Create

Sign in to ReadmeX

or

How we handle your data: Privacy Policy

ReadmeX

Your AI reading companion

What would you like to catch up on?

Pick a briefing, or ask your own question.

Your community and people briefings stay personal to you.

Nvidia RTX 3090

AI overview

Sign in and the AI will write an overview from our coverage.

Headlines · 4

  1. DeepSeek-V4.1-Flash benchmarked on dual DGX Spark hardware

    Nick Mermiges of PLI Labs benchmarked DeepSeek-V4.1-Flash-1017 at 2.75 bpw with EXL3 and vLLM across two NVIDIA DGX Spark systems. An EPYC system with two RTX 3090s handled Engram tables and speculative drafting. In the author's tests, reported as medians of three runs, prefill reached 2,618 tokens per second with a 256K prompt, while structured JSON output reached 322 tokens per second across four streams. The report published the code, launch scripts, benchmark tool and raw results, while noting lower prose throughput and slower decoding than some cited deployments. Planned 1M-token context, llama-benchy, BFCL and head-to-head KLD tests were not yet available, and the prefix-cache code was still pending publication.

    r/LocalLLaMA · 🔥 4
  2. Developer Runs DeepSeek-V4.1-Flash-1017 Q2 at 41 Tokens per Second

    A community developer reported running the DeepSeek-V4.1-Flash-1017 Q2 GGUF natively on Windows with one RTX 3090 and 192 GB of RAM, without WSL. On that system, the developer measured an average of 41 tokens per second and next-token agreement with DeepSeek's API of 90.2% on general text and 92.4% on code and mathematics. The developer also claimed the quantization fits the full 1M-token context and said the interface engine supports several published quantization sets.

    r/LocalLLaMA · 🔥 0
  3. Qwen3.8 27B Ties Opus 5.5 in User-Run Korean RAG Test

    A Reddit user evaluated Qwen3.8 27B and Opus 5.5 on Allganize’s Korean RAG dataset, which contains 300 questions and 64 PDFs, giving both models the same prompt and top five retrieved pages. With thinking enabled, Qwen3.8 27B scored 77.3%, versus 76.6% for Opus 5.5. When Opus 5.5 served as judge, the scores were 80.6% and 80.3%, respectively. The author reported a McNemar test result of p=0.82. The author also cautioned that this was a single run at temperature 0, that 11 verdicts changed on different hardware, and that 20 additional unanswerable questions were too easy because every model refused them. The one-point gap therefore was not treated as conclusive.

    r/LocalLLaMA · 🔥 0
  4. DeepSeek V4.1 Flash 2.61-bit quant runs at 26 t/s on dual RTX 3090s

    Reddit user whiteh4cker (r/LocalLLaMA) says they built DeepSeek-V4.1-Flash-1017-dsx-2.6bit, a 2.6-bit quantization made with lxe's dsx engine for programming and agentic tasks, and that the full weights would be uploaded within two hours of the post. The post self-reports that the 2.6127 bpw build scored 82.59% top-1 and 9.3626 perplexity against the FP8/FP4 teacher model, and reached 26 t/s generation and 170 t/s prompt processing at 262,144 context with Engram tables on two RTX 3090s with 192 GB DDR5. The author also notes the engine does not yet support images and that loading the 187.6 GB weight from an HDD takes 90–110 minutes.

    r/LocalLLaMA · 🔥 0

Experience and discussion from the community

Share my Nvidia RTX 3090 experienceAsk about Nvidia RTX 3090

Nobody has shared their experience with Nvidia RTX 3090 yet.