VRAMGlass
Runyard.dev
What LLM Can I Run
slopsome.com
SelfHostLLM
Pick your GPU or Apple Silicon chip and see which open-weight LLMs fit — with a recommended model per job (coding, reasoning, vision, agents, speed), quantised weight sizes, KV cache at your context length, estimated tokens per second.

Splitwise
Splitaha allows users to easily manage shared expenses between friends, families, and individuals

Which is more popular?
Website, pricing, platforms and company facts side by side.
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RML
RunMyLLM
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|---|---|---|
| Website | runmyllm.com | splitaha.com |
| Pricing | — | |
| Listed in |
What each product offers, as listed by its team.

Possible disadvantages
Possible disadvantages
How often each product is chosen within a category, 0–100% relative to the other.

Share your experience with using RunMyLLM and Splitaha. For example, how are they different and which one is better?
When comparing RunMyLLM and Splitaha, you can also consider the following products.

Find the right GPU for local LLMs. Compare VRAM, GPU prices and model memory requirements to choose hardware that fits your models and budget.
Compare VRAMGlass to RunMyLLM or Splitaha:

Splitwise is a free tool for friends and roommates to track bills and other shared expenses, so that everyone gets paid back. On the web, iPhone, and Android!
Compare Splitwise to RunMyLLM or Splitaha:

Hardware-aware AI model discovery. Enter your GPU and VRAM — instantly see every LLM that fits, ranked by speed and quality.
Compare Runyard.dev to RunMyLLM or Splitaha:

Hardware-first ranking of local LLMs. Tell us your machine, get every model that fits — ranked by real benchmarks (LiveBench, Aider, Arena), with the math shown.
Compare What LLM Can I Run to RunMyLLM or Splitaha:

Search engine for LLM & GPU stats — compare local open-weight and API models and the GPUs that run them. See what fits your rig, how fast, and at what cost. Community reviews, real tokens/sec and a live VRAM fit-calculator.
Compare slopsome.com to RunMyLLM or Splitaha:
Calculate the GPU memory you need for LLM inference
Compare SelfHostLLM to RunMyLLM or Splitaha: