Social·Community
FP8/BF16 users seeing someone run IQ1_S
Community meme mocks FP8/BF16 users' reactions to IQ1_S quantization
7 engagement1 sourceSat, Oct 3, 2026, 11:47 AM
AI Overview
- A Reddit post asks why FP8/BF16 users react with horror to 4-bit quantization formats like FP4/INT4 and someone running IQ1_S
Related
Releases·Model Release
Kimi K3 Neuron IQ1_S GGUF quant ships at 308GB, keeps all experts, matches full model on HumanEval
A new IQ1_S GGUF quantization of the Kimi K3 Neuron model was released, weighing ~308GB while retaining all experts, roughly 40% smaller than the existing Unsloth 594GB version. It achieves 94.5% on HumanEval, matching the full model, and runs at 12.5 tokens/s on 3× DGX Sparks.
A new IQ1_S GGUF quantization of the Kimi K3 Neuron model was released, weighing ~308GB while retaining all experts, roughly 40% smaller than the existing Unsloth 594GB version. It achieves 94.5% on HumanEval, matching the full model, and runs at 12.5 tokens/s on 3× DGX Sparks.
1 engagement1 sourceredditWed, Aug 12, 2026, 10:43 PM
Releases·Model Release
VeriLoop E2 GGUF Release: BF16 → IQ1_M, 50.11 GiB → 16.79 GiB with +0.319% PPL drift
The VeriLoop team released a complete GGUF precision ladder for VeriLoop E2, spanning a canonical BF16 reference (50.11 GiB) to a mixed-precision IQ1_M deployment tier (16.79 GiB). Every tier was evaluated against the same frozen BF16 reference, with low-bit variants keeping a protected higher-precision spine rather than uniformly downquantizing. The release gives practitioners concrete quality/size tradeoffs for local deployment.
The VeriLoop team released a complete GGUF precision ladder for VeriLoop E2, spanning a canonical BF16 reference (50.11 GiB) to a mixed-precision IQ1_M deployment tier (16.79 GiB). Every tier was evaluated against the same frozen BF16 reference, with low-bit variants keeping a protected higher-precision spine rather than uniformly downquantizing. The release gives practitioners concrete quality/size tradeoffs for local deployment.
2 engagement1 sourceredditFri, Sep 25, 2026, 01:29 PM
Developments·Benchmark
Qwen3.8-27B hits 29/30 on AIME 2026 with FP8 xhigh reasoning
A Reddit user benchmarked Qwen3.8-27B on the MathArena AIME 2026 dataset, comparing BF16 and FP8 weights at medium and xhigh reasoning effort. The FP8 quantized model with xhigh reasoning scored 29/30, matching BF16 xhigh while offering better speed. The results suggest FP8 quantization can be as effective as BF16 for math reasoning tasks.
A Reddit user benchmarked Qwen3.8-27B on the MathArena AIME 2026 dataset, comparing BF16 and FP8 weights at medium and xhigh reasoning effort. The FP8 quantized model with xhigh reasoning scored 29/30, matching BF16 xhigh while offering better speed. The results suggest FP8 quantization can be as effective as BF16 for math reasoning tasks.
103 engagement1 sourceredditThu, Aug 20, 2026, 06:59 PM
Developments·Paper
Beyond Accuracy: Prefix-Invariant Realizations of Low-Precision Fast Matrix Multiplication
An arXiv paper argues accuracy benchmarks fail to certify prefix invariance in low-precision fast matrix multiplication: rounding sums that mix token rows can leak later-token contributions into earlier LM outputs. On Qwen2.5-14B-Instruct, FP8 realizations changed multiple-choice likelihood answers on OpenBookQA even at ordinary-looking accuracy. The authors construct certified Strassen realizations on integer codes that guarantee bitwise equality to a row-local classical int8 operator.
An arXiv paper argues accuracy benchmarks fail to certify prefix invariance in low-precision fast matrix multiplication: rounding sums that mix token rows can leak later-token contributions into earlier LM outputs. On Qwen2.5-14B-Instruct, FP8 realizations changed multiple-choice likelihood answers on OpenBookQA even at ordinary-looking accuracy. The authors construct certified Strassen realizations on integer codes that guarantee bitwise equality to a row-local classical int8 operator.
0 engagement1 sourcearxivWed, Sep 30, 2026, 02:20 PM
Social·Community
Community asks AI labs to avoid BF16 tensors in GGUF quants for Strix Halo
A Reddit user appeals to AI labs producing quantized local language models to avoid BF16 tensors, which would benefit AMD Strix Halo users. They propose a new tag in GGUF file naming, similar to Unsloth's -UD-, to indicate the absence of BF16 tensors.
A Reddit user appeals to AI labs producing quantized local language models to avoid BF16 tensors, which would benefit AMD Strix Halo users. They propose a new tag in GGUF file naming, similar to Unsloth's -UD-, to indicate the absence of BF16 tensors.
22 engagement2 sourcesredditSun, Aug 23, 2026, 07:36 AM