Show HN: GPT-2 inference in pure C#, 0 bytes allocated per token
A new deep-learning engine in pure C# enables zero-allocation inference for GPT-2 models. It boasts predictable CPU performance and does not rely on native binaries or Python runtimes. The engine allows for efficient model training and inference, achieving competitive results with existing frameworks.
- ▪The engine supports loading and building models while ensuring zero-allocation CPU inference.
- ▪It can load GPT-2 Small weights from HuggingFace and achieves 0 bytes allocated per token during inference.
- ▪The framework allows for ONNX import, enabling direct loading of PyTorch-exported models.
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Story provenance
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Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/DevOnBike/Overfit |
| Publication time | Sun, 17 May 2026 19:17:22 +0000 |
| Retrieval time | 2026-05-17T19:33:20.883Z |
| Last seen | 2026-05-17T19:33:20.883Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | J0v-8BUqQNKK |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Overfit Pure C# deep-learning and optimization engine. Predictable CPU performance, explicit memory ownership, zero-allocation inference hot paths. No native binaries. No Python runtime. No ONNX Runtime dependency. What it does Train in PyTorch or .NET. Load or build a model. Run predictable, allocation-free inference in .NET. Zero-allocation CPU inference — preallocated buffers, no per-call GC pressure, competitive with ONNX Runtime. GPT-2 inference — load GPT-2 Small (124M params) weights from HuggingFace. KV-cache decode: 0 bytes allocated per token, O(N) scaling. Top-10 logit overlap 10/10 vs PyTorch, maxAbsDiff=0.000107. ONNX import — load PyTorch-exported models directly. 14 operators, branching DAGs (ResNet skip connections), output matches PyTorch within 1e-4.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.