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A New Race for Faster Local AI
The next major battle in artificial intelligence may not be about building the largest model. It may be about making increasingly capable models run dramatically faster on the hardware people already own.
That is the idea behind the Base Optimization Stack (B), a pipeline designed to take an open-weight AI model and transform it into a hardware-specific, highly optimized runtime release. Instead of relying on engineers to manually port every new architecture, identify performance bottlenecks, write specialized kernels, benchmark dozens of configurations, and repeat the process for every device, B uses AI research agents to perform much of that work under tightly controlled constraints.
The result described in this report is particularly striking because the system was tested on NVIDIA’s Nemotron 3 Nano 30B-A3B, a hybrid mixture-of-experts architecture that was previously unsupported by BaseRT. After being ported and tuned for Apple silicon, the resulting implementation reportedly reached substantially higher inference performance than both llama.cpp and MLX across the tested workloads.
More importantly, the project presents a broader argument: optimization itself can become an automated, continuously improving process.
The Core Problem: Powerful Models Are Not Automatically Fast
Modern AI models are becoming increasingly diverse, but hardware runtimes do not automatically keep pace with every architectural innovation.
A model can be released with open weights and still require extensive engineering before it can perform efficiently on a particular processor. A model optimized for GPUs may behave very differently on Apple silicon, while a model designed around dense transformer layers may require completely different techniques from one built around state-space models and sparse mixture-of-experts components.
This creates a growing gap between model availability and model usability.
A new model may technically run on a device while delivering disappointing performance. Closing that gap traditionally requires specialized engineers who understand compilers, kernels, memory hierarchies, numerical precision, model architecture, scheduling, and hardware-specific behavior.
B attempts to automate that optimization process.
What the Base Optimization Stack Does
The Base Optimization Stack begins with public model weights and ends with a version of the model tuned specifically for a particular device.
The pipeline is deliberately divided into stages because correctness comes before speed.
The first stage is the open-weight checkpoint, representing the model exactly as released.
The second stage creates a .base quantization bundle from reference weights. The objective is to avoid introducing unnecessary discrepancies through repeated or lossy re-quantization.
The third stage is the porting phase, where research agents implement the model architecture for the target runtime and hardware.
The fourth stage is tuning, where the agents systematically search for ways to improve performance without violating accuracy constraints.
Finally, the optimized implementation becomes a BaseRT release targeted at the particular model and device.
Correctness Comes Before Performance
One of the most important aspects of the system is that performance is not allowed to override correctness.
Every modification is represented by a commit, and every experiment is benchmarked. This creates a reproducible trail connecting an optimization idea to its implementation and measured result.
The system also uses fixed gates to prevent an optimization from being accepted simply because it makes the model faster.
A kernel modification must pass runtime unit tests. Quantized weights must remain consistent with the reference checkpoint. Held-out perplexity must remain within an established tolerance.
This means an optimization that increases throughput but changes model behavior beyond the allowed threshold is considered a failure.
That distinction matters because AI inference optimization can become deceptively dangerous. A benchmark can improve while model quality silently deteriorates.
B attempts to make that tradeoff explicit.
The Most Interesting Part: Optimization as a Search Problem
Rather than relying on a predetermined list of optimizations, B describes its tuning process as an evolving tree search.
Every node represents an experiment.
Each experiment contains a hypothesis, an implementation, and measured benchmark results. Successful directions can generate additional branches, while weaker approaches can be abandoned.
The system also attempts to preserve the knowledge gained from previous experiments.
That creates the possibility of a compounding optimization system.
An optimization discovered while tuning one model on one chip could become useful when another model arrives on a different piece of hardware.
In other words, the system is not supposed to start from zero every time a new model is released.
Nemotron 3 Nano Becomes the Stress Test
The demonstration focuses on Nemotron 3 Nano 30B-A3B, a particularly interesting test case because its architecture does not resemble a conventional dense transformer.
The model contains 52 layers and combines multiple architectural ideas.
Most of its sequence mixing is handled by Mamba-2 state-space blocks. Attention appears only in a smaller subset of layers. Its feed-forward architecture uses sparse mixture-of-experts computation, including squared-ReLU experts, a shared expert, and a sigmoid-based router.
