust, cross-platform GPU support, and Ollaya's strategic response to prioritize verified, stable deployments, even if it means a phased rollout. The reliance on community testing for new hardware paths indicates a pragmatic approach to expanding its hardware matrix.
Proprietary Technical Taxonomy
Raw Developer Origin & Technical Request
GitHub Issue
Sep 28, 2026
Repo: ollaya-dev/ollaya
aarch64 CUDA support?
I have a DGX Spark (aarch64, nvidia gb10, unified ram) and would like to run my Ollaya instance on the GPU instead of the CPU. How hard would it be to get CUDA working?
Developer Debate & Comments
Good question. There are two engines, and aarch64 CUDA differs between them: - **GGUF models on llama.cpp** (`winnow`, `jevk5`): llama.cpp b11146, the build Ollaya pins, ships `ubuntu-cuda-13.4-arm64`, and its CUDA backend can go into a linux-arm64 GPU pack the same way as on x86-64 and Windows . This is the realistic first step, and it covers `winnow:e4b`, the recommended model. - **ONNX models** (`laya`, `decider`, `kev`, …): Microsoft publishes ONNX Runtime's CUDA build only for x86-64, and linux-aarch64 is CPU-only. GPU support there would mean building ONNX Runtime ourselves for sm_121 (GB10), which is a bigger job. The blocker is verification. Every device path has to pass the parity gate on real hardware before it ships, and I don't have a GB10. If I prepare a linux-arm64 CUDA pack for the llama.cpp models, would you be up for running the parity check on your Spark? It's one command against a published fixture, and I'd post the exact steps. Leaving this open to track it.
I also have a DGX Spark (aarch64, GB10) and would be happy to help test the ARM64 CUDA pack if needed. Share the build and parity-check commands, and I can run them and report the results and logs.
Thanks @drsect0r and @Xe, that helps a lot. Here's the parity check for `winnow:e4b` on llama.cpp's CUDA backend on a GB10. It's the same gate every device passes: Ollaya's runner against a stock `llama-server` of the pinned build (b11146), on the same GPU, with every decision identical and option logits within 1e-3. ggml-org's `ubuntu-cuda-13.4-arm64` build of b11146 includes `sm_121a` kernels, so GB10 is covered. I ran these exact steps on x86-64 with the x86 archives (505/505, logits within 1.3e-5). **You need:** `git`, `curl`, `python3` (standard library only), a Rust toolchain (`curl https://sh.rustup.rs -sSf | sh`), the CUDA 13 runtime that DGX OS ships (`libcudart.so.13`, `libcublas.so.13`), and about 9 GB of disk. ```sh git clone https://github.com/ollaya-dev/ollaya && cd ollaya T=$PWD/gb10 && mkdir -p $T/model # 1. llama.cpp b11146 from ggml-org, sha256-pinned: the CPU libraries and the CUDA backend sh scripts/llama-cpp.sh linux-arm64 $T/lib/llama sh scripts/llama-cpp.sh l...
Adjacent Repository Pain Points
Other highly discussed features and pain points extracted from ollaya-dev/ollaya.
Extracted Positioning
Integration of `snap1-2b` (MiniCPM5-2B + LoRA) into Ollaya, leveraging GGUF format for efficient deployment.
Achieving high accuracy (0.655-0.660) with a compact 2B parameter model, optimized for one-forward-pass decision-making. Strict adherence to prompt versioning and byte-for-byte parity checks ensures consistent, reliable model behavior across different environments. Prioritizing Q8_0 quantization for broader compatibility while offering Q4_K_M for smaller machines.
Top Replies
@emnlmn, thanks, this is a clear proposal and it fits the GGUF path well. Like Cygnet, it reads letter logits after one prompt, so it is one forward pass per question. We would like to add it. How ...
@cobanov thanks, this plan works for me. Updates since your reply: - **Quantization**: we measured Q8_0 against Q4_K_M on typed-decisions (snap 0.5.0, M1 Max): 0.655 vs 0.654 accuracy, so Q4_K_M lo...
@emnlmn, thanks for the numbers and the v0.5.0 notes. Plan: - **Prompt.** We port v0.5.0 (`PROMPT_VERSION` 6). - **Quantization.** The default tag will be Q8_0: our rule is Q8_0 wherever it fits, a...
Extracted Positioning
Feature request for integrated local training capabilities (`ollaya train`) for System 1 decision models, specifically using LoRA, to allow users to fine-tune models on proprietary datasets.
Ollaya maintains its core identity as an "ultra-fast, calibrated in-process runtime" for inference, strictly avoiding bloat from training dependencies. The platform aims to facilitate the *running* of custom-trained models by defining an import path for LoRA adapters and calibration parameters, rather than integrating the training process itself. This ensures a lightweight core while supporting user customization.
Top Replies
To follow up on this proposal, here are some thoughts on how the architecture could be cleanly designed **without bloating the core Ollaya runtime**: ### 1. Absolute Rule: Zero Bloat in Core Ollaya...
@solarpush, thanks for the proposal and the follow-up on keeping the core lean. Sorry it took a while to answer. We have decided not to put training into Ollaya itself. Ollaya runs authors' models ...
Extracted Positioning
Integration of Decima, a 122M parameter System One decision model, into the Ollaya library.
Targeting low-end CPU inference with high performance (~20 ms per decision on one x86 core, int8), supporting TypeSafe API for `choice`, `noul`, and `score` questions. Emphasizes local, efficient decision model serving.
Frequently Asked Questions
Market intelligence mapped to Request for aarch64 CUDA support for Ollaya, specifically for NVIDIA GB10 GPUs on DGX Spark platforms..
What problem does Request for aarch64 CUDA support for Ollaya, specifically for NVIDIA GB10 GPUs on DGX Spark platforms. solve?
Based on our AI analysis of the original developer request, its primary technical positioning is: Ollaya aims to provide GPU acceleration across diverse architectures, but prioritizes verification on real hardware. It leverages existing `llama.cpp` CUDA builds for GGUF models (e.g., `winnow`) as a realistic first step, while acknowledging the greater effort required for ONNX models due to lack of official aarch64 CUDA builds from Microsoft. The core principle is rigorous "parity gate" testing before shipping.
How is the developer community reacting to Request for aarch64 CUDA support for Ollaya, specifically for NVIDIA GB10 GPUs on DGX Spark platforms.?
Yes, we have tracked 3 direct responses and active debates regarding this specific topic originating from GitHub Issue.
What are the foundational technologies related to Request for aarch64 CUDA support for Ollaya, specifically for NVIDIA GB10 GPUs on DGX Spark platforms.?
Our proprietary extraction maps Request for aarch64 CUDA support for Ollaya, specifically for NVIDIA GB10 GPUs on DGX Spark platforms. to adjacent architectural concepts including aarch64 CUDA support, DGX Spark, nvidia gb10, unified ram.
Engagement Signals
Cross-Market Term Frequency
Quantifies the cross-market adoption of foundational terms like llama-server and real hardware by tracking occurrence frequency across active SaaS architectures and enterprise developer debates.