0.9B MBZUAI K2 Horizon
The smallest member of MBZUAI IFM's K2 Horizon family, Apache 2.0 and released with its training data. It is called 0.9B and holds 1.08B parameters. At Q4_K_M it needs about 1.0 GB at 4K and 2.6 GB at 32K, so it runs on almost anything, including a CPU. Every layer keeps an ordinary cache, which is why its full 128K window needs about 8.4 GB. IFM's own GGUF repo names these files 1B; the sizes here are bartowski's files, and no per-level quality loss has been published.
131K
Max Context
2
Quant Variants
GGUF Q8_0
Best Quality
—
Perplexity rise (best build)
Quantization Variants #
Per-quant VRAM, quality loss, and inference speed on RTX 4090
Measured = run on this site · Estimated = not run here: calculated, or a figure this site has not verified · Community = public reports
| Format | Level | BPW | VRAM | PPL Loss | Speed | Source | Actions |
|---|---|---|---|---|---|---|---|
| GGUF | Q4_K_M | 5.11 | 1.0 GB | — | — | Estimated | |
| GGUF | Q8_0 | 8.51 | 1.4 GB | — | — | Estimated |
Which file each size is for
A file size is not a memory figure: the VRAM column is the calculator's total at 4K context — weights plus KV cache plus a 10% activation buffer. Units differ on purpose: file sizes are decimal GB, as Hugging Face lists them; the VRAM column is GiB (1024³ bytes) with 2% added to the weights, so 7.12 GB of file counts as about 6.8 GiB there. A vision projector (mmproj), where the repo ships one, is a separate download and is in neither number.
- GGUF Q4_K_M[bartowski/K2-Horizon-0.9B-GGUF](https://huggingface.co/bartowski/K2-Horizon-0.9B-GGUF) · K2-Horizon-0.9B-Q4_K_M.gguf · 0.69 GB (689,934,432 bytes) · listing read 2026-10-23
- GGUF Q8_0[bartowski/K2-Horizon-0.9B-GGUF](https://huggingface.co/bartowski/K2-Horizon-0.9B-GGUF) · K2-Horizon-0.9B-Q8_0.gguf · 1.15 GB (1,148,623,968 bytes) · listing read 2026-10-23
Similar models #
[Compare with K2 Horizon](https://quantized.uk/tools/compare/?a=k2-horizon-0-9b&b=k2-horizon-7b)
[7BConsumer GPUMac / Apple Silicon6.5 GBmin VRAM—perplexity rise](https://quantized.uk/quant-hub/k2-horizon-7b/)
K2 Horizon 7B
MBZUAI K2 Horizon
The small dense member of MBZUAI IFM's K2 Horizon family, Apache 2.0, released with its training data. It is called 7B but holds 9.0B parameters: the 250K-token vocabulary's input and output tables add about 2B. Q4_K_M is about 6.5 GB at 4K, comfortable on an 8 GB card at that length; a 16 GB card holds it comfortably to 32K (10.8 GB), and 128K needs about 25.6 GB because every layer keeps an ordinary KV cache. llama.cpp runs it as k2-horizon. Sizes come from IFM's own GGUF files (bartowski's Q4_K_M is about 0.2 GB larger); no per-level quality loss has been published.
3.7BConsumer GPUMac / Apple Silicon4.0 GBmin VRAM—perplexity rise
K2 Horizon 3.7B
MBZUAI K2 Horizon
A small dense member of MBZUAI IFM's K2 Horizon family, Apache 2.0 and released with its training data. It is called 3.7B but holds 5.06B parameters: the 250K-token vocabulary's input and output tables add about 1.3B. At Q4_K_M it needs about 4.0 GB at 4K, comfortable on any card; at 32K it is about 8.3 GB, tight on an 8 GB card. Every layer keeps an ordinary cache, so 128K needs about 23.2 GB. IFM's own GGUF repo names these files 4B; the sizes here are bartowski's files, and no per-level quality loss has been published.
