d-Matrix Corsair and the Rise of Dedicated AI Inference Silicon (2026 Analysis)

For three years the AI hardware story was a training story, and the training story was a GPU story. That framing is now quietly obsolete. The dominant cost of running large language models in production is no longer teaching them — it is serving them, token by token, to millions of users under tight latency budgets. AI inference silicon built specifically for that job is arriving in volume, and in 2026 d-Matrix moved its Corsair platform into full production, with independent testing showing more than 10x speed improvement over GPU-only setups on some inference workloads. That number is real but narrow, and understanding why it is both impressive and workload-specific is the whole point of this analysis. Inference is a different physics problem from training: it is memory-bandwidth bound, latency-sensitive, and increasingly defined by the growing KV cache. Chips designed around that reality — in-memory compute, wafer-scale, deterministic dataflow — are now credible alternatives to the general-purpose GPU.

What this covers: why inference is a distinct workload, the memory-wall problem that motivates in-memory compute, how d-Matrix Corsair’s digital in-memory-compute architecture works, how it sits against Groq, Cerebras, SambaNova, AWS, and NVIDIA, and how to actually evaluate an inference accelerator without being fooled by a headline benchmark.

Context and Background

The economics of large-scale AI have inverted. A frontier model is trained once, at enormous but bounded cost, and then serves inference requests continuously for the rest of its deployed life. Every chatbot reply, every code completion, every retrieval-augmented answer is an inference pass, and at scale those passes dominate the total compute bill. Industry estimates through 2025 and into 2026 consistently put inference at well over half — often cited around 80–90% — of the lifetime compute a deployed model consumes. When a model serves billions of tokens a day, small improvements in cost-per-token and latency compound into very large numbers on the operator’s income statement.

GPUs won the training era for good reasons: massive parallel floating-point throughput, mature software, and flexibility across every model architecture researchers could invent. But the properties that make a GPU excellent for training — huge batch sizes, tolerance for latency, an appetite for raw FLOPS — are not the properties that make it ideal for latency-bound single-user inference. In the decode phase of autoregressive generation, a GPU’s thousands of arithmetic units frequently sit idle, starved for data because the bottleneck is moving weights and the key-value cache out of high-bandwidth memory rather than doing math. You are paying for a supercomputer’s compute and using a fraction of it. This is the opening that a wave of dedicated inference startups and hyperscaler silicon teams have driven into.

There is a second force pushing this shift beyond pure economics: power. Datacenter build-outs are increasingly constrained not by capital for chips but by megawatts at the substation and by the ability to remove heat from the rack. In that environment, a chip that produces the same tokens for fewer joules is not merely cheaper — it lets an operator fit more useful serving capacity inside a fixed power envelope they physically cannot exceed. Efficiency has become a capacity story, and that reframing is a large part of why energy-per-token architectures are getting serious attention from buyers who would previously have defaulted to the fastest available GPU without a second thought.

The incumbents and challengers now include NVIDIA (still the default, and not standing still), plus Groq with its deterministic Language Processing Unit, Cerebras with its wafer-scale engine, SambaNova with its reconfigurable dataflow architecture, AWS with Inferentia and Trainium, and d-Matrix with its Corsair digital in-memory-compute design. Each makes a different bet about where inference is going. For the edge side of this same trend — small models running on NPUs and low-power accelerators — see our guide to edge AI inference on NVIDIA Jetson, Intel Movidius, and Arm NPUs. For the manufacturing context that shapes who can actually build these chips, the Semiconductor Industry Association tracks the capacity and supply-chain dynamics underneath the whole race.

Why Inference Needs Different Silicon

Inference needs different silicon because LLM token generation is dominated by memory movement, not arithmetic: during decode, the accelerator reads the entire model’s weights and a steadily growing KV cache from memory to produce just one token, so raw compute sits idle waiting on bandwidth. The workload is latency-bound and memory-bound, which rewards hardware that keeps data close to compute rather than hardware that maximizes peak FLOPS.

Figure 1: The memory wall in transformer decode. Each new token forces a fetch of weights and the growing KV cache from off-chip HBM; the matrix engines stall waiting on bandwidth, leaving expensive compute idle.

