Running Kimi K3 on MI355X at Better Performance per Dollar Than B300
July 31, 2026Ian YeIs memory the moat?Running Kimi K3 at ~952 tok/s/node, AMD continues to prove its case as the winner in performance per dollar.Over the past several months, we’ve seen an explosion in the capabilities of open source models. With DeepSeek V4-Pro and GLM5.2 reaching near-Opus levels of intelligence, open source has emerged as a real, cost-efficient alternative to the closed source models we’ve been married to. Promising Fable/Sol levels of intelligence, Kimi K3 marks the start of a new era for open source.
- ▪July 31, 2026Ian YeIs memory the moat?Running Kimi K3 at ~952 tok/s/node, AMD continues to prove its case as the winner in performance per dollar.Over the past several months, we’ve seen an explosion in the capabilities of open source model
- ▪With DeepSeek V4-Pro and GLM5.2 reaching near-Opus levels of intelligence, open source has emerged as a real, cost-efficient alternative to the closed source models we’ve been married to.
- ▪Promising Fable/Sol levels of intelligence, Kimi K3 marks the start of a new era for open source.
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Record
| Original publisher | Wafer |
| Canonical URL | https://www.wafer.ai/blog/kimi-k3-mi355x |
| Publication time | Sun, 02 Aug 2026 04:21:14 +0000 |
| Retrieval time | 2026-08-02T05:05:40.173Z |
| Last seen | 2026-08-02T05:05:40.173Z |
| 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 | oDCpdJBWwJNC · 1 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 |
Rights status (four layers)
WeSearch handling by dimension
| 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
July 31, 2026Ian YeIs memory the moat?Running Kimi K3 at ~952 tok/s/node, AMD continues to prove its case as the winner in performance per dollar.Over the past several months, we’ve seen an explosion in the capabilities of open source models. With DeepSeek V4-Pro and GLM5.2 reaching near-Opus levels of intelligence, open source has emerged as a real, cost-efficient alternative to the closed source models we’ve been married to. But we have yet to see one like Kimi K3. Promising Fable/Sol levels of intelligence, Kimi K3 marks the start of a new era for open source. But a smarter model means a bigger model — and these models are expanding in size just as fast as they are in capabilities. GLM5.2 has 753B parameters, DeepSeek V4-Pro 1.6T, and Kimi K3 weighs in at 2.8T (!!) parameters.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Wafer.
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