Kimi K2.5 runs on RTX 3060 with 768GB Intel Optane memory at 4 tokens per second
A Chinese AI enthusiast demonstrated the Kimi K2.5 model running on an Nvidia RTX 3060 GPU with 768GB of Intel Optane memory. This setup achieved a performance of four tokens per second, showcasing the capabilities of a trillion-parameter model on consumer hardware. The experiment highlights the potential for using legacy components to run advanced AI models typically reserved for high-end infrastructure.
- ▪The Kimi K2.5 model has a total of 1 trillion parameters but activates only 32 billion at a time for each token generated.
- ▪The full model size is approximately 630 GB, necessitating the use of 768 GB of Intel Optane Persistent Memory.
- ▪APFrisco's demonstration was notable as it utilized a mid-range GPU designed for gaming rather than AI workloads.
3 outlets in our directory ran this story, first to last over 19 hours. All of the coverage we found sits in one bucket: centre. That one-sidedness is itself worth noticing.
- ▪ 768GB of cheap Intel Optane DIMM memory sticks used to run 1-trillion-parameter LLM on a system with a single GPU — local Kimi K2.5 install achieved roughly 4 tokens per second — r/singularity
- ▪ 768GB of cheap Intel Optane DIMM memory sticks used to run 1-trillion-parameter LLM on a system with a single GPU — local Kimi K2.5 install achieved roughly 4 tokens per second — Tom's Hardware
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| Original publisher | Crypto Briefing |
| Canonical URL | https://cryptobriefing.com/kimi-k2-5-rtx-3060-optane-memory-local-inference/ |
| Publication time | Sun, 24 May 2026 06:16:51 +0000 |
| Retrieval time | 2026-05-24T06:37:31.105Z |
| Last seen | 2026-05-24T06:37:31.105Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 6j7ol5BTz8eH · 3 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Kimi K2.5 runs on RTX 3060 with 768GB Intel Optane memory at 4 tokens per second A Chinese AI enthusiast squeezed a trillion-parameter model onto consumer hardware using second-hand memory DIMMs, and the implications go far beyond the stunt itself. Share Add us on Google by Editorial Team May. 24, 2026 window.sevioads = window.sevioads || []; var sevioads_preferences = []; sevioads_preferences[0] = {}; sevioads_preferences[0].zone = "01f21ccf-2092-46b1-9ac7-8c44cc782e0f"; sevioads_preferences[0].adType = "native"; sevioads_preferences[0].inventoryId = "c5700508-581b-472c-8fdd-a931cdbfc8e1"; sevioads_preferences[0].accountId = "1e47efc1-ec2d-4fca-a8b9-354e249e5095"; sevioads.push(sevioads_preferences); A trillion-parameter AI model just ran on a graphics card that most gamers would…
Excerpt limited to ~120 words for fair-use compliance. The full article is at Crypto Briefing.
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