Although the model contains roughly 31.6 billion parameters, approximately 3.2 billion parameters are active for each token.
That makes the model considerably more complicated to optimize than a straightforward dense transformer.
Why the Architecture Matters
Traditional AI inference engines often benefit from highly mature kernels designed around common transformer operations.
Nemotron 3 Nano does not fit neatly into that model.
Its architecture requires specialized operations for state-space processing, causal convolution, normalization, routing, and expert computation.
Consequently, simply configuring an existing runtime is not enough.
The implementation needs new kernels.
That makes the example useful because it tests whether B can handle a genuinely unfamiliar architecture rather than merely squeeze another few percentage points out of an already-supported model.
Building the Missing Kernels
The porting process introduced specialized operations including ssm_scan for Mamba-2 selective scanning.
It also introduced depthwise_conv1d_causal, combining convolution, bias, and SiLU operations during decoding.
Another important component was mamba_gated_rmsnorm, along with routing and expert-related operations such as router_sigmoid_bias_topk and fused ReLU-squared operations inside expert GEMVs.
These operations are not merely cosmetic additions.
They form the computational foundation required for the architecture to execute efficiently on the target hardware.
The First Version Was Correct but Slow
The initial port was intentionally focused on correctness rather than maximum speed.
On Apple silicon, the implementation was compared against a reference implementation using the same underlying weights.
The objective was to establish that the port behaved correctly before optimization began.
Once that baseline existed, the tuning agents could begin attacking the performance problem.
This separation between porting and tuning is important because it creates a clear starting point.
Instead of asking an optimization agent to simultaneously determine whether the architecture works and whether it works quickly, B establishes correctness first.
The Performance Explosion During Prefill
The results from tuning were particularly dramatic during prefill.
At a 512-token context, prefill performance increased from 85.7 tokens per second to 927 tokens per second, representing a reported 10.8× improvement.
At 4K context, performance increased from 85.4 tokens per second to 1,093 tokens per second, or approximately 12.8×.
Decode performance improved from 85.7 to 111 tokens per second, representing a smaller 1.3× gain.
The difference between these workloads reveals something fundamental about AI inference.
Why Prefill Improves More Than Decode
Prefill processes a large amount of input in a highly parallel fashion.
That makes it much more dependent on computational efficiency and allows aggressive kernel optimization to produce substantial gains.
Decode is different.
During token-by-token generation, the system frequently becomes constrained by memory bandwidth and the cost of moving data rather than simply performing calculations.
That limits the impact of certain computational optimizations.
The B results therefore show an important distinction: the fastest inference engine is not necessarily optimized with one universal technique.
Different phases of inference require different strategies.
Kernel Fusion Becomes a Major Weapon
One of the main strategies described in the optimization process involves fusing chains of operations.
Instead of executing multiple operations separately, related operations can be combined into fewer kernel launches.
This reduces dispatch overhead and can minimize unnecessary movement of intermediate data.
On modern hardware, eliminating apparently small inefficiencies can have a major cumulative effect.
The fact that the optimization agents independently discovered similar techniques suggests that the search process was identifying genuine architectural bottlenecks rather than simply producing random changes.
Three Different AI Agents Enter the Optimization Race
The project also compares three frontier AI models used as optimization agents: Claude Fable 5, Kimi K3, and GLM 5.2.
Each model received the same optimization branch, evaluation methodology, and eight-hour budget.
This is an interesting experiment because it shifts the question from “Which AI model writes the best code?” to something more practical:
Which AI model can discover the best hardware optimization strategy under controlled conditions?
Fable 5 Delivers the Highest Reported Score
According to the supplied benchmark, Claude Fable 5 conducted 50 experiments and achieved a best score of 335.9, representing a 3.9× uplift.
The reported token usage was 178 million, with a cost of approximately $265.
This made Fable 5 the strongest performer in the test according to the project’s scoring system.
But it was not necessarily the most economical.
Kimi K3 Shows the Cost-Performance Tradeoff
Kimi K3 conducted 21 experiments and achieved a score of 326.5, representing a 3.8× uplift.
Its reported cost was only $77, despite consuming approximately 197 million tokens.
