Model: K2 Horizon 0.9B

MBZUAI IFM released K2 Horizon 0.9B, the smallest model in its K2 Horizon family, under Apache 2.0 with its training data; the model holds 1.08B parameters despite its 0.9B name. At Q4_K_M quantization the model needs about 1.0 GB of VRAM at 4K context and 2.6 GB at 32K, letting it run on a CPU, while its full 128K window requires about 8.4 GB because every layer keeps an ordinary KV cache. GGUF builds are published by bartowski, with Q4_K_M at 0.69 GB (689,934,432 bytes) and Q8_0 at 1.15 GB (1,148,623,968 bytes), and no per-level quality loss has been published.

K2 Horizon 0.9B 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 https://quantized.uk/quant-hub/k2-horizon-3-7b/ 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 https://quantized.uk/quant-hub/k2-horizon-mova-36b-a4b/ 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 https://quantized.uk/quant-hub/k2-horizon-32b/ 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 · +5.0 https://quantized.uk/gpu/mac-m1-8g/ Mac M3 8G8GB · +5.0 https://quantized.uk/gpu/mac-m3-8g/ RTX 40608GB · +7.0 https://quantized.uk/gpu/rtx-4060/ RTX 4060 Ti 8G8GB · +7.0 https://quantized.uk/gpu/rtx-4060-ti-8g/ Radeon RX 9060 XT 8G8GB · +7.0 https://quantized.uk/gpu/radeon-rx-9060-xt-8g/ - Ships in - GGUF https://quantized.uk/formats/gguf/ - Try it yourself - Size it in the calculator https://quantized.uk/tools/vram-calc/?model=k2-horizon-0-9b&quant=Q4 K M Generate the run command https://quantized.uk/tools/cli-gen/?model=k2-horizon-0-9b&quant=Q4 K M 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 https://quantized.uk/feed.xml