GLM-5.2 Shifts Agentic Coding Economics to 1/6th Frontier Cost
Z.AI's open-weights GLM-5.2 hits 81% on Terminal-Bench, undercutting GPT-5.5 for a fraction of the cost. Here's the benchmark breakdown.
#benchmarks
11 posts
DiffusionGemma Puts 1000+ tok/s on an RTX 5090
Google's open 26B diffusion model hits 700+ tok/s on consumer GPUs with day-zero vLLM support. Here's what the bidirectional architecture changes for local inference.
Apple Silicon Just Cracked the 150B Wall at 56 tok/s
Rapid-MLX's new engine optimizations push DeepSeek V4 Flash 158B-A13B to 56 tok/s on a Mac. Here are the exact numbers and the tradeoffs.
Your 12GB MTP Throughput Just Jumped 23%
A community llama.cpp fork squeezes 110 tok/s out of Qwen3.6-35B-A3B MTP on a 12GB card. Here are the exact flags and the VRAM trick to make it fit.
Your Local Throughput Just Doubled (With No Accuracy Tax)
Multi-token prediction is merged into llama.cpp, delivering 1.4–2.2x throughput on Qwen3.6 with zero accuracy loss.
MTP in llama.cpp Adds 24–33% Throughput on Qwen3
llama.cpp merged Multi-Token Prediction for Qwen3. Community benchmarks show 38→47 tok/s on RTX 3090 and 63→84 tok/s on RTX 5090 — no new hardware needed.
DFlash Decode Collapses at Long Output. MTP Doesn't.
One llama.cpp flag—--spec-draft-p-min 0.75—turns MTP from a dud into a decode speed that holds flat across output lengths where DFlash falls apart.
One llama.cpp Flag Turns MTP From Dead Weight to 68% Faster
The --spec-draft-p-min filter in llama.cpp PR #22397 rescues MTP for Qwen3.6-27B: 48.9 tok/s vs 29 tok/s at 2000 tokens on a 24GB card.
Your vLLM Thinking Budget Was Doing Nothing With MTP On
vLLM 0.21.0 shipped Friday with a quiet fix: thinking_token_budget was being silently ignored when MTP speculative decoding was enabled. If you serve reasoning models with spec decode, you have been paying for it.
Claude Code v2.1.100+ Burns ~20K Phantom Tokens Per Request
A server-side bug in Claude Code v2.1.100+ inflates every request by roughly 20K cache_creation tokens — about 40% overhead. Pin v2.1.98 until fixed.
A 27B Model on a Single GPU Is 10 Points Off Claude Opus 4.7
Qwen3.6-27B running locally now scores within 10 points of frontier closed models on SWE-bench Verified. The benchmark table, lined up side by side.