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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Yes—with an important qualification: VIDRAFT’s POCKET-Darwin-180B-GGUF can run on some gaming laptops by memory-mapping its roughly 111.3 GB checkpoint from an NVMe SSD, rather than loading the whole model into RAM or VRAM. The publisher reports 4.17 generated tokens per second on one laptop with an RTX 5060 Laptop GPU, 8 GB VRAM and 32 GB RAM. That is a specific, self-reported result, not a speed guarantee for other laptops.
What the 180B label means—and what must fit
POCKET-Darwin-180B-GGUF is a four-file, 4-bit GGUF version of Darwin-180B-RSI-R3, intended for llama.cpp. The model card lists 180 billion total parameters, 512 routed experts and about 10 experts—roughly 3 billion parameters—active for each generated token. Sparse activation reduces the parameters involved in each token’s computation; it does not shrink the checkpoint to 3 billion parameters. The four files total 111.3 GB, compared with 360 GB for the BF16 original, according to FINAL-Bench’s model card.
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That distinction explains how the model can be usable on a machine with less than 111 GB of RAM. The runtime memory-maps the model files and reads the needed experts from storage. The model does not all need to be resident in GPU memory, but SSD access becomes part of inference. Storage performance, access patterns and how much of the checkpoint can remain resident affect latency; the active-parameter count alone does not predict speed. Hugging Face Blog Editorial describes SSD streaming as part of the inference pipeline.
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The model publisher’s entry-level laptop or desktop configuration is 32 GB of system RAM, at least 8 GB of GPU VRAM, an NVMe SSD and 120 GB of free disk space. The free-space figure exceeds the listed 111.3 GB checkpoint size. More memory can keep more of the model resident and improve speed, but the card does not promise a particular performance level for every system meeting these minimums.
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The model card says the qwen4exp architecture requires llama.cpp b11048 or newer. Check the current model card and llama.cpp support before setup: supported flags, build compatibility and file paths can change.
Example server command
The card provides this example laptop or desktop command. Replace the model path with the location of the downloaded shard:
llama-server -m POCKET-Darwin-180B-UD-Q4_K_XL-00001-of-00004.gguf -ngl 999 --cpu-moe -fa on -c 8192 --jinjaThe command uses the first shard as the model path; keep all four model files together as required by the model format. The card also provides a CPU-only mode using -ngl 0, 16 threads, context 8192 and --load-mode none. Its example flags are not a guarantee that every GPU, llama.cpp build or laptop configuration will use the same settings successfully.
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How fast the published laptop result was
FINAL-Bench reports 4.17 generated tokens per second on an RTX 5060 Laptop GPU with 8 GB VRAM, 32 GB RAM and an NVMe SSD, using llama.cpp with the model memory-mapped from SSD. This is the publisher’s measurement, checked in 2026—not an independently replicated typical result. Your laptop’s storage, memory residency, context, prompt, offload settings, power limits and thermals can change the experience.
The same publisher reports 18.4–21.0 tokens per second and 78.8 GB peak RAM for a CPU-only run on a 16-thread AMD EPYC 9365 server with the model resident in memory. That server result is not a like-for-like laptop comparison: it uses different hardware and keeps the model in memory rather than relying on the laptop’s SSD-streaming setup.
How to decide whether the speed is usable for you
A tokens-per-second figure by itself does not tell you whether the model will suit your work. Measure the parts of the interaction that matter: time to first token, generation rate, output quality and the longest response time you can accept. Short and long prompts can behave differently, and context length and prompt processing can change both memory use and latency.
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- Test your actual workload. Use representative prompts and response lengths rather than relying on a single short sample.
- Record the setup. Note RAM, VRAM, SSD type, llama.cpp build, context size and offload settings so you can reproduce the result.
- Compare alternatives on the same machine. Test a smaller dense or MoE model as well. A model that fits fully in memory may respond faster or support more simultaneous requests.
- Check operational limits. Consider portability, storage traffic, concurrency, power use, thermals and total system cost alongside speed and output quality.
FINAL-Bench also reports 87.65% before and after quantization on 2,000 paired MMLU-Pro questions, with a 0.00-point difference and a 95% confidence interval of [−0.95, +1.00]. This is the publisher’s test, not independent replication; it does not establish identical quality on other tasks.
When another device or a smaller model makes more sense
If the workload depends on low latency, long sessions or multiple concurrent requests, compare the laptop’s SSD-streamed setup with a configuration that holds more of the checkpoint in memory. FINAL-Bench lists a 128 GB RAM mini PC as a configuration able to hold the full model in memory; that is an alternative device class, not a guarantee for every mini PC. A smaller model may also better fit a machine’s memory and response-time needs. Decide by testing the same prompts and measuring the same outcomes on each option.
Hugging Face Blog Editorial’s October 4, 2026 article puts the practical caveat plainly: “Sparse activation and SSD streaming can make a 111 GB model usable on a laptop, but workload-level testing is essential before treating technical compatibility as practical deployment readiness.”
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