Figure 1 shows the core pathology. To generate a single output token during autoregressive decode, the accelerator must read every weight the layer touches plus the accumulated KV cache for the whole sequence so far. The arithmetic per token is modest; the data motion is enormous. When the ratio of bytes moved to floating-point operations performed is high — the low arithmetic intensity regime — the chip’s throughput is capped by memory bandwidth, not by how many multiply-accumulate units it has. This is why a GPU rated at petaflops can deliver disappointing tokens-per-second on a single low-batch request.

It helps to make this concrete with the roofline mental model. Every accelerator has two ceilings: a compute ceiling (peak FLOPS) and a memory ceiling (peak bytes/second). Which one you hit depends on your operation’s arithmetic intensity — FLOPS performed per byte moved. Matrix-heavy operations with large batches sit on the compute roofline and use the chip well. A batch-size-one decode step is a matrix-vector product: it reuses each weight exactly once, so its arithmetic intensity is roughly one operation per weight byte read. That places it far out on the memory-bound side of the roofline, where doubling the chip’s FLOPS buys nothing because you were never compute-limited. The only levers that help are increasing effective bandwidth, shrinking the bytes you must move (quantization), or reusing each weight across more tokens (batching). Dedicated inference silicon attacks the first lever structurally, by relocating weights into memory that is orders of magnitude faster to reach than off-chip HBM.

Batching is the lever operators reach for first, and it explains a lot of real-world behavior. If a chip reads the weights once and applies them to sixty-four queued requests at once, the fixed cost of the weight fetch is amortized across sixty-four tokens, and arithmetic intensity climbs back toward the compute roofline. This is why GPU inference throughput looks excellent in aggregate benchmarks that assume a full batch. The catch is that assembling a large batch means holding requests until enough arrive, which inflates latency, and interactive workloads rarely present a steady stream of identical-length sequences. Continuous batching and disaggregated prefill/decode help, but they are software patches over a hardware substrate whose economics reward big batches — precisely the regime where single-user latency suffers. Silicon that is already fast at batch size one sidesteps the whole trade-off.

The Memory Wall and Bandwidth-Bound Decode

The “memory wall” is an old idea from computer architecture: compute performance has grown far faster than memory bandwidth for decades, so more and more workloads become limited by how fast you can feed the arithmetic units rather than by the units themselves. LLM decode is close to a worst case for this. Prefill — processing the input prompt — is compute-heavy and parallel, and GPUs handle it well because you can batch the whole prompt’s tokens together and reach high arithmetic intensity. Decode is the opposite: it is inherently sequential (token N+1 depends on token N), each step touches the full weight set, and with a batch size of one there is almost no arithmetic reuse to amortize the memory traffic. The result is that a large fraction of a GPU’s silicon is idle during the phase that users actually wait on. Foundational discussions of the memory wall, such as the widely cited work on AI and the memory wall, quantify how sharply model growth has outrun bandwidth growth.

The KV Cache Problem

The key-value cache is the second, compounding pressure. To avoid recomputing attention over the entire history at every step, transformers cache the key and value tensors for all prior tokens. That cache grows linearly with sequence length and with batch size, and for long-context serving it can rival or exceed the model weights in memory footprint. Two bad things follow. First, capacity: the KV cache eats the fast memory you wanted to spend on weights, forcing spillover or limiting how many concurrent requests a device can hold. Second, bandwidth: every decode step must read the relevant KV entries back, so a longer context makes each token more expensive to produce. Techniques like grouped-query attention, paged KV caches, and quantized KV storage all attack this, but they are mitigations layered on top of a hardware substrate that was never designed for it. A chip that can keep more of the working set in on-die SRAM changes the arithmetic directly.

The dynamics of the cache also explain why inference cost is not a single number but a curve that steepens with context length. Early in a generation, the KV cache is small and per-token cost is dominated by weight reads. As the conversation or document grows to tens or hundreds of thousands of tokens, the cache balloons until reading it back begins to dominate each decode step, and effective tokens-per-second falls even though nothing about the model changed. This is why long-context features that look free in a demo become expensive at scale, and why the memory subsystem — its capacity, its bandwidth, and how gracefully it degrades as the cache grows — is often the single most important thing to interrogate about an inference accelerator. Two chips with identical peak FLOPS can diverge by a wide margin once you push them into long-context, high-concurrency serving, and that divergence is almost entirely a memory-architecture story. It is also the axis on which in-memory and large-SRAM designs claim their most durable advantage, because they change the constant factor on the bandwidth term rather than merely re-scheduling around it in software.

