The Lightreason project combines technologies like Nvidia CUDA Graphs, Inco AI DFlash2, and FlashML FreeToken to accelerate open-weight model execution. Because the 34.66-billion-parameter Qwen model requires 64.56 GiB of memory, the runtime keeps non-active expert weights in system RAM and streams them dynamically to an RTX 4090. Meanwhile, the fully GPU-resident 3-billion-parameter variant maximizes throughput using compact KV caching and reused GPU preparation routines. The open-source repository provides all benchmark evidence and runner code for Linux systems running PyTorch 2.9.1 and CUDA 12.8.
Key points
Consumer-grade RTX 4090 hardware can run massive 35B-parameter models by selectively stre…: Consumer-grade RTX 4090 hardware can run massive 35B-parameter models by selectively streaming expert weights from system memory.
The original BF16 target model maintains full precision by verifying and making all final…: The original BF16 target model maintains full precision by verifying and making all final decisions on drafted tokens.
Deploying these optimizations requires a Linux environment configured with Python 3.12, P…: Deploying these optimizations requires a Linux environment configured with Python 3.12, PyTorch 2.9.1, and CUDA 12.8 alongside official model weights.
Sources checked by our analysis pipeline: 1
Lightreason is a local inference lab and interactive Studio for running Qwen faster on one RTX 4090. The experiments use the original BF16 weights for Qwen2.5-3B-Instruct (3.09B parameters) and the text model of Qwen3.6-35B-A3B (34.66B parameters).
- 3B · 63.0 → 139.6 tok/s across six prompts. LR CUDA graphs and compact KV matched all 30 Hugging Face timing runs. It also matched complete Python, JavaScript, and SQL responses in 9/9 new pairs. The separate PARD2 runner reached 58.3 → 525.2 tok/s on a complete matching weather response and 63.6 → 724.4 tok/s on the best matching 128-token Fibonacci slice. Performance · Coding benchmarks
- 35B · 7.93 → 51.08 tok/s on the capped Python prompt. The 6.44× comparison uses the same input across sessions. Output first differs from Scheduled at token 116. The packed configuration matched all 20 pairs against its closer fixed eight-position control. Performance · Coding results and output checks
The smaller model fits on the GPU, where reusing prepared GPU work and checking several tokens together saves time. The larger model’s text weights occupy 64.56 GiB. The…
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