36B-A4BConsumer GPUPro GPU24.2 GBmin VRAM—perplexity rise
K2 Horizon MoVA 36B-A4B
MBZUAI K2 Horizon
The mixture-of-experts member of MBZUAI IFM's K2 Horizon family: 37.5B parameters with about 4B active per token, Apache 2.0, released with its training data. Every one of its 48 layers keeps an ordinary KV cache, so context is not cheap: Q4_K_M is about 24.2 GB at 4K, which is tight on a 24 GB card, and comfortable on a 32 GB card up to 16K. The 512K window would need about 129 GB. llama.cpp runs it as k2-horizon. Sizes come from IFM's own GGUF files (bartowski's Q4_K_M is about 0.3 GB larger); no per-level quality loss has been published.
32BConsumer GPUPro GPU23.1 GBmin VRAM—perplexity rise
K2 Horizon 32B
MBZUAI K2 Horizon
The dense 32B member of MBZUAI IFM's K2 Horizon family (34.8B counted, Apache 2.0, released with its training data). All 64 layers keep an ordinary KV cache with 8 KV heads: Q4_K_M is about 23.1 GB at 4K, tight on a 24 GB card and comfortable on a 32 GB card up to 16K; each extra 32K of context adds roughly 8.8 GB. llama.cpp runs it as k2-horizon. Sizes come from IFM's own GGUF files (bartowski's Q4_K_M is about 1 GB larger); no per-level quality loss has been published.
Running K2 Horizon 0.9B locally #
At Q4_K_M and 4K of context, K2 Horizon 0.9B needs about 1.0 GB — 0.7 GB of weights, 0.22 GB of KV cache and a 0.1 GB activation buffer. The smallest cards in this index that clear that comfortably are 8 GB ones — 11 of them, from the Mac M1 8G up — and 78 of the 78 cards here do. These are calculated figures, not measurements: the estimate stops counting a card as comfortable at 88% of its VRAM, which is roughly the room a desktop session needs.
What longer context costs #
Going from 4K to 32K adds about 1.53 GB, taking the total to 2.6 GB. Weights do not move with context — only the KV cache does, and it grows linearly, so this is the number to watch when planning for long documents. The model's native window is 128K; holding all of it at Q4_K_M would need about 8 GB.
Which build to download #
This index only tracks K2 Horizon 0.9B in GGUF, across 2 levels (Q4_K_M, Q8_0). No per-level perplexity sweep has been published for this model, so the quality column is empty rather than estimated — within one model, more bits per weight is the only ordering the data supports. No throughput has been measured for this model on this site's hardware. GGUF runs on llama.cpp and Ollama across NVIDIA, AMD and Apple silicon; AWQ and GPTQ target vLLM on CUDA and ROCm; EXL2 is ExLlamaV2 — CUDA only, and now archived, so local use rather than serving.
Common questions #
- How much VRAM does K2 Horizon 0.9B need?
- About 1.0 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 2.6 GB at 32K. That figure is weights plus KV cache plus a 10% activation buffer, calculated from the model's architecture rather than measured on a card.
- Will K2 Horizon 0.9B run on a 8GB GPU?
- Yes — at Q4_K_M and 4K context it needs about 1.0 GB, which leaves 5.0 GB spare on a Mac M1 8G, or on any of the other 8 GB cards here (11 in all). That is the smallest size in this index that clears it comfortably; 78 of 78 do.
- Which quantization of K2 Horizon 0.9B should I use?
- No published quality comparison exists for this model, so pick by footprint: Q4_K_M is the level most people run, and a higher bits-per-weight level is more faithful. All 2 levels in the index are Q4_K_M, Q8_0.
Where this model fits #
Sized at Q4_K_M with a 4K context window, smallest card first. Comfortable means the estimate uses at most 88% of the memory.
- Runs comfortably on
- Mac M1 8G8GB · +5.0Mac M3 8G8GB · +5.0RTX 40608GB · +7.0RTX 4060 Ti 8G8GB · +7.0Radeon RX 9060 XT 8G8GB · +7.0
- Ships in
- GGUF
- Try it yourself
- Size it in the calculatorGenerate the run command
How to actually run this #
Deployment guides for this model and this class of hardware.
This page's figures change when the model or the runtime does.Last updated 2026-10-23 RSS → /feed.xml