If implemented, TurboQuant could make AI models less expensive to run and less hungry for memory, though it's possible the companies creating this technology could use that newly freed-up memory to run more complex models.
Google's TurboQuant AI-compression algorithm can reduce LLM memory usage by 6x
TurboQuant makes AI models more efficient but doesn't reduce output quality like other methods.
arstechnica.com
March 26, 2026 at 4:07 PM
If implemented, TurboQuant could make AI models less expensive to run and less hungry for memory, though it's possible the companies creating this technology could use that newly freed-up memory to run more complex models.
The sourcing on this is so funny because it’s yahoo finance republishing the telegraph citing wccftech who themselves blame TurboQuant (??) and not data centers and their example is that you can get 32 GB of RAM for 379.99, which is still not a great deal.
Good news! OpenAI is canceling so many large purchase orders and datacenter expansions that the global price of RAM is dropping.
OpenAI funding fears hit memory chip prices
Spending cuts at OpenAI have hit memory chip prices as fears grow about the tech company’s ability to fund its expansion.
finance.yahoo.com
March 30, 2026 at 3:02 PM
The sourcing on this is so funny because it’s yahoo finance republishing the telegraph citing wccftech who themselves blame TurboQuant (??) and not data centers and their example is that you can get 32 GB of RAM for 379.99, which is still not a great deal.
really interesting! also turboquant was pretty overhyped tho apparently
www.youtube.com/watch?v=haoA...
www.youtube.com/watch?v=haoA...
Google's TurboQuant Memory Reduction Claim vs Reality
YouTube video by bycloud
www.youtube.com
May 12, 2026 at 9:42 AM
really interesting! also turboquant was pretty overhyped tho apparently
www.youtube.com/watch?v=haoA...
www.youtube.com/watch?v=haoA...
TurboQuant wurde von Google Research entwickelt, um die Effizienz von LLMs durch extrem starke Datenkompression massiv zu steigern.
Es wird bis zu 6-mal weniger Speicher benötigt, während die Verarbeitungsgeschwindigkeit um das 8-fache steigen kann.
research.google/blog/turboqu...
Es wird bis zu 6-mal weniger Speicher benötigt, während die Verarbeitungsgeschwindigkeit um das 8-fache steigen kann.
research.google/blog/turboqu...
TurboQuant: Redefining AI efficiency with extreme compression
research.google
May 6, 2026 at 6:17 AM
TurboQuant wurde von Google Research entwickelt, um die Effizienz von LLMs durch extrem starke Datenkompression massiv zu steigern.
Es wird bis zu 6-mal weniger Speicher benötigt, während die Verarbeitungsgeschwindigkeit um das 8-fache steigen kann.
research.google/blog/turboqu...
Es wird bis zu 6-mal weniger Speicher benötigt, während die Verarbeitungsgeschwindigkeit um das 8-fache steigen kann.
research.google/blog/turboqu...
AIを数分の1のデータ量で動かすための「TurboQuant」の仕組みをインタラクティブな図解で把握できるサイト「TurboQuant: A First-Principles Walkthrough」
https://gigazine.net/news/20260428-turboquant-a-first-principles-walkthrough/
https://gigazine.net/news/20260428-turboquant-a-first-principles-walkthrough/
AIを数分の1のデータ量で動かすための「TurboQuant」の仕組みをインタラクティブな図解で把握できるサイト「TurboQuant: A First-Principles Walkthrough」
Google Researchは大規模言語モデルとベクトル検索エンジンのための新しい圧縮技術群の1つとして、「TurboQuant」を2026年3月に発表しました。AIで大きな負担になっているメモリ使用量を減らしつつ、処理速度と検索性能も高めることを目的としたこのTurboQuantとはどういうものなのかを視覚的に理解するためのサイト「TurboQuant: A First-Principles Walkthrough」が公開されています。
gigazine.net
April 28, 2026 at 1:01 PM
AIを数分の1のデータ量で動かすための「TurboQuant」の仕組みをインタラクティブな図解で把握できるサイト「TurboQuant: A First-Principles Walkthrough」
https://gigazine.net/news/20260428-turboquant-a-first-principles-walkthrough/
https://gigazine.net/news/20260428-turboquant-a-first-principles-walkthrough/
If Google’s AI researchers had a sense of humor, they would have called TurboQuant, the new, ultra-efficient AI memory compression algorithm announced Tuesday, “Pied Piper” — or, at least that’s what the internet thinks.
