Daily Frontier Digest ·
A Linux kernel module doubles the RTX 4090's raw FP16 and BF16 tensor rate, but real workloads gain far less
Curated by Alfredo Borrás · Plymouth AI Lab
An open-source Linux module doubles raw FP16 and BF16 tensor rates on the RTX 4090, headless Claude Code appears to drain the 5-hour limit faster than the app, llama.cpp adds DeepSeek-V4.1-Flash, and Cerebras explains how it fits a 3T-parameter model on wafers.
The story today
A Linux kernel module doubles the RTX 4090's raw FP16 and BF16 tensor rate, but real workloads gain far less
gpu-unlock-ada, an open-source project published on GitHub on 23 October, is a Linux kernel module that switches on a hidden speed setting in the GPU before the normal NVIDIA driver loads. It builds on an earlier unlock for 30-series cards and on a payload published separately on Hugging Face, and it generates the needed firmware on the user's own machine rather than shipping it.
On an RTX 4090 the project measured FP16 and BF16 tensor throughput with FP32 accumulation going from about 165 to about 331 TFLOPS. A second owner repeated the test on their own card with the same driver and power cap and got 160 to 320 TFLOPS. INT8 and plain FP32 maths did not change.
Those are peak instruction rates. In that owner's run, ordinary cuBLAS matrix multiplications got 12 to 34% faster in FP16 and 16 to 20% faster in BF16 on two shapes, with no change on the largest one. The authors stress this was one matched pair of runs, not an application benchmark or a long stability test.
The RTX 4070 Ti SUPER and RTX 4060 Ti 16GB are supported only as experimental targets, each checked on a single card, and the 4090 D needs an extra opt-in flag. A single FP8 test on the 4070 Ti SUPER showed essentially no gain.
Why it matters: If you run local models on a 4090 under Linux, expect quicker prompt processing rather than faster token generation, and treat this as experimental low-level firmware work on hardware you own. Read the project's testing notes before trying it.
Highlights
Headless Claude Code seems to use up the 5-hour limit about 1.8 times faster than the interactive app
A bug report open since 25 September describes a controlled test on a Max plan: the same workload run through claude -p, which the Agent SDK uses, moved the 5-hour meter roughly 1.6 to 3 times further per dollar of usage than the interactive terminal, with identical token counts. The reporter's history on the same account shows the same pattern. Anthropic's help page says SDK use draws from the same limits and mentions no different weighting. Anthropic has not answered in the issue.
claude-codeanthropicSource: anthropics/claude-code issue on GitHub
llama.cpp now runs DeepSeek-V4.1-Flash without a fork
Build b11870, released on 25 October, adds the deepseek41 model architecture to mainline llama.cpp, including its Engram tables and compressed key-value cache. DeepSeek-V4.1-Flash is released under the MIT licence, and community GGUF conversions are already on Hugging Face. It is still a very large model, so plan for a lot of memory.
localopen-weightsSource: llama.cpp releases on GitHub
Cerebras explains how it spreads the 3T-parameter Kimi K3 across 59 wafer-scale chips
In a 22 October post, Cerebras works through an example with 4-bit weights: each of Kimi K3's 93 layers needs about 16 GB, and each wafer has 44 GB of on-chip memory. The 69 linear-attention layers go two per wafer, and each of the 24 dense layers gets its own wafer so the rest can hold its key-value cache, which makes 59 wafers. Only activations move between chips, about 2 microseconds per hop. The company aims at 1,000 to 2,000 or more tokens per second for models this size but published no measured speed, power or price for Kimi K3.
inferencehardwareSource: Cerebras blog
Claude Code 2.1.310 brings the Monitor tool to Bedrock, Vertex and Foundry
The 23 October release also makes Claude Code retry without the newer effort setting or mid-conversation system messages when a gateway rejects them. It stops background cleanup from deleting recent files such as pasted images when cleanupPeriodDays is very large, and closes a Windows gap where some hooks let a tool call through if Git Bash was missing.
claude-codeSource: Claude Code releases on GitHub
Claude users report reverse-engineering answers being cut off mid-response
Kotaku reports that since about 21 October, users on r/Claude have seen Opus 5.5 stop partway through reverse-engineering answers with a message that a safety check interrupted it. Anthropic has not announced a change, and some users in the thread say the classifier was always inconsistent. Treat it as user reports, not a confirmed policy change.
anthropicai-safetySource: Kotaku
Concept of the day
Why faster maths speeds up reading a prompt but not writing the answer
A language model does two kinds of work. When it reads your prompt, it processes all the tokens at once in large matrix multiplications, so the speed is set by how fast the chip can do arithmetic; that is compute-bound work. When it writes the answer, it produces one token at a time and has to stream the model's weights from memory for each one, so the speed is set by memory bandwidth; that is memory-bound work. An unlock that doubles tensor arithmetic, like the RTX 4090 module above, can help the first phase but leaves the second largely unchanged.
Models released
| Model | Maker | Size | Notes |
|---|---|---|---|
| DeepSeek-V4.1-Flash | DeepSeek | Large MoE | Now supported in mainline llama.cpp from build b11870. MIT licence. Community GGUF conversions are available on Hugging Face. |
Try this
llama.cpp Run DeepSeek-V4.1-Flash in stock llama.cpp
Update llama.cpp to build b11870 or later, download a DeepSeek-V4.1-Flash GGUF conversion from Hugging Face that fits your memory, and load it as you would any other model. No fork or custom branch is needed any more.
Source: llama.cpp releases on GitHub
Every item is checked against its primary source before it appears here. Figures we could not confirm are left out.
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