Maintainer of this skill?
Automated security scan — not a certification.
by carloslfu
Run a 105 GB AI model on a Mac that can't hold it. Qwen3.8-Flash-Next 4-bit, streamed from SSD on 16–64 GB Apple Silicon Macs. Fully native Swift, MLX and Metal. Offline, no Python. CLI, Swift library, and Ollama/OpenAI/Anthropic APIs. Works with Claude Code and Codex.
# Add to your Claude Code skills
git clone https://github.com/carloslfu/slotstreamGuides for using cli tools skills like slotstream.
Last scanned: 9/25/2026
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"file": "README.md",
"line": 156,
"type": "remote-install",
"message": "Install command (remote install script piped to a shell — review the source before running): \"curl -fsSL https://raw.githubusercontent.com/carloslfu/slotstream/main/install.s\"",
"severity": "low"
}
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"status": "PASSED",
"scannedAt": "2026-09-25T09:25:36.665Z",
"npmAuditRan": true,
"pipAuditRan": true,
"promptInjectionRan": true
}See how slotstream compares with popular alternatives.
slotstream is an open-source cli tools skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by carloslfu. Run a 105 GB AI model on a Mac that can't hold it. Qwen3.8-Flash-Next 4-bit, streamed from SSD on 16–64 GB Apple Silicon Macs. Fully native Swift, MLX and Metal. Offline, no Python. CLI, Swift library, and Ollama/OpenAI/Anthropic APIs. Works with Claude Code and Codex. It has 658 GitHub stars.
Yes. slotstream passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/carloslfu/slotstream" and add it to your Claude Code skills directory (see the Installation section above).
slotstream is primarily written in Swift. It is open-source under carloslfu on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other CLI Tools skills you can browse and compare side by side. Open the CLI Tools category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh slotstream against similar tools.
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⚠️ Third-Party Software Notice
This skill is third-party open-source software developed and hosted independently on GitHub. SkillsLLM is an informational directory and does not control or maintain the underlying repository.
Any security checks, ratings, or warnings displayed by SkillsLLM are automated and limited in scope. They do not constitute a security certification or guarantee that the software is safe, error-free, or free from malicious code, vulnerabilities, compromised dependencies, or prompt-injection risks.
Review the source code, permissions, dependencies, and configuration before installing or running any third-party skill. Use is at your own risk. To the maximum extent permitted by applicable law, SkillsLLM is not liable for losses arising from third-party software.
Automated security scan — not a certification.
Run a 105 GB AI model on a Mac that can't hold it.
Slotstream is a fully native local AI engine for Apple Silicon Macs, written in Swift on Apple's MLX and Metal. It streams model experts from SSD and keeps the ones it needs in a memory cache, so the model can be much larger than your Mac's RAM. Inference runs on your Mac, offline after the download, with no Python runtime or cloud account.
The released engine runs Qwen3.8-Flash-Next, a 125-billion-parameter open model, on Macs with 16 to 64 GB of memory. Our 48 GB M5 Pro measured 20.91–23.69 tok/s across three workloads in development tests at a 33 GB memory budget, using the original 4-bit model (results and test conditions).
Get started · Model · Speed · Get help
The latest release, v0.2.30, ships the engine and command-line tool. Its default is the original Qwen3.8-Flash-Next 4-bit model. The installer below downloads this release.
Chat with it, ask it about pictures, or code with it: slotstream launch claude
starts Claude Code on the local model, and Codex, Pi, opencode and Hermes work
the same way. Developers can connect their own apps through its Ollama-,
OpenAI- and Anthropic-compatible APIs or its Swift library.
Slotstream focuses on Qwen3.8-Flash-Next 4-bit. The work goes into making this capable model useful on a Mac that cannot hold all its weights in RAM: SSD streaming, memory use, context and tool use.
We maintain one model at a time. A future successor needs evidence from complete tasks on the hardware we support. The model guide records the current checkpoint and defaults.
The Fast and Q2 models and the native Mac app are archived. Their source, licenses, tests and measured results remain available. Current work focuses on the engine, CLI, serving APIs and Swift library.
Slotstream focuses on Macs with 16 to 64 GB of memory, where running the larger model depends on managing what stays in RAM and what comes from SSD. The CLI and APIs connect the model to local chat apps and coding agents. Developers can also embed the engine through its Swift library.
The original model also runs on 96 GB and larger Macs, but those are outside the optimization target: engines that keep it fully resident report faster replies there. See related projects if that is your Mac.
For the released engine and its default model, you need an Apple Silicon Mac with at least 16 GB of memory, macOS 14 or later, and about 110 GB of free SSD space. Open About This Mac from the Apple menu to check your chip and memory. On an 8 GB Mac even the smallest memory plan doesn't fit, so Slotstream refuses to start instead of swapping. Windows, Linux and Intel Macs are not supported. The hardware guide has the tested macOS versions.
