IFM / k2_horizon

K2-Horizon-0.9B

Name: K2-Horizon-0.9B architecture and memory requirements
Creator: LLM Bottleneck
License: https://creativecommons.org/licenses/by/4.0/

1.08 billion parameters.

Open in the calculator →

Architecturepublished data

Quick answer

K2-Horizon-0.9B needs about 1.94 GB at Q4_K_M — the format most people download — with 8,192 tokens of context. It fits entirely on 18 of 18 common devices, starting with the NVIDIA GeForce RTX 5070 (12 GB). What else fits in 12 GB. No hardware for it? Rent a GPU that runs it, priced live.

Smallest device that fitsNVIDIA GeForce RTX 507012 GB · 672 GB/s~424 tok/s Faster than you readOpen in the calculator →
Fastest in this listNVIDIA GeForce RTX 509032 GB · 1792 GB/s~1129 tok/s Faster than you readOpen in the calculator →

Sized at 8,192 tokens of context with the whole model in device memory, across 18 common devices. Speeds are estimates, not benchmarks.

Find the NVIDIA GeForce RTX 5070, the smallest card listed here that holds it: Amazon ↗ · eBay (new and used) ↗Store links may pay us a commission. They never decide which card is suggested — the memory arithmetic does.

The same dense-inside-a-routed-config shape as K2-Horizon-7B: num_experts 0, zero expert widths, and mlp_only_layers covering all 28 layers. Licence declared on the repository: apache-2.0.

Weight-file size by format

every format, with its evidence

Format	Size	Range	Basis
FP16	2.16 GB	1.96 GB – 2.17 GB	reconstructed size
Q8_0	1.15 GB	1.04 GB – 1.15 GB	reconstructed size
Q6_K	0.89 GB	0.80 GB – 0.89 GB	reconstructed size
Q5_K_M	0.77 GB	0.69 GB – 0.79 GB	reconstructed size
Q5_0	0.76 GB	0.67 GB – 0.77 GB	reconstructed size
Q4_K_M	0.67 GB	0.58 GB – 0.69 GB	reconstructed size
Q4_0	0.64 GB	0.55 GB – 0.66 GB	reconstructed size
Q3_K_M	0.55 GB	0.47 GB – 0.59 GB	reconstructed size
Q2_K	0.43 GB	0.35 GB – 0.48 GB	reconstructed size

A file size is not the memory a run needs: the KV cache and the runtime reserve come on top, and the calculator adds both.

What the labels mean

published data: Read from a published source — a file's byte count, a model's configuration or a manufacturer's specification — or exact arithmetic on such values. Not a measurement on a machine.
reconstructed size: Weight size reconstructed from the pinned architecture, because no published file exists.
size range: Only a lower and an upper bound are claimed for this weight size.

Hardware ladder

Q4_K_M at 8,192 tokens · 18 of 18 devices hold it entirely

Device	Memory	Verdict	Decode	Calculator
NVIDIA GeForce RTX 5070	1.94 GB of 12 GB	fits	~424 tok/s	Open →
AMD Radeon™ RX 9070 XT	1.94 GB of 16 GB	fits	~462 tok/s	Open →
NVIDIA GeForce RTX 4080	1.94 GB of 16 GB	fits	~452 tok/s	Open →
NVIDIA GeForce RTX 5080	1.94 GB of 16 GB	fits	~605 tok/s	Open →
NVIDIA GeForce RTX 5070 Ti	1.94 GB of 16 GB	fits	~565 tok/s	Open →
NVIDIA GeForce RTX 5060 Ti 16GB	1.94 GB of 16 GB	fits	~282 tok/s	Open →
NVIDIA GeForce RTX 4090	1.94 GB of 24 GB	fits	~635 tok/s	Open →
AMD Radeon™ RX 7900 XTX	1.94 GB of 24 GB	fits	~693 tok/s	Open →
NVIDIA GeForce RTX 3090	1.94 GB of 24 GB	fits	~590 tok/s	Open →
NVIDIA GeForce RTX 5090	1.94 GB of 32 GB	fits	~1129 tok/s	Open →
NVIDIA RTX 6000 Ada Generation	1.94 GB of 48 GB	fits	~605 tok/s	Open →
Apple M4 Pro	1.94 GB of 64 GB	fits	~101 tok/s	Open →
Apple M5 Pro	1.94 GB of 64 GB	fits	~106 tok/s	Open →
AMD Ryzen AI Max+ 395 with Radeon 8060S	1.94 GB of 128 GB	fits	~185 tok/s	Open →
Apple M3 Max	1.94 GB of 128 GB	fits	~117 tok/s	Open →
Apple M4 Max	1.94 GB of 128 GB	fits	~128 tok/s	Open →
NVIDIA DGX Spark	1.94 GB of 128 GB	fits	~172 tok/s	Open →
Apple M2 Ultra	1.94 GB of 192 GB	fits	~140 tok/s	Open →

Decode is a calibrated estimate from the published calibration; capacity is the manufacturer's published ceiling, not guaranteed free memory. Fitting in memory is not the same as loading: whether the runtime and version you have supports this architecture and format on that machine has not been tested here. Offloaded rows assume there is enough system RAM for the overflow — the calculator checks that against the RAM you declare. Each “Open” link keeps this model, format and context.