Latency SLOs Versus Throughput, and the In-Memory Idea

Serving has two numbers that pull against each other. Throughput — total tokens per second across all users — is what you optimize by batching aggressively. Latency — how long one user waits, usually split into time-to-first-token and inter-token latency — degrades as you batch harder, because a request may sit in a queue and share compute. Interactive products live and die on the tail of that latency distribution, so operators cannot simply crank the batch size. This tension is where specialized silicon earns its keep: if the hardware is fast at low batch sizes, you get good latency without sacrificing as much throughput.

The two latency numbers are worth separating because they are gated by different phases. Time-to-first-token is dominated by prefill — the compute-heavy pass over the prompt — and by any queuing delay before the request is scheduled. Inter-token latency, the steady cadence at which subsequent tokens arrive, is dominated by the memory-bound decode step. A chat product that streams its answer can tolerate a slightly slower first token if the stream that follows is smooth and fast, whereas a system that must return a complete structured payload before acting cares mostly about total wall-clock time. These are different optimization targets, and they favor different hardware. Prefill-heavy, batchable work rewards raw compute; decode-heavy, low-batch interactive work rewards exactly the memory-close architectures this article is about. Knowing which of the two numbers your product actually lives or dies on is the first thing to establish, because it determines whether specialized inference silicon is even solving your problem — and buying a decode-optimized chip for a prefill-bound workload is a common and expensive mismatch.

In-memory compute is one structural answer. The idea is to do the multiply-accumulate operations physically inside or immediately adjacent to the memory that holds the weights, rather than shuttling weights across a bus to a separate compute unit on every cycle. There are two families. Analog IMC performs the matrix multiply in the analog domain using the physics of a memory array — dense and potentially very efficient, but wrestling with noise, precision, and ADC/DAC overhead. Digital IMC, which is d-Matrix’s approach, keeps the computation digital but places compute tightly with SRAM, preserving the determinism and bit-exactness engineers expect while still slashing the data movement that dominates energy and latency. Digital IMC trades some of the theoretical density of analog for practicality and precision — a bet that matters enormously for whether a chip is actually deployable.

The energy argument is worth spelling out because it is often the real driver behind the latency story. In modern process nodes, the energy to perform a multiply-accumulate is small compared to the energy to move the operands to and from off-chip memory. Reading a byte from external DRAM can cost one to two orders of magnitude more energy than reading it from local on-chip SRAM, and both dwarf the cost of the arithmetic itself. So a von Neumann design that streams every weight from HBM for every token is spending most of its power budget on data transport, not computation. If you keep the weights resident next to the compute, you delete that transport cost — and because power and heat, not transistors, are the binding constraint in a dense datacenter rack, cutting joules-per-token translates almost directly into more tokens per rack and lower serving cost. This is why performance-per-watt, not peak FLOPS, is the metric that inference-silicon vendors lead with, and why it is the right metric for buyers to scrutinize.

d-Matrix Corsair and the Competitive Field

d-Matrix’s Corsair is a chiplet-based accelerator built around digital in-memory compute: weights live in SRAM tiles that also perform the matrix math, so the memory-movement penalty that throttles GPU decode is attacked at the architectural level. According to d-Matrix, Corsair entered full production in 2026, and independent testing has shown more than 10x speed improvements over GPU-only alternatives on certain inference workloads. Those are vendor and third-party claims tied to specific benchmarks — treat the 10x as a best-case, workload-dependent figure, not a universal multiplier.

Figure 2: In-memory compute versus the von Neumann model. Instead of moving weights from memory to a separate compute unit each cycle, digital IMC places multiply-accumulate hardware inside the SRAM tiles, cutting the data movement that dominates energy and latency.

Figure 2 captures the structural difference. In the classic von Neumann arrangement, weights sit in memory and are streamed to a compute unit for every operation, and that streaming — not the math — is where most of the energy and time go. d-Matrix’s digital IMC keeps the weights resident in SRAM tiles interleaved with digital MAC units. Because Corsair emphasizes SRAM rather than relying primarily on off-chip HBM for the hot weight path, the working set for a suitably sized model can stay on-die, where bandwidth is effectively free compared to crossing a memory bus. The chiplet packaging lets d-Matrix scale capacity by composing multiple dies, which matters because on-die SRAM is expensive per bit and a single die cannot hold a large model. The company positions Corsair for low-latency, energy-efficient transformer inference specifically — not as a general-purpose training part. You can read d-Matrix’s own framing at d-matrix.ai.