That means Kimi K3 reportedly recovered around 97% of Fable 5’s performance while costing less than one-third as much.
For production optimization pipelines, that difference could become more important than the final benchmark winner.
If thousands of models and hardware configurations need to be optimized, the cheapest successful optimization process could ultimately have greater commercial value than the absolute fastest agent.
GLM 5.2 Creates a Different Possibility
GLM 5.2 performed only 12 experiments and reached a score of 290.2, representing a 3.3× uplift.
The remarkable part is not simply the score.
The project reports that GLM 5.2 ran locally on an M3 Ultra Mac Studio through BaseRT, resulting in practically zero marginal model-serving cost.
That suggests another possible future for AI optimization: AI systems optimizing AI systems locally.
Instead of sending every optimization experiment to an expensive cloud model, some of the work could eventually be performed directly on developer hardware.
The Agents Found Similar First Moves
Despite their differences, all three agents reportedly discovered several of the same early optimization strategies.
These included removing limitations on batched SSM prefill, improving the decode dispatch table, and fusing the Mamba convolution chain.
That convergence is significant.
When independent optimization systems repeatedly identify similar bottlenecks, it suggests that the underlying performance opportunities are relatively discoverable.
The agents then diverged in how aggressively they pursued additional optimizations.
This is exactly where differences between reasoning systems become valuable.
BaseRT Versus llama.cpp
The final comparison places the tuned BaseRT implementation against llama.cpp and MLX.
The benchmark was reportedly conducted on the same machine and day, with pinned versions and the same testing protocol.
That type of paired comparison is important because hardware, software versions, drivers, and environmental conditions can all influence inference results.
The reported results put BaseRT ahead in every tested configuration.
Prefill Performance Leads the Comparison
At 128-token prefill, BaseRT reached 672 tokens per second, compared with 382.8 for llama.cpp and 263.5 for MLX.
That represents approximately 1.76× the performance of llama.cpp and 2.55× the performance of MLX.
At 512 tokens, BaseRT reached 925.1 tokens per second, compared with 667.1 for llama.cpp and 441.7 for MLX.
The advantage remained substantial at longer contexts.
At 2,048 tokens, BaseRT reached 1,037.6 tokens per second, while llama.cpp reached 660.9 and MLX reached 525.1.
Decode Performance Is Also Ahead
For 128-token decode, BaseRT reached 113.8 tokens per second.
The corresponding llama.cpp figure was 60.0 tokens per second, while MLX reached 79.6.
That means BaseRT was approximately 1.90× faster than llama.cpp and 1.43× faster than MLX in the reported decode test.
The decode lead is smaller than the prefill lead, but that is consistent with the project’s explanation that decode is more heavily constrained by memory bandwidth.
The Bigger Achievement Is Not One Benchmark
It would be easy to focus entirely on the headline speed numbers.
But the more important achievement may be the process that produced them.
Nemotron 3 Nano started as an architecture that BaseRT did not support.
Instead of waiting for a human engineering team to manually implement every component and optimize it over an extended period, the B pipeline used AI agents to perform the porting and optimization process under defined rules.
That points toward a fundamentally different software development model.
Hardware-Specific AI Could Become the New Normal
Today, AI models are often treated as universal software packages.
In practice, they are not.
The same model can behave very differently depending on the GPU, CPU, memory subsystem, accelerator, runtime, compiler, quantization scheme, and operating environment.
B embraces that reality.
Rather than producing one generic build and hoping it performs well everywhere, it aims to produce versions tuned for specific model-hardware combinations.
That could eventually mean that a model downloaded onto an M-series Mac receives a different optimized implementation from the same model running on an NVIDIA system.
The 27% Cross-Hardware Acceleration Is Especially Interesting
The supplied report says that the same harness later carried Nemotron 3 Nano to an NVIDIA DGX Spark.
More importantly, knowledge gained during the Apple silicon optimization reportedly accelerated the process on the NVIDIA hardware by 27%.
That is one of the most consequential claims in the entire article.
If optimization knowledge can genuinely transfer between hardware environments, B is not merely an automated compiler pipeline.
It begins to resemble a persistent optimization knowledge system.
