MODEL PROFILE

gemma-4-26B-A4B-it-NVFP4

cklaus · gemma-4-26B-A4B-it-NVFP4

NVFP4-quantized version of google/gemma-4-26B-A4B-it in **modelopt format**, optimized for vLLM on NVIDIA Blackwell (SM120) GPUs. Other available NVFP4 quantizations of this model quantize attention layers too, which causes: 1. Quality degradation from aggressive attention quantization 2. vLLM's QKV fusion scale bug (fused projections take `max()` of global scales, causing underflow) 1. Started from RedHatAI/gemma-4-26B-A4B-it-NVFP4 (compressed-tensors format) 2. Dequantized all 115 self-attention projections (q/k/v/o across 30 layers) back to BF16 using FP4-E2M1 lookup table 3. Converted from compressed-tensors to modelopt format: - `weight_packed` -> `weight` (same data) - `weight_global_scale` (divisor) -> `weight_scale_2` (1/divisor) - `input_global_scale` (divisor) -> `input_scale` (1/divisor) 4. Updated quantization config to match NVIDIA's modelopt pattern with proper `exclude_modules`

Text generationlicense:apache-2.0
Context windowNot disclosed
Input → OutputText → Text
Model weightsNot verified
Profile updated2026/10/30 22:07:22
OVERVIEW

Model overview

NVFP4-quantized version of google/gemma-4-26B-A4B-it in **modelopt format**, optimized for vLLM on NVIDIA Blackwell (SM120) GPUs. Other available NVFP4 quantizations of this model quantize attention layers too, which causes: 1. Quality degradation from aggressive attention quantization 2. vLLM's QKV fusion scale bug (fused projections take `max()` of global scales, causing underflow) 1. Started from RedHatAI/gemma-4-26B-A4B-it-NVFP4 (compressed-tensors format) 2. Dequantized all 115 self-attention projections (q/k/v/o across 30 layers) back to BF16 using FP4-E2M1 lookup table 3. Converted from compressed-tensors to modelopt format: - `weight_packed` -> `weight` (same data) - `weight_global_scale` (divisor) -> `weight_scale_2` (1/divisor) - `input_global_scale` (divisor) -> `input_scale` (1/divisor) 4. Updated quantization config to match NVIDIA's modelopt pattern with proper `exclude_modules`

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

PRICING & COST

Source prices & cost analysis

Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.

Source / channelInputOutputBilling unitCollected at
No verified prices yet. Check the source channel.

Estimate monthly base token costs

—

Estimates exclude cache discounts, tiered pricing, multimodal surcharges, web search, taxes and additional reasoning output.

INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

View full indexed rankings →

Frequently asked questions

Do open weights permit free commercial use?

Not necessarily. Check the version license, commercial conditions and usage policies. Self-hosting incurs compute and maintenance costs.

Are these real-time billing prices?

These are timestamped source snapshots, not live billing prices. Check the channel for current versions, regions, discounts and billing methods.

Sources & traceability

许可证:apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。