DeepSeek-V4-Flash vs. GLM-5.3-Flash on 2× DGX Spark

I've tried both and been having this debate with myself for the last few days, on two Asus Ascent GX10s (effectively the same as 2x DGX Spark): DeepSeek-V4-Flash-0731 (official weights) GLM-5.3-Flash (RedHatAI/GLM-5.3-Flash-NVFP4) Have any of you guys also tried both on this hardware (2x DGX Spark / Asus Ascent GX10), and what are your use cases and findings? DeepSeek runs with more tokens/s… but GLM feels like the better tool for how I actually work. I'll share my experience. Where DeepSeek wins: It feels better, because it's the original official weights. Much faster token generation, though not actually faster to a final result. It's excellent at open-ended research, pulling and chaining sources on its own. Huge context (~1M tokens). Where GLM wins: Comes to conclusions faster, and often gets them more right. Much better writing, especially in languages other than English and Chinese. That matters a lot for writing letters. Much better at "extracting the essence of a text". Superb vision. DeepSeek's Vision-Exp model can read images just well enough to score on benchmarks, but not nearly well enough for real OCR / text extraction (its vision input is token-limited to 384 tokens, which renders images unsharp and text unreadable). Considerably better benchmark results, at least for the full model, though I'm running a quantized build, so the numbers may not carry over directly. But my empirical results prove it gets better results with fewer instructions. Hallucinates much less. That's the decisive one for me: I can't run a "test and improve" loop on text like you could on code, so office work and letters depend on one-shot accuracy and a low hallucination rate. The core problem: there's no GLM build that runs on 2x DGX Spark with official weights. I wish there were. So I keep testing and tweaking the GLM setup to get rid of the artifacts (which should be possible, per various sources). It feels a bit more messy than the DeepSeek setup, but if I manage to configure it correctly it should give more reliable outputs, with far better vision on top. submitted by /u/kuhunaxeyive [link] [comments]

[[benchmark-llamacpp-batchubatch-impacts-on-pp-and-tg|[Benchmark] llama.cpp batch/ubatch impacts on PP and TG]]
Serving Deepseek v4 Flash 0731 on 2x DGX Spark — 5-7 GB OS headroom, what would you do to lower VRAM usage and increase OS available RAM?
Running DeepSeek-V4-Flash-0731 (155 GB MoE) on a DGX Spark with vLLM-Moet 2-bit quantization - AI's narrative
DeepSeek-V4-Flash-0731 (284B MoE) at 75 tok/s on 2× DGX Spark — full recipe, 11 gotchas, reboot-proof cluster, Codex CLI integration

Source: r/LocalLLaMA | 2026-09-03

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