Cloudflare Python Workers GA, HF wires GGUF Metal, tokenizers v1 hits 15.9x

Sources

Cloudflare Python Workers are now generally available simonwillison.net

Cloudflare Python Workers are now generally available After a two year preview, Cloudflare’s support for running Python code in their server-side Workers platform is now stable: “Python is now a first-class, fully supported language on the Cloudflare Developer Platform”. A neat thing about this is how it works. Cloudflare are running Python compiled to WebAssembly via Pyodide in their V8-based workerd runtime. This comes with some limitations, documented here - most notably both multiprocessing…

Transformers now runs llama.cpp quants huggingface.co

tokenizers v1: encode, decode and scaling, measured huggingface.co

Pruning LLMs Like a Physicist: Block Removal as an Ising Optimization Problem huggingface.co

Researchers recast transformer block removal as an Ising optimization, borrowing statistical-physics methods to decide which layers to cut. The approach treats each block as a spin variable, letting solvers find pruning configurations that preserve accuracy better than greedy layer-drop heuristics used in prior compression work.

MCP was always a bad idea? simonwillison.net

MCP looks pointless if you hand a terminal agent unrestricted API access, Willison concedes, but it earns its keep the moment you want scoped service access, key isolation from the model, user-facing auth flows, and audit logs — the plumbing non-YOLO deployments actually need.

References

Cloudflare blog — Python Workers advancements blog.cloudflare.com

Cold starts drop from roughly 10 seconds to about 1 second by snapshotting WebAssembly linear memory after top-level imports at deploy time.

Wasmer blog — Python on the Edge wasmer.io

Cold starts as low as 60ms are technically feasible; Cloudflare’s ~1s Python cold start remains a significant barrier for latency-sensitive edge workloads, and users are locked to the specific Pyodide version embedded in workerd.

explainX — Python Workers GA analysis explainx.ai

Cloudflare patches the network stack to route through the JavaScript fetch API and ties Python’s async loop to the JS event loop; libraries like urllib3 exhibit redirect and security behaviors that diverge from CPython semantics.

Cloudflare Workers pricing docs developers.cloudflare.com

Worker size limit is 64 MiB compressed on both Free and Paid plans; each isolate is capped at 128 MiB RAM, and CPU-ms (not wall-clock) is billed at $0.02 per million.

TechBytes — Edge WASM CPython guide 2026 techbytes.app

Python 3.13 reached Tier-2 support for wasm32-wasi and Deno Deploy uses the WebAssembly Component Model (componentize-py) to achieve microsecond-level instantiation — a different bet than Cloudflare’s embedded-Pyodide approach.

MachineLearningPlus — tiktoken vs Hugging Face tokenizers machinelearningplus.com

For pure encoding tasks, tiktoken historically outperformed older open-source libraries by 3–6 times… the choice of vocabulary often impacts performance more than the library itself; a vocabulary with fewer merges to apply can appear ‘faster’ regardless of the engine.

Hacker News — TokenDagger discussion (item 44422480) news.ycombinator.com

TokenDagger… reports a 4x speedup on code sample tokenization and 2x throughput on >1GB NLP files versus tiktoken, using a JIT-compiled PCRE2 engine and bypassing regex for special tokens entirely.

vLLM optimization documentation docs.vllm.ai

vLLM V1 reports up to a 5x improvement in latency and a 1.7x boost in total throughput over V0, with faster tokenization letting the engine transition sooner to GPU-bound KV cache generation and directly lowering TTFT.

Hugging Face discuss forum — ‘Tokenizer is splitting special token’ discuss.huggingface.co

Users report edge cases where special tokens are unexpectedly split by the tokenizer, and rare (~1 in 1000) ‘gibberish’ decode outputs suggest edge-case stability in the decoding path remains under active development.

arXiv 2507.11941 — tokenizer membership inference research arxiv.org

Tokenizers can be exploited for membership inference attacks: the specific way a tokenizer handles text can reveal whether that data was included in the model’s original training set.

arXiv 2604.11518 — BPE algorithmic analysis arxiv.org

Newer Rust implementations achieve linear-time O(n) BPE encoding versus the quadratic worst-case behavior of some legacy implementations; ports of minbpe using PyTorch+CUDA report 55x–108x speedups over the original code.

local-llm.net (llama.cpp vs MLX comparison) local-llm.net

llama.cpp achieved roughly 85 tok/s on Llama 3.2 3B (Q4_K_M), while Apple’s MLX reached 88 tok/s [on M4 Max, 64GB]

GitHub discussion, ik_llama.cpp github.com

llama.cpp frequently outperforms MLX in prompt processing (prefill) and long-context scenarios by up to 50% when Flash Attention is enabled

Hugging Face docs — Quantization/GGUF huggingface.co

By default, the library employs a legacy loader that dequantizes GGUF blocks into full-precision (FP32 or BF16) tensors during the loading phase to ensure compatibility with standard PyTorch operations

r/LocalLLaMA — Qwen3 MoE expert-count hacking reddit.com

expanding the active expert count… in the later transformer layers reported an 8.5% reduction in reasoning tokens and a 10.9% drop in latency without a loss in accuracy

shartech.cloud — ggml.ai / Hugging Face partnership blog.shartech.cloud

In February 2026, Hugging Face officially acquired ggml.ai, the founding team behind the pervasive llama.cpp and whisper.cpp libraries… the projects would remain under the MIT license and that the original team would maintain full technical autonomy

Hugging Face docs — Metal quantization backend huggingface.co

certain Metal kernels, such as the Scaled Dot Product Attention (SDPA) implementation, do not guarantee bit-for-bit reproducibility; they match standard PyTorch outputs only about 97% of the time due to differences in operation reordering