Self-Hosting LLM Inference vs an API: The Break-Even Math
Is it cheaper to run an open model on your own GPUs than to call an API? The deciding number isn't the token price — it's how busy the GPU stays.
5 min
vLLM vs TensorRT-LLM vs TGI: Choosing a Production LLM Serving Engine
Three engines, one job: turn a model into a high-throughput endpoint. The feature gaps are closing — what's left is portability, vendor lock-in, and which project is still being built.
5 min
vLLM vs SGLang vs LMDeploy: Picking a Self-Hosted Inference Engine in 2026
With TGI archived and Hugging Face pointing everyone at vLLM and SGLang, the open-source serving field narrowed to three real choices. They hit nearly the same throughput ceiling from opposite directions — so speed is not the thing you're actually picking.
4 min
NVIDIA NIM vs vLLM vs TGI: How to Self-Host LLM Inference in 2026
One of these isn't an inference engine at all — it's a wrapper around the other two. Sorting that out is the whole decision, and it just got simpler because one contender quietly left the race.
4 min
Ollama vs LM Studio vs Jan: Running LLMs Locally in 2026
They all wrap roughly the same inference engine, so they all run the same model at roughly the same speed. The thing that actually separates them is what shape they want to be — a daemon, a polished app, or an open one.
4 min
MLX vs llama.cpp: Which Engine Should Run LLMs on Apple Silicon
Ollama just ripped out llama.cpp and bolted in Apple's MLX on the Mac. The switch is a tell about where your bottleneck actually lives — and when the older engine still wins.
4 min
Text Generation Inference Is Archived: Migrating Off TGI in 2026
Hugging Face's TGI went read-only in March. The way it wound down — not the fact that it did — tells you where model serving actually settled.
4 min
vLLM v0.24 Makes Model Runner V2 the Default. The Win Isn't a Faster Kernel — It's Never Waiting on the CPU.
The June 29 release flips vLLM's rebuilt execution core on by default and lands a Rust serving front-end. The throughput comes from deleting the CPU–GPU sync, not from a hotter matmul.
5 min
TensorRT-LLM Is Removing the TensorRT Backend — the PyTorch Runtime Won
The library named after TensorRT is deleting TensorRT. The June 30 release candidate is the last to support the compiled engine backend; the next version removes it. The lesson isn't about NVIDIA — it's about which tradeoff keeps winning.
4 min
GPU for LLM Inference: H100 vs H200 vs A100 vs L40S
Buyers shop for these cards by peak FLOPS. Token generation barely uses them. The spec that actually moves inference throughput is the one most spec sheets bury — and a single NVIDIA card proves it.
5 min
B200 vs H200 vs H100 for LLM Inference: Pick by Memory Wall, Not Peak FLOPS
The B200's headline 5-6x throughput jump is two different upgrades wearing one number — bigger HBM and FP4 compute — and which one matters depends entirely on whether your workload is memory-bound or compute-bound.
5 min
AMD MI300X vs NVIDIA H100 for LLM Inference: The Memory Wall and the Software Tax
It isn't a FLOPS race. Decode is memory-bound, and the MI300X's 192 GB lets a model live on fewer GPUs than an 80 GB H100 can. The catch was never the silicon — it was ROCm. Here's where that tax stands in 2026.
5 min
TPU vs GPU for LLM Inference in 2026: It Comes Down to the Network, Not the Chip
Per chip, Google's Ironwood and Nvidia's B200 are now within ten percent of each other on every number that used to decide this. The real fork is the interconnect — and vLLM just deleted the reason you couldn't cross it.
6 min
AWS Trainium vs NVIDIA GPU for LLM Inference: The Bill Is Cheaper, the Onramp Isn't
Trainium2 and Inferentia2 sell real price-performance and AWS capacity. NVIDIA sells CUDA. The decision is whether the Neuron SDK supports your model and serving stack — and how much engineering you'll spend finding out.
5 min
OpenAI's Jalapeño Chip: The Real Bet Behind a Custom Inference ASIC
OpenAI's first silicon claims roughly 50% cheaper inference than Nvidia. The number is self-reported and unverifiable — but the vertical-integration bet underneath it is the part actually worth understanding.