Optimization Knowledge Could Compound Over Time
Imagine optimizing hundreds of models across dozens of chips.
A traditional engineering workflow could repeatedly rediscover similar performance patterns.
An intelligent optimization system could potentially remember them.
A scheduling technique discovered on one architecture could become an initial hypothesis for another.
A kernel fusion strategy discovered during one
A memory-management trick could transfer across several hardware generations.
The result would be an optimization system that becomes more capable as the number of supported models increases.
Why This Matters for Local AI
Local AI is becoming increasingly important because users want faster inference without sending every request to a cloud service.
Running models locally can offer privacy benefits, offline operation, lower recurring inference costs, and more control over data.
But local AI has always faced a fundamental limitation: hardware resources are finite.
The more efficient the software becomes, the more capable the models that can realistically run on consumer devices.
That makes optimization almost as important as model compression.
Better Software Can Make Existing Hardware Feel New
A major implication of the B approach is that improving software can effectively increase the usable capability of existing hardware.
A computer does not necessarily need a new accelerator to become more useful for AI.
Sometimes it needs better kernels, scheduling, memory management, quantization, and runtime integration.
The difference between 85 tokens per second and more than 900 tokens per second during prefill demonstrates how dramatic those gains can become when multiple layers of optimization align.
The Cost of AI Optimization Could Also Fall
There is another economic dimension to this story.
If AI agents can perform a substantial amount of model porting and kernel optimization, the cost of supporting a new architecture could decrease.
Today, highly specialized optimization talent is expensive and limited.
If automated systems can handle routine experimentation while human engineers supervise the constraints and evaluate unusual results, optimization could become more scalable.
The result would be particularly valuable for smaller model developers and hardware companies that cannot maintain large optimization teams.
Local Optimization Could Be the Long-Term Goal
GLM
Instead of renting expensive frontier models every time a kernel needs improvement, developers could increasingly run capable optimization agents directly on their own workstations.
The frontier of AI-assisted engineering would therefore move closer to the hardware itself.
That could create a feedback loop where local AI models optimize local AI inference engines.
The Real Product May Be the Optimization Engine
The
The larger ambition is a system where the time and cost required to achieve frontier-level inference performance continuously decline.
That changes the value proposition.
A single optimized model is useful.
An optimization platform that becomes better every time it supports another model or chip could be considerably more valuable.
Day-One Support Is the Ultimate Ambition
The long-term objective is even more ambitious: day-one support for new models across different hardware platforms.
That would represent a significant change from
Instead of a model launching and then waiting for community developers to implement support, optimize kernels, add quantization, test performance, and fix compatibility issues, an automated pipeline could begin the process immediately.
The model could potentially arrive with optimized runtime support almost simultaneously.
Why Hardware Companies Should Pay Attention
For chip manufacturers, inference performance is increasingly becoming a competitive differentiator.
Raw compute specifications are only part of the story.
If one accelerator theoretically offers enormous compute capacity but lacks efficient software support for the latest AI architectures, the real-world experience can fall behind.
A system like B attacks that software bottleneck.
It could help hardware vendors demonstrate better performance across a rapidly changing collection of models without manually optimizing every architecture from scratch.
Why Model Developers Should Pay Attention
Model developers face a similar problem.
A model can be technically impressive while receiving disappointing real-world adoption if it is difficult to run efficiently.
Users care about latency, throughput, memory consumption, compatibility, and installation complexity.
An automated optimization pipeline could allow model developers to treat hardware performance as part of the release process rather than as an afterthought.
Why Consumers Could Eventually Benefit
Consumers may never know that a system like B exists.
That could actually be a sign that it succeeded.
The ultimate result could simply be that a downloaded model runs faster, uses fewer resources, generates tokens more smoothly, and works on a wider range of devices.
Instead of users thinking about kernels and dispatch tables, they would simply experience faster AI.
The Biggest Challenge: Optimization Is Not Magic
Despite the impressive numbers, automated optimization should not be treated as an unlimited solution.
AI agents can make incorrect assumptions.
Benchmarks can be misleading.
An optimization that works on one workload may perform poorly on another.
Hardware behavior can also change depending on thermal conditions, memory pressure, operating-system versions, and competing workloads.
That is why the
The Benchmark Methodology Matters
Performance comparisons are only meaningful when the test conditions are controlled.
The reported comparison attempts to address this by using the same machine, the same day, pinned versions, and a paired sweep.
That does not eliminate every possible source of measurement variation, but it makes the comparison considerably more useful than isolated benchmark numbers collected under different circumstances.
The same principle should be applied to future evaluations of B.
Perplexity Gates Are a Critical Safeguard
One of the strongest parts of the methodology is the insistence that throughput gains are insufficient if model quality deteriorates.
Perplexity provides a practical signal for determining whether an optimization has changed model behavior beyond the accepted tolerance.
The report even states that the tuned build finished with perplexity 2.0% below the untuned baseline.
That is important because it indicates that the performance improvements did not require the optimization process to sacrifice the measured quality metric.
The Difference Between Fast and Useful
There is a fundamental distinction between making a model benchmark quickly and making a model useful.
A runtime that achieves spectacular throughput on one synthetic workload but behaves poorly during long-context generation may not provide a better real-world experience.
The most valuable optimization systems therefore need to optimize across multiple workloads.
B’s geometric-mean scoring approach attempts to avoid optimizing for a single isolated number.
That makes the final score more representative of overall performance.
Deep Analysis: The Beginning of Self-Optimizing AI Infrastructure
The deeper significance of B is that it brings AI into a part of the AI software stack that has historically depended heavily on human specialists.
For decades, compilers have attempted to automatically transform code.
GPU libraries have provided optimized primitives.
Specialized inference engines have combined hardware knowledge with model-specific implementations.
B pushes this concept further by asking AI systems to participate directly in the optimization research process.
The important shift is from automated execution to automated experimentation.
The system does not simply execute a known optimization.
It proposes modifications, tests them, measures the outcome, keeps successful ideas, and discards failures.
That resembles scientific experimentation more than traditional compilation.
Deep Analysis: Every Experiment Becomes Institutional Memory
The persistent experiment database may ultimately be more valuable than any individual optimization.
Software engineering teams routinely lose optimization knowledge when projects end or engineers move to other work.
A persistent agent memory can potentially preserve the reasoning behind successful techniques.
Over enough iterations, that knowledge base could become an increasingly sophisticated map of how different AI architectures behave across different processors.
The system would then have an advantage that a newly created optimization pipeline would not possess: accumulated experience.
Deep Analysis: The Optimization Tree Could Become a Competitive Moat
If B genuinely compounds knowledge between models and hardware platforms, its competitive advantage could grow over time.
A new system starting from scratch might require hundreds of experiments to find important optimizations.
An experienced system could begin with those discoveries already encoded into its search strategy.
That creates a potential flywheel.
More models produce more experiments.
More experiments produce more optimization knowledge.
More knowledge makes future optimizations faster.
Faster optimization enables support for more models.
The cycle then repeats.
Deep Analysis: Frontier Models Become Engineering Tools
The comparison between Fable 5, Kimi K3, and GLM 5.2 also illustrates another emerging trend.
AI models are increasingly becoming tools for building AI infrastructure.
The important question is no longer only how well a model writes an answer.
It is how effectively the model can reason about kernels, memory access, scheduling, hardware constraints, and benchmark feedback.
That represents a new category of AI evaluation.
A model could be mediocre at general-purpose conversation but extremely valuable as a systems optimization agent.
Deep Analysis: Cost May Matter More Than the Absolute Winner
Fable 5 reportedly achieved the highest optimization score, but Kimi K3 achieved nearly the same performance for dramatically less money.
This is exactly the type of tradeoff that matters in production.
If an optimization process costs $265 once, the difference may be irrelevant for a flagship model.
But if thousands of model-device combinations need to be optimized, the economics change dramatically.
A slightly weaker agent that costs a fraction as much could ultimately deliver more total optimization work.
Deep Analysis: Local AI Agents Change the Equation Again
GLM
Cloud-based optimization agents incur API and infrastructure costs.
Local agents shift the cost toward hardware ownership and electricity.
For organizations already operating powerful development machines, that marginal cost can become extremely low.
