How to Turn Local AI into a SUPER BEAST 🤯 (Multiprocessing Explained)
Credibility score: 49/100 — Mixed Credibility. Several questionable claims detected. Watch with healthy skepticism.
BSmeter analyzed "How to Turn Local AI into a SUPER BEAST 🤯 (Multiprocessing Explained)" and rated it 49/100 for credibility (a BS score of 51/100 — mixed credibility), on 2026-06-04. Its weakest claim — "Kimmy Kane 2.6 Q4.24 quant is ~99% quality" — scored 35/100 and was flagged as sketchy. 9 claims were checked against the video transcript. Scores are produced by BSmeter's AI analysis of the transcript, not independent human verification.
Of 9 claims analyzed: 1 scored under 40, 7 between 40 and 69, and 1 at 70 or above.
Claims analyzed
Multiprocessing lets you run 4+ models simultaneously for max local AI performance. — Just Vibes (50/100)
At 0:00
Four models running at once? That's ambitious hype! — But yeah, batching is the key to unlocking that beast mode. 🤯🚀
Why this score: High potential for performance gain. The speaker claims simultaneous inference of four different models while maintaining distributed computing. This aligns perfectly with current trends in local AI optimization (like MoE models mentioned in the web context). It's a massive leap from single-model runs.
Original quote: “And with multiprocessing, it's now held both models into memory. So if I wanted to do a Deep Seek inference right now, it's going ahead and continuing that story. [music] So we got four different models being inferenced at the same time. That's that's awesome. And still got the distributed comput…”
Seed gives 18 quintillion different generation paths — Dubious (45/100)
At 0:30
18 quintillion is a cute big number — actual space depends on vocab size and context length.
Why this score: For a 32k context and ~32k vocab the theoretical space is enormous, but the speaker's figure appears to be a rounded 2^64 reference rather than a precise count of reachable sequences. Not technically wrong in spirit, but presented as exact without showing the math.
Original quote: “there's something called seed. And with seed, there's actually 18 quintilion different choices to make and that will make it traverse the tree slightly different”
Multiprocessing produces identical deterministic outputs simultaneously — Dubious (40/100)
At 2:30
Outputs shown are identical but that's the *goal*, not proof it always works.
Why this score: - Two identical strings displayed doesn't prove determinism across runs.
- Temperature, seed, and sampling settings aren't mentioned.
- One cherry-picked example isn't strong evidence.
Original quote: “So now we got two deterministic responses both at the same time.”
Multiprocessing lets two models stay loaded while hitting 29-30 tokens/sec — Dubious (45/100)
At 4:30
Numbers sound plausible but no hardware specs given to verify — typical for these demos
Why this score: No baseline provided.
- Speaker gives token rates but never states which models or exact hardware
- Without knowing VRAM/GPU count, 29-30 t/s is impossible to validate
- Common issue in local AI demos where "it works on my machine" replaces actual specs
Original quote: “we're going around 29 to 30 tokens a second and with multiprocessing it's now held both models into memory”
Running four models simultaneously with multiprocessing — OK (60/100)
At 6:30
Sounds plausible on paper — actual memory math is the real question.
Why this score: Concurrent inference is technically possible with enough RAM.
- No specific hardware specs or VRAM numbers given to verify
- Modern Mac unified memory setups can handle multiple models but performance drops fast
- Community comments ask exactly this: how much unified memory is needed
Original quote: “So we got four different models being inferenced at the same time. Two using the server API and two separate ones here.”
Software capped at eight simultaneous inferences — Unverifiable (50/100)
At 6:42
Feature preview claim with zero external proof.
Why this score: Arbitrary limit mentioned but not demonstrated.
- No code, settings, or documentation shown
- Could be a real constraint or just the current test setup
- Web results don't reference this specific tool's limits
Original quote: “I think I limited it though to eight inferences at the same time. I can always change the limit.”
GPT hitting 11 tokens/sec while Llama runs in background — Dubious (45/100)
At 6:55
Token speeds thrown out with zero context on model size or hardware.
Why this score: 11 t/s sounds decent but depends entirely on quantization and GPU.
- No model sizes, quant levels, or hardware specs attached to the number
- Background Llama running simultaneously makes the claim even harder to evaluate
- Without benchmarks, it's just a vibe
Original quote: “GPT is going 11 tokens a second times two of course. And we still got Llama running in the background here on our server API.”
Distributed compute lets you cluster multiple machines for bigger models — Solid (75/100)
At 7:10
✅
Why this score: Distributed inference is a real technique.
- 2026 guides confirm tensor/pipeline parallelism and remote memory pooling exist for local AI
- Speaker correctly distinguishes direct connection from full clustering
- Matches current technical reality
Original quote: “So distributed compute, it allows us to run even larger models using multiple computers at the same time. So there's something called a cluster feature here which you can define a selection of computers to connect to to make a whole big network.”
Kimmy Kane 2.6 Q4.24 quant is ~99% quality — Sketchy (35/100)
At 7:35
"99% quality" is marketing speak, not a measurable stat 💀
Why this score: Q4.24 is a specific quantization format.
- No standard benchmark measures "99% quality" — it's not how quantization works
- Quality loss is model-dependent and usually measured in perplexity or downstream tasks
- Sounds impressive but lacks any actual comparison data
Original quote: “So I've got special edition of Kimmy Kane 2.6. And this is a Q 4.24 quant. It's almost like 99% quality.”
See the full analysis with timestamps →