4 min
Tenstorrent Built a CPU for the Agent Loop: Inside TT-Ascalon S
The AI-hardware story has been about matmul for a decade. Tenstorrent's new RISC-V core is a bet that the agentic bottleneck is quietly moving back onto the CPU's branch-heavy control plane.
4 min
Groq vs Cerebras vs SambaNova: The Race for Faster-Than-GPU Inference
Three startups built custom silicon to outrun the GPU on token generation. The speed is real, the SRAM is tiny, and that tradeoff decides everything.
5 min
Should You Run AI Agents on a DGX Spark? The Number That Decides Isn't 128GB
NVIDIA sells the Spark as a 200B-parameter supercomputer for your desk. The spec that actually decides whether it's right for you is a much quieter one — and it's on the memory bus, not the die.
5 min
CoreWeave vs Lambda vs Nebius: How to Actually Pick a GPU Cloud in 2026
The neocloud pitch started as 'cheap raw GPUs vs AWS.' In 2026 the scarce input isn't price — it's powered, networked racks — and the category has quietly split into two businesses that barely compete.
4 min
Together AI Raised $800M at an $8.3B Valuation. The Story Is What Agents Did to Inference Pricing.
A neocloud that owns none of the models it serves just booked $1.15B a year. The number that matters isn't the valuation — it's that open-model inference outgrew the labs whose weights it runs.
5 min
Continuous Batching vs Static Batching: Why LLM Serving Throughput Jumps an Order of Magnitude
Static batching wastes the GPU because LLM outputs are variable-length — short replies idle while the batch waits for the longest. Continuous batching schedules at every token step instead. The catch is that the same trick that wins throughput can spike latency.
4 min
Why LLM Inference Has Two Speeds: Continuous Batching and Prefill/Decode Disaggregation
A single tokens-per-second number hides two workloads pulling in opposite directions — and the whole arc of serving optimization is the field admitting they should never share a GPU.
5 min
Tensor Parallelism vs Pipeline Parallelism: How to Split an LLM Across GPUs
When one model won't fit on one GPU, you have two ways to cut it up — and the right cut is a description of your interconnect, not a tuning knob you guess at.
4 min
BentoML vs Ray Serve vs KServe: Choosing a Model-Serving Framework
Three ways to put a model behind an endpoint — and they increasingly run the same engine underneath, so the thing you are actually choosing is not speed.
5 min
Speculative Decoding, Explained: Why EAGLE Beats Medusa for Faster LLM Inference
Speculative decoding makes a single LLM response 2–6x faster without changing a token of the output. The reason it works — and why the newest method wins — is a fact about your GPU, not your model.
5 min
MHA vs MQA vs GQA vs MLA: How Attention Stopped Eating Your KV Cache
Every attention variant since 2019 has been one argument about the same scarce resource — the key-value cache — and the newest answer changes the terms of the deal.
5 min
FlashAttention vs PagedAttention vs FlashInfer: Three Different Problems, One Word
Stop choosing between them. FlashAttention is the compute kernel, PagedAttention is the memory layout, FlashInfer is the engine — a modern stack runs all three at once.
5 min
PyTorch 2.13 Just Changed the Math on Running Models on a Mac
FlexAttention landed on Apple Silicon with up to a ~12x speedup on sparse patterns, and a new fused loss cuts training memory 4x. For a founder whose whole 'cluster' is one MacBook and one rented GPU, that's a budget line, not a footnote.
4 min
How to Use FlexAttention on Apple Silicon: Sliding-Window and Document Masks with block_mask
PyTorch 2.13 brought the fused FlexAttention kernel to the Metal (MPS) backend. Here's the working code for the three masks you'll actually reach for — causal, sliding-window, and document-packed — on the Mac you already own.
4 min
KV Cache Quantization: The Memory That Actually Caps Your LLM Throughput
You quantized the weights to 4-bit and thought memory was solved. At long context the KV cache dwarfs the weights — and it needs a different kind of quantization to shrink safely.
4 min
KV Cache Eviction: StreamingLLM vs H2O vs SnapKV vs Quest
Three of these throw tokens away to save memory. One keeps them all and just reads less — and for a long-running agent that revisits its own past, that difference is the whole game.
5 min
KV Cache Offloading: LMCache vs Mooncake vs NVIDIA Dynamo
Your engine computes a KV cache, uses it once, and throws it away. Offloading turns that scratchpad into a shared storage tier — and changes the question you should be asking.