It is worth being precise about where a figure like “10x” can plausibly come from, because the mechanism matters more than the number. When a model’s weights live in SRAM instead of HBM, the effective bandwidth feeding the matrix units can be far higher than what an off-chip memory bus delivers, and the memory-bound decode step is exactly the operation that bandwidth gates. If decode is where your GPU was spending most of its wall-clock time, and the new architecture removes the bandwidth bottleneck on that step, a large multiplier on tokens-per-second at low batch is physically reasonable — for models and context lengths that fit the on-chip budget. Equally, the same architecture can show a much smaller advantage, or none, on a workload that was already compute-bound (heavy prefill, very large batches) or on a model too large to keep resident, where you pay the sharding tax. This is the honest reading of a workload-specific benchmark: the win is real and mechanistic in the regime the chip targets, and it does not generalize outside that regime. A buyer’s job is to determine whether their traffic actually lives in that favorable regime, not to assume the headline transfers.

The scale-out story is the other half of the design, and it is where the practical constraints live. A single Corsair-class die holds a bounded amount of SRAM, so serving a large model means composing many chiplets and, beyond that, many cards linked by a fast interconnect, with the model’s layers partitioned across them. The moment you cross a chiplet or card boundary, some of the data movement you were trying to eliminate reappears as inter-die or inter-card traffic. The architecture’s advantage therefore depends on keeping as much of the hot path as possible inside the fast on-package domain, which in turn depends on model size, quantization, and how cleanly the model partitions. This is not a criticism unique to d-Matrix — every accelerator faces the same wall once the model outgrows a single device — but it is the reason on-chip-memory designs advertise their sweet spot as models that fit, and it is the first thing to check against your own model footprint.

Figure 3: The inference accelerator landscape. Different architectures optimize for different corners — latency-critical low-batch serving, high-throughput batch serving, or maximal flexibility — and no single design dominates every axis.

Figure 3 places the field on the axes that matter. Groq takes a different route to low latency: its LPU is a deterministic, software-scheduled dataflow processor with large on-chip SRAM and no reliance on HBM, engineered to produce tokens at very high, predictable speed for a given model. Groq and d-Matrix therefore compete for similar latency-critical territory but via different mechanisms — Groq’s compiler statically schedules every operation, while d-Matrix leans on in-memory compute. Groq’s public materials describe its LPU inference architecture in detail. Cerebras occupies the opposite corner with its wafer-scale engine: an entire wafer as one chip, with enormous on-wafer memory bandwidth, aimed at very high throughput and at holding large models with minimal partitioning — impressive per-unit performance, but a large and costly footprint. SambaNova uses a reconfigurable dataflow architecture with a large tiered memory system, pitching efficient serving of many large models and long context. AWS plays the vertical-integration game: Inferentia for inference and Trainium for training let it undercut GPU pricing inside its own cloud and steer customers onto silicon it controls, trading peak flexibility for cost and supply security.

These architectures also make different bets about where each fits in the serving pipeline, which is worth separating out. Prefill and decode have opposite hardware appetites: prefill is compute-bound and batches naturally, while decode is memory-bound and latency-sensitive. A growing design pattern is disaggregation — running prefill on throughput-optimized hardware and decode on latency-optimized hardware, connected over a fast fabric. Dedicated inference silicon like Corsair and Groq’s LPU is strongest on the decode side, where their low-batch speed and on-chip weight residency pay off most, while GPUs remain comfortable across both phases. That means the near-term adoption story is less “replace the GPU” and more “peel off the decode-heavy, latency-critical portion of the workload onto specialized silicon,” which is a lower-risk wedge for a challenger to drive.

And then there is NVIDIA, which is not a passive incumbent. Its newer parts add inference-oriented features, lower-precision formats, and disaggregated serving support, and — crucially — it owns the software. The real moat is CUDA and the surrounding ecosystem: kernels, libraries, framework integrations, serving stacks, and the muscle memory of every ML engineer. A challenger can beat NVIDIA on a benchmark and still lose the deal because porting, debugging, and maintaining a model on an unfamiliar stack is expensive and risky, and because the incumbent’s tooling handles the long tail of real production needs — quantization recipes, tensor/pipeline parallelism, observability, and the newest model architectures on day one. Every specialized vendor must therefore ship not just fast silicon but a compiler and runtime good enough to make migration painless, plus first-class support for whatever open-source serving framework the customer already runs. That software-maturity gap, more than any transistor count, is what decides how fast this market actually shifts. It is also why several challengers now offer their silicon primarily as a hosted inference API rather than as hardware you rack yourself: selling tokens instead of chips lets the vendor hide the software immaturity behind their own stack and removes the customer’s migration burden entirely — a telling sign of where the friction really lives.

