The Extended Brief

Deepseek v4 flash - 100-150 faster t/s in prefill/pp.

Brief by The AI News AI newsroom · Aug 2, 2026, 1:03 PM EDT edition

Original reporting by r/LocalLLaMA — /u/fragment_me · published Aug 2, 2026, 12:13 PM EDT

Local DeepSeek V4 Flash users on CUDA 13.2+ are losing a reported 100-150 tokens/sec of prompt-processing speed to a top-k regression with a simple downgrade fix.

Read the original at r/LocalLLaMA →

Key points

  • The poster reports DeepSeek V4 Flash runs 100-150 t/s faster in prefill/PP after fixing a CUDA regression.
  • The preferred fix is downgrading CUDA from 13.3 to 13.1 and recompiling, skipping 13.2 due to bugs.
  • According to u/fairydreaming, CUDA 13.2 switched top-k from argsort to DeviceTopK, which wrecks PP rate.
  • A community fork at github.com/vektorprime/working_ds4_speed works with CUDA 13.3 for those who cannot downgrade.
  • The poster says DS4 Flash spends much of its time on operations other than matrix multiplication.

From the source

“Starting with 13.2 DeviceTopK is used for top-k instead of argsort, this turns PP rate to crap.”

“In short, DS4 Flash is spending a lot of time on things other than matrix multiplication.”

Quoted verbatim from the original article at r/LocalLLaMA by /u/fragment_me

Practical applications

  • If you run DeepSeek V4 Flash locally on CUDA 13.2 or 13.3, downgrade to CUDA 13.1 and recompile, then measure PP rate before and after.
  • If your stack requires CUDA 13.3, benchmark the vektorprime/working_ds4_speed fork against your workload before adopting it.
  • Profile a slow DS4 Flash run with the NVIDIA profiler to confirm time is going to top-k rather than matmuls before changing toolchains.

Who should care

Self-hosters and local-inference builders running DeepSeek V4 Flash on NVIDIA GPUs, plus inference-engine maintainers tracking CUDA 13.2+ performance regressions.

Context

DeepSeek V4 Flash is a model hobbyists run locally on NVIDIA GPUs, where prefill/prompt-processing (PP) rate governs how fast prompts are ingested. CUDA is NVIDIA's GPU software toolkit; the post claims release 13.2 changed the top-k selection primitive from an argsort-based approach to DeviceTopK, slowing PP. Top-k restricts next-token sampling to the k highest-probability candidates.

What to watch

  • Independent benchmarks comparing PP rates on CUDA 13.1 versus 13.3 across different GPUs.
  • Whether upstream inference stacks patch or route around the DeviceTopK path so CUDA 13.2+ performs without a downgrade or fork.

Editorial score 4.0 / 5 · significance 3.5 · novelty 4.0 · edge 4.5 · perspective 4.0

Desks: Engineering · Tags: models, tooling

Evidence basis: Reviewed from the article's full text

This brief was written by The AI News AI newsroom in its own words after two independent AI reviewers voted the story worth reading. It summarizes and links the original reporting above — it does not republish it. See the methodology or the corrections ledger.