Used the RT Cores on my RTX 5070 Ti for LLM routing — 218x speedup on a single consumer GPU

The News

A Reddit user on /r/deeplearning has sparked considerable interest in the AI community by detailing a novel approach to accelerating Large Language Model (LLM) routing using the ray tracing (RT) cores of an NVIDIA GeForce RTX 5070 Ti. The user claims to have achieved a 218x speedup in LLM routing performance by repurposing these traditionally graphics-focused cores. This unconventional application leverages the RT cores' parallel processing capabilities, typically reserved for ray tracing in gaming and rendering, to handle the complex routing decisions inherent in LLM inference. The post details an unspecified framework, though the user intends to release further details, including code, in the coming days [1]. If verified and widely applicable, this development could represent a significant breakthrough for consumer-grade LLM deployment and reshape edge AI processing [1].

The Context

The core innovation lies in recognizing the inherent parallelism within LLM routing, a process often bottlenecked by sequential execution. LLMs, as defined by Wikipedia, are computational models designed for natural language processing, relying on vast datasets and complex contextual relationships. Routing refers to directing incoming queries and generated tokens through the LLM's layers and modules [1]. Traditional CPU or standard GPU implementations of this process can become performance bottlenecks, especially as LLMs grow in size and complexity. The RTX 5070 Ti, as described by Wikipedia, uses NVIDIA's Blackwell architecture with fourth-generation RT cores optimized for hardware-accelerated ray tracing. These cores are massively parallel processors capable of handling thousands of independent calculations simultaneously. The user’s approach re-purposes this parallel processing power to handle routing workloads, bypassing the sequential limitations of conventional methods [1].

This development occurs amid rising demand for efficient LLM inference, particularly at the edge. The rise of generative AI, driven by models from Meta’s Superintelligence Labs [3], has intensified the need for accessible and performant LLM solutions. Meta’s recent unveiling of Muse Spark, described as "a ground-up overhaul of our AI efforts" [3], signals a commitment to democratizing advanced AI access. Simultaneously, the open-source community is booming, exemplified by SmolLM3-3B’s 1,095,987 downloads from HuggingFace. This proliferation of smaller models drives the need for innovative hardware acceleration techniques to enable efficient deployment on consumer-grade hardware. Tools like vllm (72,929 GitHub stars) and anything-llm (56,111 stars) highlight the community’s focus on optimizing LLM inference performance [4]. Arcee, a 26-person startup, has also gained traction with its high-performing, open-source LLM, particularly among OpenClaw users [4]. This underscores a trend toward smaller, more efficient models that can run on less powerful hardware, a trend the RTX 5070 Ti optimization directly addresses.

Why It Matters

The potential impact of this technique spans developers, enterprises, and the broader AI ecosystem. For developers, leveraging existing hardware in novel ways reduces reliance on specialized AI accelerators, lowering costs and simplifying deployment pipelines. This is especially relevant for smaller teams and independent researchers lacking dedicated AI infrastructure. The 218x speedup, if reproducible, could drastically reduce inference latency, enhancing user experiences for LLM-powered applications [1]. However, adapting codebases to utilize RT cores may introduce technical friction, requiring specialized expertise and limiting adoption among less technically proficient users.

Enterprises increasingly recognize the value of LLM-powered applications, particularly in customer service and content generation. VentureBeat’s analysis of LLM-referred traffic highlights a 30-40% conversion rate [2], demonstrating the effectiveness of AI-driven interactions. Optimizing for "Answer Engine Optimization" (AEO) or "Generative Engine Optimization" (GEO) requires significant infrastructure and expertise [2]. The RTX 5070 Ti optimization could offer a cost-effective solution for enterprises deploying LLMs at scale without dedicated AI hardware. Conversely, the need for specialized knowledge to implement this technique may create barriers for smaller businesses.

The winners in this ecosystem are likely those who can effectively leverage this optimization. NVIDIA, by showcasing the versatility of its RTX architecture, strengthens its position as a leading AI hardware provider. Open-source communities, empowered by accessible and performant LLM solutions, will continue driving innovation and democratizing AI access. Vendors of dedicated AI accelerators may face increased competition, potentially impacting their market share. The rise of smaller, more efficient models like those championed by Arcee [4] also challenges the dominance of resource-intensive models.

The Bigger Picture

This development aligns with a broader trend toward hardware-software co-optimization in AI. While specialized accelerators like Google’s TPUs and custom ASICs dominate high-performance computing, consumer-grade hardware is gaining traction for performance gains through innovative software techniques. Repurposing RT cores for LLM routing blurs the line between traditional graphics processing and AI acceleration, reflecting a shift toward edge AI, where processing occurs closer to data sources, reducing latency and improving privacy.

The emergence of this technique coincides with a surge in research focused on improving LLM efficiency. Recent arXiv papers, such as "SUPERNOVA: Eliciting General Reasoning in LLMs with Reinforcement Learning on Natural Instructions" and "Verify Before You Commit: Towards Faithful Reasoning in LLM Agents via Self-Auditing," highlight ongoing efforts to enhance LLM capabilities and address their limitations. Research into peer-preservation in multi-agent LLM systems also underscores growing focus on responsible AI development and robust safety mechanisms.

Daily Neural Digest Analysis

Mainstream media coverage will likely emphasize the 218x speedup, potentially oversimplifying technical complexities and overlooking adoption challenges. A critical element often missed is the expertise required to implement this optimization, which could limit accessibility to a small subset of developers. Security risks from repurposing hardware for unintended purposes also warrant investigation, as seen in the recent parisneo/lollms vulnerability. The long-term implications suggest a future where AI acceleration relies less on specialized hardware and more on innovative software solutions. However, a key question remains: Will this approach scale to larger, more complex LLMs, or is it limited to smaller models like SmolLM2-135M (1,284,574 downloads) and SmolLM3-3B (1,095,987 downloads)? The answer will determine whether this represents a fleeting novelty or a fundamental shift in LLM deployment.

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References

[1] Editorial_board — Original article — https://reddit.com/r/deeplearning/comments/1sgsfk7/used_the_rt_cores_on_my_rtx_5070_ti_for_llm/

[2] VentureBeat — LLM-referred traffic converts at 30-40% — and most enterprises aren't optimizing for it — https://venturebeat.com/technology/llm-referred-traffic-converts-at-30-40-and-most-enterprises-arent-optimizing

[3] Ars Technica — Meta's Superintelligence Lab unveils its first public model, Muse Spark — https://arstechnica.com/ai/2026/04/metas-superintelligence-lab-unveils-its-first-public-model-muse-spark/

[4] TechCrunch — I can’t help rooting for tiny open source AI model maker Arcee — https://techcrunch.com/2026/04/07/i-cant-help-rooting-for-tiny-open-source-ai-model-maker-arcee/