Google unveils TurboQuant, a new AI memory compression algorithm — and yes, the internet is calling it 'Pied Piper' | TechCrunch
Google’s TurboQuant has the internet joking about Pied Piper from HBO's "Silicon Valley." The compression algorithm promises to shrink AI’s “working memory” by up to 6x, but it’s still just a lab experiment for now.
spr.ly
March 25, 2026 at 9:03 PM
If Google’s AI researchers had a sense of humor, they would have called TurboQuant, the new, ultra-efficient AI memory compression algorithm announced Tuesday, “Pied Piper” — or, at least that’s what the internet thinks.
Google's TurboQuant
Their new compression algorithm that reduces LLM key-value cache memory by at least 6x and delivers up to 8x speedup, all with zero accuracy loss, redefining AI efficiency.
research.google/blog/turboqu...
Their new compression algorithm that reduces LLM key-value cache memory by at least 6x and delivers up to 8x speedup, all with zero accuracy loss, redefining AI efficiency.
research.google/blog/turboqu...
March 25, 2026 at 12:32 AM
Google's TurboQuant
Their new compression algorithm that reduces LLM key-value cache memory by at least 6x and delivers up to 8x speedup, all with zero accuracy loss, redefining AI efficiency.
research.google/blog/turboqu...
Their new compression algorithm that reduces LLM key-value cache memory by at least 6x and delivers up to 8x speedup, all with zero accuracy loss, redefining AI efficiency.
research.google/blog/turboqu...
the big lesson for researchers from Google’s TurboQuant paper — if you post your work in a place where nobody will see it, then nobody will see it
March 26, 2026 at 12:25 PM
the big lesson for researchers from Google’s TurboQuant paper — if you post your work in a place where nobody will see it, then nobody will see it
turbovec est un projet écrit en Rust sous licence MIT et basé sur TurboQuant, permettant de créer un index vectoriel pour indexer et rechercher localement avec compression mémoire, ingestion en ligne et recherche filtrée, des bindings Python, ... ⬇️
github.com/RyanCodrai/t...
github.com/RyanCodrai/t...
GitHub - RyanCodrai/turbovec: A vector index built on TurboQuant, written in Rust with Python bindings
A vector index built on TurboQuant, written in Rust with Python bindings - RyanCodrai/turbovec
github.com
June 9, 2026 at 4:47 AM
turbovec est un projet écrit en Rust sous licence MIT et basé sur TurboQuant, permettant de créer un index vectoriel pour indexer et rechercher localement avec compression mémoire, ingestion en ligne et recherche filtrée, des bindings Python, ... ⬇️
github.com/RyanCodrai/t...
github.com/RyanCodrai/t...
Google unveils TurboQuant, a new AI memory compression algorithm — and yes, the internet is calling it ‘Pied Piper’
Google unveils TurboQuant, a new AI memory compression algorithm — and yes, the internet is calling it 'Pied Piper' | TechCrunch
Google’s TurboQuant has the internet joking about Pied Piper from HBO's "Silicon Valley." The compression algorithm promises to shrink AI’s “working memory” by up to 6x, but it’s still just a lab experiment for now.
techcrunch.com
March 25, 2026 at 9:04 PM
Google unveils TurboQuant, a new AI memory compression algorithm — and yes, the internet is calling it ‘Pied Piper’
I missed this announcement and didn’t learn about it until this evening
Here is a v0.0.1 version of PolarQuant; 2/3 of TurboQuant specifically for embeddings and cosine similarity— the fancy QJL was found to reduce accuracy for this specific use case
github.com/oaustegard/p...
To be continued…
Here is a v0.0.1 version of PolarQuant; 2/3 of TurboQuant specifically for embeddings and cosine similarity— the fancy QJL was found to reduce accuracy for this specific use case
github.com/oaustegard/p...
To be continued…
April 2, 2026 at 5:12 AM
I missed this announcement and didn’t learn about it until this evening
Here is a v0.0.1 version of PolarQuant; 2/3 of TurboQuant specifically for embeddings and cosine similarity— the fancy QJL was found to reduce accuracy for this specific use case
github.com/oaustegard/p...
To be continued…
Here is a v0.0.1 version of PolarQuant; 2/3 of TurboQuant specifically for embeddings and cosine similarity— the fancy QJL was found to reduce accuracy for this specific use case
github.com/oaustegard/p...
To be continued…
Turbovec - github.com/RyanCodrai/t... - Turboquant meets RAG.
GitHub - RyanCodrai/turbovec: A vector index built on TurboQuant, written in Rust with Python bindings
A vector index built on TurboQuant, written in Rust with Python bindings - RyanCodrai/turbovec
github.com
May 15, 2026 at 12:22 PM
Turbovec - github.com/RyanCodrai/t... - Turboquant meets RAG.
This is the repo of the turboquant fork BTW: github.com/TheTom/llama...