Trade-offs, Gotchas, and What Goes Wrong

The case for dedicated inference silicon is strong, but specialization cuts both ways, and several failure modes recur. SRAM capacity is the hard limit. Keeping weights on-die is what delivers the latency and energy win, but on-die SRAM is scarce and expensive; a model that does not fit forces sharding across many chiplets or devices, which reintroduces interconnect traffic and can erode the very advantage you paid for. The architecture shines brightest when the model — after quantization — fits the on-chip budget.

Precision and model constraints matter. Many inference accelerators lean on low-precision formats (FP8, INT8, or lower) to fit weights and hit throughput targets. That is often fine, but it is not free: some models and some tasks degrade under aggressive quantization, and the accuracy hit is workload-specific. Anyone evaluating this silicon should validate quality on their own model and data, not trust a generic claim. Our breakdown of FP8 vs INT8 vs INT4 LLM quantization walks through where the trade-offs actually bite.

Software maturity is the silent killer. A benchmark measures a hand-tuned kernel on a supported model; your production stack has custom sampling, LoRA adapters, speculative decoding, structured output, and a long tail of edge cases. If the vendor’s compiler does not support them — or supports them slowly — real-world throughput can fall far short of the datasheet. There is also a cadence problem: new model families ship constantly, and a specialized accelerator only serves a model well once its compiler has kernels for that architecture. On a GPU you can usually run a brand-new model the week it releases; on specialized silicon you may wait for vendor support, which is a real operational cost for teams that need to chase the frontier. Flexibility is the structural tax of specialization: a chip optimized for today’s transformer decode may adapt poorly if architectures shift (state-space models, mixture-of-experts routing, new attention variants), whereas a GPU absorbs change. Finally, benchmark caveats: a “10x” is only meaningful with the model, batch size, sequence length, precision, and latency target attached. Different corners of that space can move the number by an order of magnitude. Treat every headline multiple as a hypothesis to test on your own traffic, not a delivered result.

Practical Recommendations

Evaluating an inference accelerator is an exercise in resisting the datasheet. Start from your workload, not the chip. Profile your actual serving pattern — model size, typical and tail sequence lengths, batch-size distribution, and the latency SLO your product truly needs (time-to-first-token and inter-token latency separately). Only then does the map in Figure 4 tell you anything.

Figure 4: A decision flow from workload profile to accelerator choice. The right answer falls out of batch size, latency SLO, whether the model fits on-chip, and how much you depend on the CUDA ecosystem.

The disciplined path is: profile, shortlist by fit, then benchmark on your own model and real traffic before committing capacity. Weigh total cost of ownership — performance-per-watt and per-dollar at your latency target, not peak throughput. And weight the software risk heavily: a chip you cannot deploy is worth nothing.

The unit that matters is cost-per-million-tokens at your SLO, and it hides several terms that a raw price-per-chip conceals. You pay for the accelerator, but also for the host system around it, the power and cooling it draws, the interconnect if the model spans devices, and the engineering time to port and maintain the model on the vendor’s stack. A chip that is cheaper per unit but needs three of them to hold your model, or that draws more power in your rack, or that eats a quarter of an engineer for six months of integration, can easily lose to a pricier part that just works. Conversely, an energy-efficient accelerator can win decisively in a power-constrained facility even at a higher sticker price, because the binding constraint there is watts, not dollars. Model the whole system at your real traffic mix, amortize the integration cost over the deployment’s expected life, and compare against a well-tuned GPU baseline rather than an off-the-shelf one — GPUs left untuned understate the incumbent and flatter the challenger. Only a comparison built on those terms tells you anything actionable.

