Silicon
Threadripper Halo Station vs DGX Station: what the memory specs mean
Your model has outgrown one GPU. AMD and NVIDIA offer different ways to make room, but total memory alone will not tell you which system fits your work.
GPUs & Silicon
Silicon: GPUs, memory and interconnects
GPUs, HBM memory, NVLink interconnects and microarchitecture: what really sets the performance and cost of AI compute, from the die to the datacenter.
12 articles
Articles: Silicon
Silicon
Ryzen AI Halo vs DGX Spark: What 128GB Doesn't Tell You
Ryzen AI Halo and DGX Spark both offer 128 GB, but performance changes with model and protocol. Vendor claims and independent LLM tests, audited.
Silicon
BlueField-4 vs Pensando Salina: the DPU enters the inference data path
BlueField-4 and Pensando Salina compute no tokens. They move networking, storage, security and selected KV-cache transfers away from the host CPU. Here is the data path that can free the GPU, or merely move the bottleneck.
Silicon
UALink 2.0 vs NVLink 6: when the network computes all-reduce
UALink 2.0 standardizes compute inside the network; NVLink 6 integrates it into its switches. Here is what the mechanism changes, and why Helios and Vera Rubin still cannot be ranked.
Silicon
The M3 Ultra Mac Studio is down to 96 GB: what local LLM users lose
Apple launched the M3 Ultra Mac Studio with 256 GB and 512 GB options. Only 96 GB remains. The chip did not get slower, but an entire class of local models has left the store.
Silicon
RTX PRO 6000 or three RTX 5090s: 96 GB is not one memory pool
An RTX PRO 6000 and three RTX 5090s each add up to 96 GB of GDDR7. For an LLM, that is where the equality ends: one pool, one sharded model and three independent replicas pay different costs.
Silicon
RTX Spark on Windows: CUDA is native, the LLM stack is not yet
RTX Spark brings up to 128 GB of unified memory and native CUDA to Windows on Arm. As of August 12, CUDA remains a developer preview, TensorRT-RTX is ready, and the LLM serving stack still needs qualification.
Silicon
AMD MI455X vs NVIDIA Rubin: can more memory beat NVLink?
The MI455X carries 432 GB of HBM4, versus 288 GB on Rubin. AMD still has to prove that UALoE can turn that capacity advantage into rack-scale performance.
Silicon
DGX Spark vs RTX 5090: capacity or speed, the choice that decides your local LLM
The DGX Spark and the RTX 5090 answer two opposite questions: hosting a very large model, or serving a mid-size one fast. The duel in numbers, measurements and mechanisms included.
Silicon
How much VRAM do you need to run an LLM locally?
How much VRAM does a local LLM need? The ~2 GB per billion parameters rule, the weight of the KV cache, what quantization changes, and what actually fits on your card.
Silicon
RTX Spark vs DGX Spark: confirmed specs and the memory question
Windows on Arm or Linux, up to 128 GB of memory: what NVIDIA confirms, what DGX Spark measurements tell us, and what remains unknown about RTX Spark.
Silicon
TorchTPU, XLA, JAX: how Google attacks NVIDIA's software lock-in
TorchTPU, PyTorch/XLA, JAX, XLA and vLLM form a stack whose keystone is a compiler. How Google attacks the one asset that keeps developers on NVIDIA: the cost of leaving CUDA.