GitHub - TheTom/llama-cpp-turboquant: LLM inference in C/C++
LLM inference in C/C++. Contribute to TheTom/llama-cpp-turboquant development by creating an account on GitHub.
github.com
May 7, 2026 at 6:07 AM
This is the repo of the turboquant fork BTW: github.com/TheTom/llama...
This week, Google announced a huge breakthrough, called TurboQuant. TurboQuant makes it significantly faster to assemble and query a vector space. Their abstract says that it reduces the time needed to build and index for vector search to "virtually zero."
www.mariehaynes.com/turboquant-h...
www.mariehaynes.com/turboquant-h...
TurboQuant has the potential to fundamentally change how Search (and AI) works - Marie Haynes
Google's TurboQuant vector search breakthrough enables real-time semantic search, gives AI agents boundless memory, and powers smarter robotics.
www.mariehaynes.com
March 27, 2026 at 3:06 PM
This week, Google announced a huge breakthrough, called TurboQuant. TurboQuant makes it significantly faster to assemble and query a vector space. Their abstract says that it reduces the time needed to build and index for vector search to "virtually zero."
www.mariehaynes.com/turboquant-h...
www.mariehaynes.com/turboquant-h...
Google's TurboQuant compresses KV cache to 3 bits with no accuracy loss — 6x memory reduction, 8x attention speedup on H100. No official code yet but llama.cpp and MLX integration is underway. Good technical breakdown with pseudocode here: turboquant.net
TurboQuant - Extreme Compression for AI Efficiency
TurboQuant is a new online vector quantization algorithm that compresses KV cache to 3 bits with zero accuracy loss, cutting memory by 6x and speeding attention up by 8x.
turboquant.net
March 29, 2026 at 2:06 AM
Google's TurboQuant compresses KV cache to 3 bits with no accuracy loss — 6x memory reduction, 8x attention speedup on H100. No official code yet but llama.cpp and MLX integration is underway. Good technical breakdown with pseudocode here: turboquant.net
AIのメモリ使用量を6分の1に削減するGoogleの「TurboQuant」はメモリ需要を減少させるどころか増加させるとの指摘
https://gigazine.net/news/20260413-google-turboquant-ram-crisis/
https://gigazine.net/news/20260413-google-turboquant-ram-crisis/
AIのメモリ使用量を6分の1に削減するGoogleの「TurboQuant」はメモリ需要を減少させるどころか増加させるとの指摘
Googleは2026年3月、AIで大きな負担になっているメモリ使用量を減らしつつ処理速度と検索性能も高める圧縮技術「TurboQuant」を発表しました。これによりAI分野のメモリ需要が減少し、メモリ価格の引き下げにつながるのではないかとの期待も寄せられていますが、複数の海外メディアは「TurboQuantはメモリ不足を解消せず、むしろメモリ需要を増加させる」との見解を示しています。
gigazine.net
April 13, 2026 at 8:01 AM
AIのメモリ使用量を6分の1に削減するGoogleの「TurboQuant」はメモリ需要を減少させるどころか増加させるとの指摘
https://gigazine.net/news/20260413-google-turboquant-ram-crisis/
https://gigazine.net/news/20260413-google-turboquant-ram-crisis/
Funny how the Google news on memory-saving compression algorithms is hitting memory stocks today. They published this two days ago. #EMH
TurboQuant: Redefining AI efficiency with extreme compression
research.google
March 26, 2026 at 11:28 AM
Funny how the Google news on memory-saving compression algorithms is hitting memory stocks today. They published this two days ago. #EMH
github.com/RyanCodrai/t...
Ring Fighter 2 Turbo.
Ring Fighter 2 Turbo.
GitHub - RyanCodrai/turbovec: A vector index built on TurboQuant, written in Rust with Python bindings
A vector index built on TurboQuant, written in Rust with Python bindings - RyanCodrai/turbovec
github.com
June 7, 2026 at 6:54 PM
github.com/RyanCodrai/t...
Ring Fighter 2 Turbo.
Ring Fighter 2 Turbo.
I do believe I've figured out how to do TurboQuant on diffusion models.
May 31, 2026 at 2:09 PM
I do believe I've figured out how to do TurboQuant on diffusion models.
piragi gets ONNX and Qdrant + TurboQuant support!
Faster embedding: Ragi("./docs", config={"embedding": {"backend": "onnx"}})
Less mem for vector storage: Ragi("./docs", store=QdrantStore(quantization="turboquant"))
pypi.org/project/pira...
#RAG #Python #ONNX #Qdrant #TurboQuant #OpenSource
Faster embedding: Ragi("./docs", config={"embedding": {"backend": "onnx"}})
Less mem for vector storage: Ragi("./docs", store=QdrantStore(quantization="turboquant"))
pypi.org/project/pira...