If local models continue improving, increasingly sophisticated optimization tasks could migrate from cloud APIs to developer workstations.
Deep Analysis: Hardware Diversity Is Becoming a Software Problem
AI hardware is fragmenting.
Apple has its own silicon architecture.
NVIDIA dominates large portions of the accelerator market.
AMD, Qualcomm, Intel, cloud providers, and specialized AI-chip companies all bring different architectures and software stacks.
A universal optimization process becomes increasingly valuable as this fragmentation grows.
Instead of manually supporting every model on every platform, developers could potentially rely on an automated system that adapts the model to each target.
Deep Analysis: Sparse Models Need Specialized Thinking
Nemotron 3 Nano also demonstrates why optimization cannot simply be reduced to FLOPS.
A sparse mixture-of-experts model may have billions of total parameters but activate only a fraction for each token.
Its performance depends on routing, expert selection, memory movement, batching, and kernel efficiency.
The Mamba components introduce another set of challenges.
This combination makes the model a useful demonstration of why future AI runtimes must understand architecture rather than simply treat every model as a generic transformer.
Deep Analysis: Prefill and Decode Are Different Optimization Problems
One of the most useful lessons from the benchmark is that prefill and decode should not be treated as identical workloads.
Prefill can take advantage of parallel computation.
Decode repeatedly processes smaller amounts of new information while maintaining and accessing model state.
That means a runtime can be exceptional at prefill and only average at decode, or vice versa.
The best inference systems will increasingly optimize both phases independently.
Deep Analysis: Memory Bandwidth Will Remain a Wall
The relatively modest decode improvement also exposes a fundamental hardware limitation.
There are circumstances where better software cannot completely overcome insufficient memory bandwidth.
When a workload is memory-bound, reducing unnecessary transfers and fusing operations can help, but there is still a physical ceiling.
That means the future of AI performance will depend on both software optimization and continued hardware improvements in memory systems.
Deep Analysis: The Runtime Could Become as Important as the Model
AI discussions often focus on parameter counts and benchmark scores.
But inference runtime efficiency can determine whether those capabilities are actually accessible.
Two identical model weights can deliver dramatically different user experiences depending on the runtime.
This means the runtime should increasingly be considered part of the AI product itself.
Deep Analysis: Open Weights Become More Valuable With Better Optimization
Open-weight models already provide flexibility.
But flexibility without efficient deployment can be frustrating.
A strong optimization pipeline increases the practical value of open weights by reducing the engineering barrier between downloading a model and running it efficiently.
That could encourage more experimentation with open architectures.
Deep Analysis: Optimization Could Become Continuous
The current model of software optimization often ends when a release ships.
An AI-driven system could make optimization continuous.
As new chips appear, new compiler capabilities emerge, or new kernels become available, the system could revisit existing models and search for additional improvements.
The “final” optimized version may therefore become a temporary state rather than a permanent endpoint.
Deep Analysis: Every New Chip Could Benefit From Previous Models
The reverse could also happen.
When a new hardware platform arrives, the optimization system could use everything it learned from older platforms to accelerate its initial search.
This is particularly relevant to the reported 27% acceleration when moving the Nemotron optimization process toward NVIDIA DGX Spark after Apple silicon work.
If that knowledge transfer scales, new hardware could become useful faster.
Deep Analysis: AI Optimization Could Reduce Engineering Bottlenecks
The biggest limitation in supporting new AI architectures may eventually stop being raw engineering manpower.
Instead, the bottleneck could become how intelligently an organization defines constraints, benchmarks, evaluation metrics, and search strategies.
Human engineers would remain essential, but their role could shift.
Rather than writing every optimization manually, they could design the environment in which optimization agents safely experiment.
Deep Analysis: Safety and Reproducibility Become More Important
Giving AI agents permission to modify performance-critical systems creates new risks.
An agent could accidentally introduce a numerical bug, exploit a benchmark weakness, or optimize for the wrong metric.
The commit-per-experiment model described by B is therefore more than a convenience.
It provides an audit trail.
Every change can be traced to a hypothesis and a measured result.
That type of reproducibility will become increasingly important as autonomous engineering systems become more capable.
