App B The Napkin Math: Predicting Token Speed from Memory Bandwidth
One formula tells you whether your hardware is working correctly or misconfigured. The community used this to diagnose the 40 t/s problem.
Dgx_series
Browse subfolders and articles in Dgx_series
Subfolders
Tags in this section
Articles
App A Why Blackwell's Native MX-Format Support Matters
Blackwell's native MX-format support eliminates the dequantization tax — the hardware and gpt-oss-120b were designed for each other.
9/9 Claude Code with Local Models: The Series Finale
Everything in the series built toward this: Claude Code running on locally served models. Here's what works, what's rough, and where it's heading.
8/9 The Recipe System: One Command, Zero Flag Archaeology
The recipe system from spark-vllm-docker turns twenty minutes of flag archaeology into one command — and makes everything reproducible.
7/9 Solo vs Cluster: Where Two Sparks Beat One (and Where They Don't)
Neither topology dominates — cluster wins decode at every concurrency level, but 2x Solo wins prefill and TTFT under load. The right choice depends on the workload.
6/9 Two Sparks, One Cluster: Setting Up with Claude Code
The second DGX Spark arrived. Before writing a single line of config: check firmware. Then cables, SSH, Docker, vLLM, model cache — and Claude Code helping build the skills to manage it all.
5/9 LiteLLM: The Translation Layer Between Claude Code and Local Models
Claude Code speaks Anthropic. gpt-oss-120b speaks OpenAI with Harmony-style tool calls. LiteLLM sits in the middle and translates — including a custom callback that patches the tool calls neither side gets right.
4/9 Benchmarking Reality: llama-benchy and the Spark Arena
Post 3 hit 4,158 t/s at c64. llama-benchy puts those numbers under the microscope. Same hardware, two tools, 25x difference in TTFT.
3/9 Switching to vLLM: From 40 t/s to 3,975 t/s
Every reviewer tested single-user latency and called the DGX Spark slow. Nobody tested concurrency. The community found the real number: 3,975 tokens per second.
2/9 First Steps: Running Models on Ollama (20B → 120B)
The standard recipe works but wastes the hardware. Scaling from 20B to 120B on Ollama shows the potential — and the ceiling.
1/9 When nvidia-smi Goes Blind: Setting Up btop on DGX Spark
NVIDIA's own monitoring can't see their newest hardware. The community had a fix before NVIDIA did.
Intro Bring It Home First
Specs can lie in both directions. Snake oil oversells. Reviews undersell. The only truth is your own testing.