Workload Picker

Get a stack tuned to how you actually work.

Tell us the use case, your latency & memory budget, and how big you plan to cluster. We'll recommend an agent stack, a model set, and the right hardware tier from the registry.

Use case
Effective pool: 64 GB across 1 node

Recommended stack

Fit score 97/100

llama.cpp + Aider

Minimal coding-agent loop. Best latency on 13B–32B coders. No daemon required.

llama.cppAiderripgrep

Min config: 32GB Spark laptop

Model set

Qwen3-Coder-32B

32B · Q4_K_M · min 24 GB

34 tok/s

~235 ms ftt

Top open coder for Spark. Excellent at long refactors with 32k context.

Qwen3-7B

7B · Q5_K_M · min 8 GB

96 tok/s

~83 ms ftt

Default planner for multi-agent meshes. Sub-10ms first token on Spark.

Llama-3.1-13B

13B · Q5_K_M · min 14 GB

62 tok/s

~129 ms ftt

Cheap, reliable assistant. Pair with a 70B for reasoning hand-off.

Hardware shortlist

NVIDIA

DGX Spark Reference

128 GB · SCS 95 · $3,999

MSI

Spark Titan Desktop

128 GB · SCS 96 · $3,899

Dell

Precision S16 RTX Spark

128 GB · SCS 94 · $3,499