Deep Analysis: The Winning Platform May Be the One That Learns Fastest
The ultimate competition may not be about which runtime has the fastest benchmark today.
It may be about which runtime improves fastest tomorrow.
A static optimization library has a fixed knowledge base.
An optimization system that continuously learns from new architectures and hardware could improve with every generation.
That is a fundamentally different competitive model.
Deep Analysis: Day-Zero Optimization Is a Powerful Goal
The phrase “Day-0 support” captures the ambition well.
When a major AI model launches, developers immediately want to run it locally.
If hardware-specific optimization takes weeks or months, early adopters are forced to use generic implementations.
An automated optimization system could potentially reduce that waiting period dramatically.
If it succeeds, model launches could become hardware events at the same time they are software events.
What Undercode Say:
The Real Innovation Is the Pipeline
The strongest part of this project is not the claim that one runtime beat another in a benchmark. The more interesting idea is the attempt to automate the journey from unsupported model architecture to optimized hardware implementation.
Optimization Is Becoming an AI Task
AI has already moved into coding, testing, debugging, and software generation. Hardware optimization is a natural next step because it combines reasoning with measurable feedback.
Benchmark Feedback Gives Agents a Reality Check
Unlike ordinary code generation, an optimization agent cannot simply claim that its code is better. The hardware has to prove it through benchmarks.
Failed Experiments Are Valuable
A failed optimization is not necessarily wasted work if the system remembers why it failed. Persistent experimental memory can prevent future searches from repeating the same mistakes.
The Search Tree Is Potentially More Important Than Individual Kernels
Specific kernels will eventually become obsolete as hardware changes. A search strategy that understands how to discover better kernels can remain valuable across generations.
Nemotron 3 Nano Was a Good Stress Test
Its mixture of Mamba, attention, and sparse MoE components creates a much more complicated optimization challenge than a conventional dense transformer.
The 10×-Plus Prefill Gains Are the Headline Numbers
Moving from roughly 85 tokens per second to more than 900 or 1,000 tokens per second demonstrates how much performance can remain hidden behind an unoptimized implementation.
Decode Shows the Limits of Software
The smaller decode improvement is equally informative. It demonstrates that not every bottleneck can be solved through kernel optimization when memory bandwidth becomes the limiting factor.
Cost Efficiency Could Determine Adoption
Fable 5 achieved the best reported result, but Kimi K3’s much lower cost makes it potentially more attractive for large-scale optimization workloads.
Local Agents Could Be Disruptive
If capable optimization models can run locally, developers could perform repeated optimization experiments without accumulating large cloud inference bills.
GLM
Even though its reported final score was lower, its ability to run locally at practically zero marginal cost introduces a compelling alternative to expensive frontier agents.
Hardware Vendors Have a Major Incentive
Chip companies need strong software ecosystems. Automated optimization could make new hardware useful with a wider range of models sooner.
Model Developers Benefit Too
Efficient deployment can make a model more attractive to developers who otherwise might avoid architectures that require significant manual optimization.
Open Models Need More Than Open Weights
A model being downloadable does not mean it is easy to run. Hardware-specific optimization closes part of that gap.
Runtime Engineering Is Becoming Model Engineering
As architectures become more complicated, model development and runtime development increasingly overlap.
The Best Runtime May Be Architecture-Aware
Generic transformer optimization is no longer enough for an ecosystem containing state-space models, MoEs, multimodal systems, recurrent architectures, and hybrids.
AI Agents Need Better Engineering Benchmarks
The future of coding-agent evaluation should include measurable systems tasks where success depends on real hardware performance.
The 27% Knowledge Transfer Claim Is Particularly Important
If optimization knowledge genuinely transfers between hardware platforms, B could become a cumulative learning system rather than a collection of isolated tuning jobs.
Continuous Optimization Could Replace Release-Time Optimization
Instead of optimizing once and stopping, future runtimes could constantly search for improvements as hardware and software evolve.
Hardware Fragmentation Makes Automation More Valuable
The greater the number of chips and architectures, the harder manual optimization becomes. Automation becomes more valuable as the ecosystem becomes more fragmented.