Checklist for evaluating an inference accelerator:

• [ ] Measured your own latency SLO (TTFT and inter-token) and batch-size distribution — not a generic target.
• [ ] Confirmed your model, at your chosen precision, fits the on-chip/on-device memory budget.
• [ ] Validated output quality on your data under the accelerator’s supported quantization.
• [ ] Checked the software stack supports your real features (sampling, adapters, speculative decoding, structured output).
• [ ] Benchmarked on representative traffic, not a single curated prompt.
• [ ] Compared performance-per-watt and per-dollar at your SLO, including host and interconnect overhead.
• [ ] Assessed migration and lock-in risk versus a CUDA baseline before committing.

Frequently Asked Questions

Why is AI inference a different workload from training?

Training processes large batches, tolerates latency, and is dominated by parallel floating-point throughput — a natural fit for GPUs. Inference, especially the decode phase of LLM generation, is sequential, latency-sensitive, and bounded by memory bandwidth rather than raw compute. Each token requires reading the model’s weights and a growing KV cache from memory, so arithmetic units often sit idle. Because inference dominates a deployed model’s lifetime compute cost, hardware tuned for low-latency, memory-efficient serving can outperform general-purpose GPUs on cost and latency even without more FLOPS.

What is digital in-memory compute, and how does d-Matrix use it?

Digital in-memory compute places multiply-accumulate hardware directly alongside the SRAM that stores model weights, so computation happens where the data lives instead of streaming weights across a bus every cycle. It keeps the math in the digital domain — preserving precision and determinism — while cutting the data movement that dominates energy and latency. d-Matrix’s Corsair is a chiplet-based accelerator built on this principle, keeping weights resident in SRAM tiles to attack the memory wall that throttles GPU decode, and targeting low-latency transformer inference specifically.

Is d-Matrix Corsair really 10x faster than a GPU?

d-Matrix reports, and independent testing has shown, more than 10x speed improvements over GPU-only alternatives — but only on certain inference workloads. That figure is workload-specific: it depends on the model, batch size, sequence length, precision, and latency target. In favorable conditions the advantage is real and large; in others it shrinks substantially. Treat any single multiple as a best-case data point tied to a particular benchmark, and validate on your own model and traffic before assuming it transfers to your deployment.

What is the memory wall and why does it matter for LLMs?

The memory wall describes the widening gap between how fast processors compute and how fast memory can supply data. Over decades, compute has scaled far faster than memory bandwidth. LLM decode is nearly a worst case: producing each token reads the full weight set plus the KV cache, so throughput is capped by bandwidth, not arithmetic. This is why expensive GPU compute sits idle during generation and why architectures that keep data close to compute — in-memory compute, large on-chip SRAM, wafer-scale integration — can win on real serving workloads.

How do Groq, Cerebras, and d-Matrix differ?

All three challenge GPUs on inference but with distinct architectures. Groq’s LPU is a deterministic, compiler-scheduled dataflow processor with large on-chip SRAM, engineered for predictable low-latency token generation. Cerebras builds a wafer-scale engine — an entire wafer as one chip — for very high throughput and huge on-wafer bandwidth. d-Matrix uses digital in-memory compute in a chiplet package, doing math inside SRAM tiles to minimize data movement. Groq and d-Matrix compete for latency-critical serving; Cerebras leans toward high-throughput and holding large models with minimal partitioning.

Will dedicated inference chips displace NVIDIA GPUs?

Not wholesale, and not quickly. GPUs remain the flexible default and absorb architectural change well, and NVIDIA’s real moat is the CUDA software ecosystem — kernels, libraries, and engineer familiarity that make migration costly. Dedicated inference silicon can win specific, high-volume, latency-sensitive workloads on cost, energy, and latency, and will likely take meaningful share there. But displacement is gated by software maturity as much as by silicon. Expect a heterogeneous future where operators run GPUs alongside specialized accelerators, matching each workload to the hardware that serves it best.

Further Reading

• Edge AI inference on NVIDIA Jetson, Intel Movidius, and Arm NPUs — how inference silicon scales down to low-power devices at the edge.
• TSMC 2nm and the 2026 AI chip competition — the manufacturing race that determines who can actually build advanced accelerators.
• FP8 vs INT8 vs INT4 LLM quantization benchmark (2026) — the precision trade-offs that decide whether a model fits on-chip.
• d-Matrix — the vendor’s own description of the Corsair platform and its digital in-memory-compute architecture (vendor source).
• AI and the Memory Wall — a widely cited analysis of how model growth has outrun memory bandwidth.

By Riju — about