cloudsurf-software · Released Oct 31, 2026 · observed 31 Oct 2026
Public-source support evidence
We ran the Q4_K_M GGUF on one Mac; see Hardware notes below. The architecture is listed in llama.cpp, MLX / mlx-lm, and vLLM.
cloudsurf-software/CloudSurf-1B
CloudSurf-1B is a text generation model published by cloudsurf-software on Hugging Face. We have not written a summary yet; see the model card ↗.
View source model ↗SUPPORT EVIDENCE
The model config architecture is listed in this runtime registry. This is family/architecture evidence only; exact checkpoint loading, model-specific configuration, hardware, and generation were not tested.
Checked 2026-10-31 02:14 UTC
Evidence source ↗The exact model and runtime-specific sources were checked; no positive model-level evidence was established.
Checked 2026-10-31 02:14 UTC
Version v0.40.13
Checked source ↗The exact model and runtime-specific sources were checked; no positive model-level evidence was established.
Checked 2026-10-31 02:14 UTC
Checked source ↗The model config architecture is listed in this runtime registry. This is family/architecture evidence only; exact checkpoint loading, model-specific configuration, hardware, and generation were not tested.
Checked 2026-10-31 02:14 UTC
Stale evidence
Evidence source ↗The model config architecture is listed in this runtime registry. This is family/architecture evidence only; exact checkpoint loading, model-specific configuration, hardware, and generation were not tested.
Checked 2026-10-31 02:14 UTC
Evidence source ↗MLX community uploads may target mlx-vlm; an upload observation does not establish mlx-lm compatibility.
MEMORY ESTIMATES
The summary estimate of about 3 GB is the recommended Q4_K_M quant (551.3 MB observed), from file size × 1.2 + 1.5 GB. Each card is the largest observed quant whose estimate fits that capacity.
Which file should I grab?
F16 · 1.8 GB observed · about 4 GB ↗About 4 GB using file size × 1.2 + 1.5 GB. Fit is an estimate; compatibility, speed, context length and hardware have not been tested.
Which file should I grab?
F16 · 1.8 GB observed · about 4 GB ↗About 4 GB using file size × 1.2 + 1.5 GB. Fit is an estimate; compatibility, speed, context length and hardware have not been tested.
Which file should I grab?
F16 · 1.8 GB observed · about 4 GB ↗About 4 GB using file size × 1.2 + 1.5 GB. Fit is an estimate; compatibility, speed, context length and hardware have not been tested.
Which file should I grab?
F16 · 1.8 GB observed · about 4 GB ↗About 4 GB using file size × 1.2 + 1.5 GB. Fit is an estimate; compatibility, speed, context length and hardware have not been tested.
Which file should I grab?
F16 · 1.8 GB observed · about 4 GB ↗About 4 GB using file size × 1.2 + 1.5 GB. Fit is an estimate; compatibility, speed, context length and hardware have not been tested.
Which file should I grab?
F16 · 1.8 GB observed · about 4 GB ↗About 4 GB using file size × 1.2 + 1.5 GB. Fit is an estimate; compatibility, speed, context length and hardware have not been tested.
Which file should I grab?
F16 · 1.8 GB observed · about 4 GB ↗About 4 GB using file size × 1.2 + 1.5 GB. Fit is an estimate; compatibility, speed, context length and hardware have not been tested.
Which file should I grab?
F16 · 1.8 GB observed · about 4 GB ↗About 4 GB using file size × 1.2 + 1.5 GB. Fit is an estimate; compatibility, speed, context length and hardware have not been tested.
Memory estimates are the observed file size plus fixed headroom (20% plus 1.5 GB runtime allowance); real needs also depend on context length, KV cache and runtime settings. Fit is an estimate; it does not establish runtime compatibility, speed, context length, or hardware performance. Text-weights-only estimates do not include a projector or total vision memory where unknown.
Terms: a quant is a lower-precision version of the weights; GGUF is a file format used by llama.cpp and compatible apps; MLX targets Apple silicon; vLLM is a model-serving engine for NVIDIA GPUs; the KV cache is stored attention state that grows with context length. Terms explained ↗
OBSERVED FILES
Sizes and file groups come from the listed uploader repositories. A size is marked as an estimated fit only for a complete observed GGUF quant group.
| Uploader | Format & quant | Observed size | Estimated fit | Files |
|---|---|---|---|---|
| cloudsurf-softwarecloudsurf-software/CloudSurf-1B-GGUF ↗Observed 31 Oct 2026 | GGUF · Q4_K_M | 551.3 MB | 16 GB, 24 GB, 32 GB, 64 GB, 128 GB, 192 GB, 256 GB, 512 GBEstimate only; requires the complete GGUF quant group. | 1 file
|
| cloudsurf-softwarecloudsurf-software/CloudSurf-1B-GGUF ↗Observed 31 Oct 2026 | GGUF · Q8_0 | 946.2 MB | 16 GB, 24 GB, 32 GB, 64 GB, 128 GB, 192 GB, 256 GB, 512 GBEstimate only; requires the complete GGUF quant group. | 1 file
|
| cloudsurf-softwarecloudsurf-software/CloudSurf-1B-GGUF ↗Observed 31 Oct 2026 | GGUF · F16 | 1.8 GB | 16 GB, 24 GB, 32 GB, 64 GB, 128 GB, 192 GB, 256 GB, 512 GBEstimate only; requires the complete GGUF quant group. | 1 file
|
CURATED TRACKING
No curated issues verified for this snapshot. An empty list does not mean there are no bugs.
HARDWARE NOTES
We ran the Q4_K_M GGUF (0.55 GB, cloudsurf-software/CloudSurf-1B-GGUF @1ff15bc) on an Apple M4 Max Mac, 128 GiB unified memory, macOS 27.0.1, Metal with llama.cpp b11912 (upstream, 961c376) on 30 Oct 2026.
Decode: 384.2 tok/s on a short prompt, 343.9 tok/s on a long prompt (medians of 3 runs).
Peak memory: 1.31 GiB (llama-server footprint).
llama-bench: pp512 6,800.5 tok/s, tg128 414.8 tok/s.
Answer checks: 1 of 4 prompts passed (quick pass/fail sanity checks, not a quality benchmark).
We ran CloudSurf's official Q4_K_M GGUF of this model.
This is a base model: it continues text and does not follow instructions. We ran it as raw text completion (no chat template) at 2K context, its trained context length, with repeat penalty 1.1.
It ran to the 512-token output cap on every prompt, so the answer checks are informational only; decode speed counts every generated token.
One machine, one run set: temperature 0, 2K context, 1 warm-up plus 3 runs per prompt. Short prompt is 12 tokens; long prompt is 1,496 tokens (each model counts with its own tokenizer). Weights were loaded from an external USB drive with mmap off; load time is not published for this run. Your speed will differ with hardware, context length and settings.
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