DeepSeek V4.1 Flash hands-on tests and comparisons

Local testing of DeepSeek V4.1 Flash 1017 on two Radeon AI R9700 GPUs with 128GB DDR5 via Strata produced speeds the author calls impractical.

A Japanese user says the DeepSeek V4.1 Flash 1017 weight download started in the daytime and still hasn't finished, noting local LLMs are costly.

The author compares Mac unified memory with single-GPU setups and lists current local-LLM hotspots, including DeepSeek V4.1-1017, debating how usable IQ1–IQ4 quantizations are.

A roundup says DeepSeek shipped V4.1-Flash-1017 weights under MIT, open-sourced its DeepInfer serving engine, and is reportedly raising about $15B.

The release ships IQ1_M at 239GB, IQ2_XXS at 257GB and IQ2_XS at 274GB quantized weights, published on Hugging Face.

A ranking of coding setups puts Claude Code + Opus 5.5 best overall and Codex + DeepSeek-V4.1-Flash at one-twentieth the cost.

On an independent Bug Hunt benchmark counting only targeted fixes across 105 planted bugs in two production repos, V4.1 Flash 1017 scored 42.7 at $1–2, versus Kimi K3.1 Pro's 36 at nearly $14.

A user is asking who can supply DeepSeek V4.1 Flash-1017 at 500+ tokens per second for an AI agent swarm platform, with no benchmark data shared.

Changing only the decode kernel with megakernels on 4x MI325X raised DeepSeek V4.1 Flash-1017 P90 output from 192.5 to 436.3 tok/s per user with no accuracy loss.

The open-source Project Maya runs DeepSeek V4.1 Flash-1017 (748B MoE) privately on 1 to 16 NVIDIA or AMD GPUs across Linux and Windows, with an OpenAI-compatible server and dashboard.

A developer switched from Opus to DeepSeek V4.1 Flash for cost and built a 3D interactive hot-rolling workshop page, calling the result decent.

A user demonstrates DeepSeek-V4.1-Flash running locally on a 64GB RAM machine using Strata with GSQ-RCO quantizations.