#Turboquant

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#Turboquant Reel by @tech.explain1 (verified account) - Are RAM prices going down? Google Turboquant is a huge breakthrough for Al. It uses a new compression technique to reduce memory needed tor Al models
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@tech.explain1
Are RAM prices going down? Google Turboquant is a huge breakthrough for Al. It uses a new compression technique to reduce memory needed tor Al models to operate. Since Al demand was the driving factor behind the recent price surge, we may see prices start to normalize.
#Turboquant Reel by @barebone.ai (verified account) - Billions wiped off AI stocks in 48 hours. Google released an algorithm called TurboQuant that compresses AI memory banks to a sixth of their original
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@barebone.ai
Billions wiped off AI stocks in 48 hours. Google released an algorithm called TurboQuant that compresses AI memory banks to a sixth of their original size. Runs 8x faster. No quality loss. The stock market sold memory chip makers immediately. If AI needs a fraction of the memory, it needs a fraction of the chips. $MU, SK Hynix, Samsung all took hits. But the sell-off has a hole in it. TurboQuant only affects inference. That's when AI answers questions. Training, where AI actually learns, still burns through the same amount of memory as before. And this pattern has played out before. DeepSeek made AI 95% cheaper to train last year. Semiconductor stocks cratered. Investors panic sold. Then companies ran more AI than ever, and $NVDA hit all-time highs months later. Every time AI gets cheaper, usage explodes. The demand doesn't shrink. It compounds. So the real question for AI hardware isn't whether memory chips lose. It's which names get repriced and which ones get bought. Barebone ran the full breakdown. Two buys and one sell across the three biggest memory chip makers. Comment TURBO to get the full analysis sent to you.
#Turboquant Reel by @marc.kaz - Introducing TurboQuant: Googles new compression algorithm that reduces LLM key-value cache memory by at least 6x and delivers up to 8x speedup, all wi
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@marc.kaz
Introducing TurboQuant: Googles 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. Read the blog post: goo.gle/4bsq2ql
#Turboquant Reel by @parthknowsai - Google's new research TurboQuant is a game changer. #ai #tech #educational #chatgpt #fyp
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@parthknowsai
Google’s new research TurboQuant is a game changer. #ai #tech #educational #chatgpt #fyp
#Turboquant Reel by @felix_ved - TurboQuant is a recently published blog post combining three research papers. It enables running the same model on consumer hardware with 4-8x larger
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@felix_ved
TurboQuant is a recently published blog post combining three research papers. It enables running the same model on consumer hardware with 4-8x larger context windows for the same or less memory. No retraining or new hardware required. Llama.cpp implementation in progress, MLX port reported. #localai #llm #aiengineering #kvcache #opensource
#Turboquant Reel by @thepatelinvestor (verified account) - Comment "update" for the private chat link Micron stock has pulled back largely on news around Google's memory compression algorithm, TurboQuant. I
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@thepatelinvestor
Comment “update” for the private chat link Micron stock has pulled back largely on news around Google’s memory compression algorithm, TurboQuant. I think the market may be overlooking something important here. This likely plays into Jevons Paradox, where increased efficiency can actually drive higher overall demand. If you want updates on the stocks I am buying, comment “update” and I will send you the link to my private chat. Disclaimer: This is not financial advice. I do not own every stock I discuss and am not recommending any specific investment. This content is for informational and learning purposes only. #stocks #stockmarket #investing
#Turboquant Reel by @schwabnetwork (verified account) - @sam_vadas breaks down how memory stocks are faring after Alphabet Google's (GOOGL) latest research, dubbed TurboQuant, hit the sector. Despite the c
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@schwabnetwork
@sam_vadas breaks down how memory stocks are faring after Alphabet Google’s (GOOGL) latest research, dubbed TurboQuant, hit the sector. Despite the current pressure, some analysts still have a bullish take on memory, noting that “the enabling of the processing of larger data sets could actually potentially increase memory demand,” Sam explains. For more, click the link in bio.
#Turboquant Reel by @dumbme.trynalearn - EP72: dumb me finds out about TurboQuant 🤯 #memory #ai #llm #turboquant #google
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@dumbme.trynalearn
EP72: dumb me finds out about TurboQuant 🤯 #memory #ai #llm #turboquant #google
#Turboquant Reel by @shyl.nmi (verified account) - TurboQuant: google to save RAM shortage
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@shyl.nmi
TurboQuant: google to save RAM shortage
#Turboquant Reel by @keshavsuki (verified account) - Comment "turbo" for the full breakdown 👇 Google TurboQuant. Your AI's memory just got 5x cheaper. **The problem:** Long conversations get slow and
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@keshavsuki
Comment “turbo” for the full breakdown 👇 Google TurboQuant. Your AI’s memory just got 5x cheaper. **The problem:** Long conversations get slow and expensive. KVCache grows with every message. After 50-100 messages, things bog down. **The solution:** Compress memory from 16 bits to 3.5 bits per number. That’s 4.5x smaller. Same accuracy. **What this means:** - 500+ message conversations without slowdown - Faster inference (less memory to process) - 5x cheaper infrastructure costs - On-device AI becomes viable **Before TurboQuant:** 50-100 message limit. Expensive. Slow. **After TurboQuant:** 500+ messages. Fast. 5x cheaper. This is research right now. Google published the paper. Implementation takes time. But this changes how we build long-context AI. Comment “turbo” for the full breakdown 👇
#Turboquant Reel by @arnitly (verified account) - TurboQuant cuts KV cache memory by 6x with no retraining required, and the benchmarks are hard to argue with. The technical detail the video skipped:
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@arnitly
TurboQuant cuts KV cache memory by 6x with no retraining required, and the benchmarks are hard to argue with. The technical detail the video skipped: PolarQuant works by converting vectors into polar coordinates after a random rotation. That rotation makes the distribution of angles highly concentrated and predictable, so the system can map everything onto a fixed circular grid. Because the boundaries are known in advance, quantization constants become unnecessary. No constants means no overhead. That is where the memory saving comes from. QJL handles the residual error PolarQuant leaves behind. It applies a Johnson-Lindenstrauss transform to that error and reduces each resulting number to a sign bit. The estimator this produces is provably unbiased, which is why attention scores stay statistically identical to full-precision despite the aggressive compression. On H100 GPUs, 4-bit TurboQuant achieved an 8x speedup computing attention logits versus the 32-bit baseline. On the Needle in a Haystack benchmark across Llama-3.1-8B and Mistral-7B, recall was perfect at 6x compression. Community ports to MLX for Apple Silicon and llama.cpp appeared within 24 hours of release. Papers and code are linked in the comments. Free to use commercially. #ai #artificialintelligence #technews #google #chatgpt
#Turboquant Reel by @cjtrowbridge - TurboQuant technique reduces AI RAM requirements 92% and increases AI speed 800%
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@cjtrowbridge
TurboQuant technique reduces AI RAM requirements 92% and increases AI speed 800%

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