4 min
Temperature vs Top-p vs Top-k: How LLM Sampling Actually Works
Three of these knobs do the same job — truncate the unreliable tail of the next-token distribution. The differences are smaller, and more contested, than the tutorials admit. And if you build agents, you probably want almost none of it.
5 min
Why Your LLM Isn't Reproducible at Temperature 0 — and How to Fix It
Setting temperature to 0 doesn't make an LLM deterministic. The real culprit isn't sampling or 'random' GPU math — it's that your request's output depends on who else is in the batch.
4 min
tiktoken vs SentencePiece vs Hugging Face Tokenizers
Three libraries everyone compares as if you get to choose. You don't — your model already chose for you. The real question is what that choice costs, and who pays it.
5 min
LiteLLM vs Portkey vs TensorZero: Choosing an LLM Gateway in 2026
Every agent ends up talking to more than one model provider. The library you put in the middle decides whether that seam stays a proxy or quietly becomes your control plane.
4 min
The Self-Hosted AI Gateway: 7 Open-Source Proxies That Became the Agent Control Plane
The 'AI gateway' stopped being a cost-tracking load balancer and turned into the policy layer for autonomous agents — and that shift is why the newcomers are all written in Go and Rust, benchmarking themselves against LiteLLM.
4 min
OpenRouter vs LiteLLM: Which LLM Gateway for Your AI Agent Stack?
They get filed as rivals because both promise "one API for every model." But one is a hosted marketplace you buy from, the other is infrastructure you run — and the smart move is often to use both.
5 min
RouteLLM vs NotDiamond vs Martian: Do LLM Model Routers Actually Cut Costs?
Per-prompt model routing promises GPT-quality answers at a fraction of the bill. The honest 2026 answer is that it's a cost lever with a threshold, not a free one — and a neutral benchmark disagrees with the marketing.
5 min
The vLLM Semantic Router: The Gateway That Decides Whether to Reason at All
Every LLM router you know picks a model. This one runs inside Envoy's data plane and turns reasoning off — and on MMLU-Pro that raised accuracy 10 points while halving tokens.
4 min
LLM Inference Latency: TTFT vs TPOT vs Throughput, and Why 'Tokens Per Second' Is Two Numbers
The three numbers everyone quotes measure three different bottlenecks — and per-user speed and system throughput move in opposite directions, so a vendor's headline tok/s can mean whatever flatters it.
5 min
How to Reduce AI Agent Latency
Buying a faster model is the reflex, and usually the wrong first move. An agent's wait is a chain of serial round-trips — so the latency is in the loop, not the tokens-per-second.
4 min
How to Reduce AI Agent Token Costs
The cheaper-model reflex is the wrong first move. An agent's bill is dominated by the transcript it re-sends on every step — so the money is in the context, not the price card.
5 min
Semantic Caching Quietly Breaks AI Agents — and Accuracy Isn't the Fix
A cache that skips a duplicate chatbot answer is a savings. A cache that skips a duplicate agent step is a wrong action. New 2026 benchmarks show the standard tools score under 40% — and the fix is the opposite of what you'd guess.
4 min
Batch API vs Real-Time Inference: The 50% Discount Isn't Why You Should Use It
Every provider now sells the same deal — hand over your requests, wait up to 24 hours, pay half. The savings are real, but the reason to reach for batch is the thing nobody puts on the pricing page.
4 min
Can You Run an AI Agent on the Batch API? Mostly Not — and What to Batch Instead
An agent is a chain of steps that each depend on the last, so a 24-hour batch window can't sit on the critical path. You can't batch the loop — but the token-heavy work around it is exactly what batch was built for.
4 min
How Many GPUs Does Your AI Agent Need? NVIDIA's NeMo Agent Toolkit Sizes the Cluster by Load Test, Not Math
The sizing calculator in NVIDIA's NeMo Agent Toolkit profiles a multi-agent workflow under concurrency and extrapolates a GPU count. The quiet lesson: an agent's cost is emergent, not calculable.
5 min
Multi-Region LLM Failover: Uptime Is the Easy Part — Your Cache and Your Data Residency Are the Bill
The managed cross-region inference you turned on already solved availability. It did it by dissolving the region boundary you may be legally required to keep — and by throwing away your prompt cache at the exact moment you're already degraded.
5 min