Observed Signal · Jun 20, 2026 · Benchmark · Source: DEV Community · Impact: 3/5 · Sentiment: Positive
GB10 Benchmarks DiffusionGemma 26B at 32K Context
A developer published a technical benchmark showing NVIDIA GB10 (Grace Blackwell, 128 GB unified memory) running nvidia/diffusiongemma-26B-A4B-it-NVFP4 with vLLM 0.22.1rc1. The GB10 peak generation throughput reached ~155 tokens/sec (at 512-token outputs) and successfully supported long contexts up to ~32,600 tokens (model limit 32,768) when deployed with --max-model-len=32768. Compared to an NVIDIA GH200 system, GB10 is slower on raw throughput (roughly 1/8 peak throughput vs GH200) but delivers comparable maximum context length at a much lower cost and power profile. The post also documents Podman deployment pitfalls (CUDA graphs warmup OOM, Podman GPU flags, CNI DNAT residues, CNI plugin paths) and tuning tips (gpu-memory-utilization=0.7, --max-num-seqs=4) to avoid OOMs.
Provides actionable LLM inference performance and deployment guidance for a new NVIDIA GB10 platform (32K context capability and Podman/OOM workarounds), relevant to teams choosing local inference hardware and tiered inference architectures.
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Key Takeaways & Evidence Grounding
- Platform: NVIDIA GB10 (Grace Blackwell) with 128 GB unified memory.
- Framework: vLLM 0.22.1rc1; Model: nvidia/diffusiongemma-26B-A4B-it-NVFP4.
- Peak generation throughput on GB10: ~155 tok/s (observed at 512-token outputs).
- GB10 reached maximum usable context of ~32,600 tokens (model limit 32,768) when --max-model-len=32768.
- Deployment notes: use Podman with --device nvidia.com/gpu=all; reduce gpu-memory-utilization to ~0.7 and limit --max-num-seqs (e.g., 4) to avoid CUDA graphs warmup OOMs.
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