DeepSeek V4 Flash on Dual RTX PRO 6000 Outpaces Claude Sonnet and Opus in Real Coding Tasks
The latest follow‑up from the LocalLLaMA community shows that a locally hosted DeepSeek V4 Flash model, running on two RTX PRO 6000 GPUs via vLLM, can complete real‑world coding challenges in roughly two minutes per task while matching the…
The latest follow‑up from the LocalLLaMA community shows that a locally hosted DeepSeek V4 Flash model, running on two RTX PRO 6000 GPUs via vLLM, can complete real‑world coding challenges in roughly two minutes per task while matching the quality of Claude Sonnet 5. By contrast, the same tasks take about six minutes when sent to the Sonnet or Opus APIs, with Opus and a model dubbed “Fable” still holding a slight edge in raw quality but at a considerably higher latency [13]. This result is more than a speed tweak; it demonstrates that a well‑optimized, self‑hosted model can rival—or beat—proprietary API offerings on practical workloads, reinforcing the “off the thumb” premise that powerful AI need not always flow through the largest cloud providers.
The achievement builds on a series of incremental gains that have been quietly accumulating in the local‑inference space. Earlier experiments with DeepSeek V4 Pro at home reported measurable speedups after applying a custom llama.cpp branch packed with fixes and optimizations not yet merged upstream [1]. While those benchmarks were still in progress, they hinted at the same direction: tighter kernels, better batching, and reduced overhead can shave precious seconds off generation time. The V4 Flash result takes that momentum further, showing that when the model is paired with sufficient GPU horsepower—here, two RTX PRO 6000 cards—and served through an efficient inference engine like vLLM, the wall‑clock advantage becomes large enough to change the economics of everyday developer workflows.
Throughput numbers from another recent experiment reinforce the picture. Running GLM‑5.2 with NVFP4 quantization on four DGX Sparks yielded a stable ~24 tokens per second at a 128 K context window, a regime where earlier attempts forced a trade‑off between context length and speed [15]. Although that setup targets a different model and hardware class, it underscores a broader trend: quantization, mixed‑precision formats, and purpose‑built inference stacks are unlocking usable performance even when models are pushed to long‑context regimes. The DeepSeek V4 Flash work benefits from similar optimizations—vLLM’s paged attention, efficient KV cache management, and likely custom CUDA kernels—allowing it to maintain low latency without sacrificing the ability to handle the multi‑turn, code‑centric prompts typical of software‑engineering assistants.
Of course, raw speed is only part of the story when deploying LLMs locally. Resource contention can quickly erode gains if other applications lay claim to GPU memory. A quick audit of common desktop tools shows that a few resident programs can silently reserve gigabytes of VRAM: Discord holds roughly 450 MB, Steam about 200 MB, and Telegram around 150 MB, with several others pushing the total toward the 1 GB mark [1