These measurements and planning ranges describe the original Qwen3.8-Flash-Next 4-bit model. Every published result links to its method; unsuccessful experiments stay in the measurements.
tok/s means tokens per second; a token is a small piece of text, often part
of a word. These speeds describe reply generation; loading the model and
processing the prompt take additional time.
On October 5, 2026, our 48 GB M5 Pro measured 20.91–23.69 tok/s with the original 4-bit model at a 33 GB memory budget. These historical development tests used the now-archived Mac app's startup settings and automatic memory ceiling:
| Workload | Median generation speed |
|---|---|
| Short prompt | 20.91 tok/s |
| Longer context | 23.69 tok/s |
| Completed coding answer | 21.59 tok/s |
Each workload ran three times without swapping during the measured runs. The first two used fixed-length replies; the coding task ran to completion. These are short controlled tests with an uncontrolled filesystem cache, not a sustained-session guarantee or a measurement of other Macs. The hardware guide gives the build, configuration and recorded evidence.
The larger budget keeps more experts in RAM, reducing weight reads during generation. No new quantization was needed for these results. They do not establish a speedup over an older release at identical settings.
The earlier 0.2.19 release benchmark measured 15.86 tok/s at a 22 GB memory target, on eight prompts the engine was never tuned on. The engine predicts which experts the next layers will need and reads them from the SSD before they are asked for, which changes speed and never the output. The expert lookahead guide has the measurements behind each release. This historical test used smaller prompt passes and disabled prefix caching, leaving more memory for experts. It is not a measurement of today's automatic configuration. The 0.2.23 calibration attempt did not qualify a replacement release benchmark; the newer development tests above use a different budget, workload set and method.
Rough planning ranges for warm replies, from community reports and our own measurements, rounded outward. Faster chips and SSDs sit at the top of each range; other apps and memory pressure pull results down.
| Installed RAM | Estimated warm reply speed | Example automatic context window |
|---|---|---|
| 8 GB | Unsupported by the current memory plan | Not available |
| 16–<24 GB | ~1–6 tok/s | 32,768 tokens |
| 24–<48 GB | ~5–16 tok/s | 32,768 through 32 GB; 65,536 at 36 GB |
| 48–<96 GB | ~15–27 tok/s | 32,768 at 48 GB; 131,072 at 64 GB |
| 96 GB+, the model fits in memory | ~20–32 tok/s | 262,144 tokens, the model's full window |
Context examples use decimal-GB memory simulations. A Mac's marketed capacity,
Metal limits and available memory can produce a different plan; slotstream doctor shows the actual choice.
The middle rows are anchored on our M5 Pro's measurement; the top ends of the last two rows come from a 128 GB M5 Max with a larger, manually chosen memory target, and the last row is outside Slotstream's target range. These are estimates, not limits. The hardware guide has the basis of each range, every result measured on real Macs with credits and test conditions, and every automatic memory plan.
The changes shipped in 0.2.23 shorten prompt processing and repeated-history work. These measurements use the same 48 GB M5 Pro at a 10 GB target, with three clean pairs per comparison:
| Workload | Matched control | Median times, control → enabled | Median paired time reduction |
|---|---|---|---|
| 16K inventory prompt, MTP on | Larger-read workspace policy off | 155.22 s → 53.94 s prefill | 65.40% |
| 2K prose follow-up, MTP off | Prefix checkpoints disabled | 30.73 s → 4.42 s request | 85.62% |
Both comparisons switch a feature off in the same tested binary. They measure prompt processing or a cached follow-up, not an increase in reply tok/s or a whole-release speedup. Times are arm medians; reductions are medians of paired changes. The hardware guide explains the fixtures and the latest audit's exclusions.
A fresh installed-release study used ordinary caching at the same memory target. These are observed first-read ranges and median exact-repeat request times across the prescribed request order, with capped replies:
| Prompt | Eligible first reads / repeats | First-read prefill range | Median repeated request |
|---|---|---|---|
| 2K code | 4 / 3 | 13.86–28.11 s | 2.79 s |
| 2K prose | 3 / 3 | 14.91–26.51 s | 3.22 s |
The desktop load screen passed for the included observations, but several runs had system swap-ins. Request history changed read batching, so these results do not replace the general speed estimates or decode headline. See the measurement and its limits.
For the original model, Auto mode picks the memory target, cache size,
speculative decoding and context window for your Mac. It takes the largest window in the table above
that still leaves room for speculative decoding and a complete conversation,
given the memory free at startup, without an unmeasured loss of useful
expert cache. slotstream doctor shows the choice and
why, and --max-context 65536 sets a window yourself, up to 262,144 tokens.
Starting a reply takes time. Slotstream first reads your question and the
conversation history, which can take minutes for a long prompt. Follow-up
turns reuse unchanged history, a new conversation reuses the system prompt
earlier ones started with, and serve --prefix-cache-dir keeps long
conversations and shared system prompts on disk so that they survive a
restart. The hardware guide
has the prompt-reading estimates
for each memory size.
To install the released engine, open Terminal and paste this c
