Apx machine learning

ApX Machine Learning is a platform for creating and deploying large language models and machine learning models, emphasizing automated processes, with its core functionality centered on "model first, then rent GPUs," that is, quantitatively predicting the deployment constraints of open-weight LLMs before actually purchasing or renting compute resources.

I. Purpose The platform's main purpose is to help engineering teams and researchers estimate, before configuring GPU clusters, whether a model can run stably on target hardware at the expected context length and concurrency, thereby avoiding trial and error and cost waste caused by insufficient VRAM, KV cache exhaustion, sharp increases in concurrency latency, or interconnect bandwidth bottlenecks. The official text repeatedly emphasizes: open weights are free, GPU time is not.

II. Main Capabilities

1. VRAM and cost modeling: calculates precise VRAM usage, hourly cost, and supports comparison across hardware configurations. In the example, under Llama-3-70B FP8, 2x H100, 32k context, and 4 concurrent users, the weights are 65.7 GB, active KV is 20.0 GB, and the available pool is 74.3 GB, accounting for 53.6% of the 160 GB of 2x H100.
2. KV cache and concurrency simulation: simulates dynamic KV cache growth, interconnect bottlenecks, and concurrency latency cliffs, which the official description says reaches the accuracy of physical benchmarks. The example gives a concurrency ceiling of 17 users and 74.3 GB of dynamic headroom.
3. Latency and throughput prediction: predicts p50 and p95 time to first token (TTFT) and cluster throughput. In the example, p50 is 3.75s, p95 is 3.83s, and cluster throughput is about 156 tok/s.
4. Operating point and saturation analysis: identifies the "safe operating inflection point," explaining that beyond the saturation batch size total throughput tends to flatten while per-user latency continues to rise; and points out that INT4 quantization can reduce the batch size at which throughput saturates to one quarter, increasing serving capacity.
5. Quantization and interconnect analysis: explains that weight quantization does not affect the KV cache, and that KV cache quantization can reduce per-user memory usage and unlock larger concurrent batch sizes; it also compares the throughput scaling curves of NVLink and PCIe under tensor-parallel deployment, pointing out that more GPUs are not necessarily faster and that interconnect bandwidth limits scaling.
6. Architecture specification data: provides structured parameters of open-weight models, including attention mechanisms, hidden dimensions, number of layers, context windows, and hardware requirements under each quantization format.
7. Validation and transparency: prediction results are validated against a physical benchmark regression suite across hardware architectures, quantization methods, and concurrency levels, andPublic tracking and release differences.
8. Course content: provides a 30-chapter masterclass on building large language models, as well as introductory courses on Transformer models, practical quantization for large language models (PTQ, QAT, GPTQ, GGUF), and efficient inference deployment of quantized LLMs.
9. Analysis articles: includes VRAM benchmark comparisons for training large language models on consumer-grade and data center GPUs (such as full fine-tuning on A100 versus 4-bit QLoRA on RTX 4090), as well as measured benchmarks for KV cache exhaustion when running Llama 3.1 8B with vLLM, explaining the reasons for preemption stalls triggered by exceeding capacity and the 85% safety inflection point protecting SLA.

III. How to use Users input parameters such as model, quantization format, hardware configuration, context length, and number of concurrent users through the platform to obtain projection results such as VRAM breakdown, concurrency upper limit, TTFT distribution, estimated throughput, and hourly cost. The official text provides the path "Get a projection before the bill," that is, obtain a projection first before generating a bill. The platform also supports assessing whether pipeline parallelism, multi-node topology, or production traffic simulation is needed. The specific operation interface and registration process are not provided in the materials.

IV. Applicable scenarios Suitable for engineering teams and researchers who need GPU cluster selection and capacity planning for open-weight LLMs, as well as developers who want to learn large language model construction, Transformer architecture, quantization, and inference deployment. It is officially stated to be used by engineers and researchers at the world's leading technology companies and universities.

V. Limitations and information not provided The materials do not provide specific pricing, plans, free quotas, a complete list of supported models, API details, platform access methods, registration requirements, or quantitative error metrics for prediction accuracy. Capabilities such as KV cache quantization, INT4 quantization, and NVLink/PCIe comparison are pointed to in the text in the form of "Verify" for further verification, and specific implementation details are not provided for now.