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Neural Nova – GPU optimization benchmarks for LLM workloads

Neural Nova published GPU optimization benchmarks for LLM workloads, reporting throughput and cost gains across four models running on vLLM. Qwen3-235B-A22B on 8× NVIDIA H100-80GB posted +138.7% token/s and +58% cost savings, while GLM-5.2 on 8× AMD Instinct MI325X posted +26.8% token/s and +21.1% cost savings. Gemma-4-31B-it on 1× NVIDIA H100-80GB gained +66.0% token/s with +40% cost savings, and GPT-OSS-120B on 1× NVIDIA H100-80GB gained +24.5% token/s with +20% cost savings.

read1 min views3 publishedSep 10, 2026

Explore Optimized AI models recipes across GPUs, frameworks,

and deployment configurations.

BENCHMARKED MODELS

REASONING

vLLM · 8× NVIDIA H100-80GB

Qwen3-235B-A22B

+138.7% token/s

+58% Cost Savings

Qwen3-235B-A22B · benchmark REASONING

vLLM · 8× AMD Instinct MI325X

GLM-5.2

+26.8% token/s

+21.1% Cost Savings

GLM-5.2 · benchmark

MULTI-MODAL

vLLM · 1× NVIDIA H100-80GB

Gemma-4-31B-it

+66.0% token/s

+40% Cost Savings

Gemma-4-31B-it · benchmark OPEN-WEIGHT

vLLM · 1× NVIDIA H100-80GB

GPT-OSS-120B

+24.5% token/s

+20% Cost Savings

GPT-OSS-120B · benchmark PRODUCTION-READY PERFORMANCE

Benchmark with confidence. Deploy with clarity. #

Use validated performance data to select the right model for your production workload. Talk to Our Team

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