Memory Bandwidth Remains a Fundamental Constraint
Software can reduce wasted memory movement, but physical bandwidth limitations remain a major barrier to faster token generation.
Prefill and Decode Need Different Strategies
A runtime should not assume that the same optimization will improve both workloads equally.
Quantization Is Only One Part of the Problem
Smaller weights help memory consumption, but kernels, scheduling, routing, fusion, and hardware utilization determine how efficiently those weights are processed.
Correctness Gates Should Become Standard
Performance gains should never be accepted blindly. Numerical and model-quality checks are essential when AI systems modify inference engines automatically.
Reproducibility Is a Competitive Advantage
Knowing exactly which change produced an improvement makes optimization easier to understand, debug, and repeat.
AI Could Turn Optimization Into Infrastructure
Once optimization becomes automated, developers may treat it as a standard infrastructure service rather than a specialized engineering project.
The Cost Curve Could Fall Dramatically
If each new model requires less human engineering time, supporting more architectures becomes economically feasible.
Local AI Benefits Directly
Faster inference means shorter waiting times, better interactive experiences, and potentially larger models running on consumer hardware.
Existing Hardware Could Become More Capable
Software efficiency can effectively unlock performance that users already paid for but were previously unable to access.
The Model Is Only Half the Product
The practical experience depends on the combination of weights, runtime, kernels, quantization, memory management, and hardware.
Autonomous Optimization Will Still Need Humans
The goal should not be removing engineers from the process entirely. Human-designed constraints, evaluation systems, and architectural judgment remain critical.
The Most Valuable AI May Be AI That Improves Other AI
This is perhaps the most important strategic lesson. An AI model that helps optimize the infrastructure running future AI models can create value beyond its own direct usage.
The Future Could Be Model-Specific and Device-Specific
Instead of one universal runtime binary, users could eventually receive implementations optimized for their exact model and hardware combination.
Day-0 Performance Would Change the AI Market
If new models could immediately receive efficient local implementations, the delay between model release and practical deployment could shrink dramatically.
B Points Toward a Self-Improving Optimization Economy
The ultimate promise is not merely faster inference today. It is a system where every optimization job contributes knowledge that can make the next optimization job faster and cheaper.
✅ The supplied benchmark reports that the tuned BaseRT implementation outperformed llama.cpp and MLX across the listed Nemotron 3 Nano Apple-silicon tests, including a reported 1.90× advantage over llama.cpp and 1.43× advantage over MLX for decode at the tested context.
✅ The supplied results report major prefill improvements during tuning, increasing from 85.7 to 927 tokens per second at 512 tokens and from 85.4 to 1,093 tokens per second at 4K context, while decode improved more modestly.
⚠️ The article's performance, agent, cost, and cross-hardware claims are presented as results from the supplied BaseRT/B report; they should be independently reproduced on the same hardware, software versions, model weights, and benchmark methodology before being treated as universally established performance figures.
Prediction
(-1) The biggest limitation will remain hardware diversity. No optimization system can completely remove physical constraints such as memory bandwidth, cache capacity, thermal limits, and accelerator-specific behavior.
(+1) Automated optimization agents are likely to become an increasingly important part of AI infrastructure, particularly as model architectures become more heterogeneous and hardware platforms continue to multiply.
(+1) The economics of optimization should improve if capable local models can perform a meaningful share of kernel research and benchmarking without expensive cloud inference.
(+1) Hardware vendors are likely to invest more heavily in AI-assisted runtime optimization because software efficiency can become a major differentiator between competing accelerator platforms.
(+1) The strongest long-term outcome would be a system that accumulates optimization knowledge across models and chips, allowing each new architecture to reach high-performance inference faster than the previous one.
(+1) If the reported knowledge-transfer effect scales beyond individual demonstrations, the industry could move toward near-automatic Day-0 optimization, where new open-weight models rapidly receive device-specific inference implementations.
(+1) Local AI could benefit substantially from this trend because better kernels and runtimes can make existing consumer hardware support larger and more sophisticated models without requiring an immediate hardware upgrade.
(+1) The most important competition may eventually shift from simply building faster AI models to building systems that can make every new AI model faster, cheaper, and easier to deploy.
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