#RAG #Python #ONNX #Qdrant #TurboQuant #OpenSource
August 8, 2026 at 4:06 PM
piragi gets ONNX and Qdrant + TurboQuant support!
Faster embedding: Ragi("./docs", config={"embedding": {"backend": "onnx"}})
Less mem for vector storage: Ragi("./docs", store=QdrantStore(quantization="turboquant"))
pypi.org/project/pira...
#RAG #Python #ONNX #Qdrant #TurboQuant #OpenSource
Faster embedding: Ragi("./docs", config={"embedding": {"backend": "onnx"}})
Less mem for vector storage: Ragi("./docs", store=QdrantStore(quantization="turboquant"))
pypi.org/project/pira...
#RAG #Python #ONNX #Qdrant #TurboQuant #OpenSource
#TurboQuant vector index for #semanticsearch.
With TQ4 quantization on a pair with full precision for recall accuracy while significantly less disk footprint and the same latency.
#OpenSource MIT lic
#AI #RAG #SLM #Embeddings
With TQ4 quantization on a pair with full precision for recall accuracy while significantly less disk footprint and the same latency.
#OpenSource MIT lic
#AI #RAG #SLM #Embeddings
GitHub - RyanCodrai/turbovec: A vector index built on TurboQuant, written in Rust with Python bindings
A vector index built on TurboQuant, written in Rust with Python bindings - RyanCodrai/turbovec
github.com
May 9, 2026 at 12:47 PM
#TurboQuant vector index for #semanticsearch.
With TQ4 quantization on a pair with full precision for recall accuracy while significantly less disk footprint and the same latency.
#OpenSource MIT lic
#AI #RAG #SLM #Embeddings
With TQ4 quantization on a pair with full precision for recall accuracy while significantly less disk footprint and the same latency.
#OpenSource MIT lic
#AI #RAG #SLM #Embeddings
PolarQuant: 6x memory reduction 8x speed improvement
weirdly, this works for both KV-cache and vector DBs
the gist is they convert from cartesian coordinate vectors into polar coordinates, and since they’re always normalized to 1.0, they drop the magnitude too
research.google/blog/turboqu...
weirdly, this works for both KV-cache and vector DBs
the gist is they convert from cartesian coordinate vectors into polar coordinates, and since they’re always normalized to 1.0, they drop the magnitude too
research.google/blog/turboqu...
TurboQuant: Redefining AI efficiency with extreme compression
research.google
March 25, 2026 at 12:16 AM
PolarQuant: 6x memory reduction 8x speed improvement
weirdly, this works for both KV-cache and vector DBs
the gist is they convert from cartesian coordinate vectors into polar coordinates, and since they’re always normalized to 1.0, they drop the magnitude too
research.google/blog/turboqu...
weirdly, this works for both KV-cache and vector DBs
the gist is they convert from cartesian coordinate vectors into polar coordinates, and since they’re always normalized to 1.0, they drop the magnitude too
research.google/blog/turboqu...
Google's TurboQuant compresses LLM memory usage by 6× with nearly zero accuracy loss — no training, no fine-tuning, works on any transformer. This is the kind of efficiency breakthrough that makes AI systems actually deployable. 🧠 #MachineLearning #AI
TurboQuant: Redefining AI efficiency with extreme compression
research.google
June 1, 2026 at 2:49 PM
Google's TurboQuant compresses LLM memory usage by 6× with nearly zero accuracy loss — no training, no fine-tuning, works on any transformer. This is the kind of efficiency breakthrough that makes AI systems actually deployable. 🧠 #MachineLearning #AI
Google's TurboQuant landed for vLLM.cpp's KV cache – but it's not just useful for KV! A possibly more impactful implementation is for quantizing embedding vectors for RAG.
github.com/oaustegard/r... is a Python and Mojo for just that purpose.
github.com/oaustegard/r... is a Python and Mojo for just that purpose.
GitHub - oaustegard/remex: Retrieval-validated embedding compression. 4-8x smaller vectors, proven recall. Based on TurboQuant (ICLR 2026).
Retrieval-validated embedding compression. 4-8x smaller vectors, proven recall. Based on TurboQuant (ICLR 2026). - oaustegard/remex
github.com
May 2, 2026 at 3:10 AM
Google's TurboQuant landed for vLLM.cpp's KV cache – but it's not just useful for KV! A possibly more impactful implementation is for quantizing embedding vectors for RAG.
github.com/oaustegard/r... is a Python and Mojo for just that purpose.
github.com/oaustegard/r... is a Python and Mojo for just that purpose.