Run it in the cloud

Rent a GPU that runs K2-Horizon-0.9B

Every rentable machine that holds the whole model, from Vast.ai and RunPod, with this site's speed estimate for each and the provider's own price, read live. Cheapest first; sort by cost per token or speed instead, or switch the format.

Prices · 18:30 UTC, 22 Sept

Machine	Speed	Per hour	Per M tokens	Where	Rent
1× RTX 306012 GBrecommendedcheapestbest value	~226 tok/sFaster than you read	$0.036	$0.044	Vast.aiMarketplace · 99.8% reliable	Rent on Vast.ai
1× RTX 308010 GB	~479 tok/sFaster than you read	$0.082	$0.048	Vast.aiMarketplace · 98.3% reliable	Rent on Vast.ai
1× RTX 30708 GB	~282 tok/sFaster than you read	$0.082	$0.081	Vast.aiMarketplace · 99.1% reliableRunPod $0.13/h	Rent on Vast.ai
1× RTX 4060 Ti 16GB16 GB	~181 tok/sFaster than you read	$0.090	$0.14	Vast.aiMarketplace · 99.4% reliable	Rent on Vast.ai
1× RTX 407012 GB	~317 tok/sFaster than you read	$0.096	$0.084	Vast.aiMarketplace · 99.8% reliable	Rent on Vast.ai
1× RTX 309024 GB	~589 tok/sFaster than you read	$0.12	$0.058	Vast.aiMarketplace · 99.5% reliableRunPod $0.50/h	Rent on Vast.ai
1× RTX 5060 Ti 16GB16 GB	~282 tok/sFaster than you read	$0.12	$0.12	Vast.aiMarketplace · 99.1% reliable	Rent on Vast.ai
1× RTX 3080 Ti12 GB	~574 tok/sFaster than you read	$0.14	$0.066	Vast.aiMarketplace · 99.9% reliable	Rent on Vast.ai

First time renting a GPU? How it works, in four steps

Create an account and add credit. Both providers are prepaid: $10 is enough to try any card on this page for hours. A new RunPod account that arrives through a referral link gets a one-time $5 credit when it first adds $10.
Pick the card and the count shown here, and a template: llama.cpp or vLLM for an OpenAI-compatible API, or one with a web chat if you only want to talk to the model. “How to launch it” below gives the exact image and command.
Wait for the download. The weights are fetched on the machine; tens of gigabytes take a few minutes on a data-centre connection. You pay by the second from the moment it starts.
Stop it when you are done. A running machine bills even when idle. A stopped one costs nothing per hour, though its disk is usually still billed until you delete it.

How to launch it

No Q4_K_M file of K2-Horizon-0.9B is catalogued here, so there is no exact command to vouch for. Start the machine from the llama.cpp server image ghcr.io/ggml-org/llama.cpp:server-cuda and point -hf at a Q4_K_M build from the publisher or a quantizer you trust — search Hugging Face for one. Check its file size against the memory figure on this page before you rent.

Every machine holds the whole model at Q4_K_M and 8,192 tokens of context, no offload. Speeds are this site’s single-stream decode estimates; prices are what each provider’s own API quoted, on-demand, for the whole machine. Vast hosts below 98% measured reliability are left out.

Referral links Vast.ai, RunPod and Novita pay us a share of what you spend if you sign up through these buttons. It costs you nothing, and it never decides an order or a recommendation: both are computed from the live price and the speed, and options that pay us nothing are listed and recommended on the same terms. How we rank

Buying a card instead, or paying by the token? Compare a year of K2-Horizon-0.9B three ways →

Under the hood

Architecture, read from the publisher’s file

The numbers every figure above is computed from, with the file they came from.

Architecture

architecture.json · pinned 9fa6faa55fe1

✓ Architecture read from the published config.json

Retrieved 2026-09-20 at pinned commit 9fa6faa55fe1; parameter count from the safetensors index at the same revision.

Architecture: k2_horizon
Layers: 28
Hidden size: 1,536
Attention heads: 32
KV heads: 8
Head dimension: 64
Feed-forward width: 5,120
Vocabulary: 64,256
Context ceiling: 131,072
RoPE theta: 1,000,000
Experts: 0
Experts / token: 0
Shared experts: 0

Where the memory goes

Grouped-query attention shares each key/value head across 4 query heads, so the KV cache is 25% of what multi-head attention would need at the same context.

Open the exact official config.json ↗

Derivatives this page also answers for

Each of these repositories declares K2-Horizon-0.9B as its base, and its published configuration matches this one on every field that decides memory. Its page lists what was compared and any files it publishes. Paste any other repository.

IFM/K2-Type-0.9B

Use this answer in your own product

As JSON, from the API

curl -s https://llmbottleneck.com/v1/analyze \
  -H "Authorization: Bearer $LLMB_KEY" -H "content-type: application/json" \
  -d '{"model":"ifm-k2-horizon-0-9b","quantization":"Q4_K_M","context":8192,"hardware":"nvidia-geforce-rtx-4090"}'

Same engine, same evidence, every field sourced. Free key in one step →

On your page, as a widget

<script src="https://llmbottleneck.com/widget.js"
  data-model="ifm-k2-horizon-0-9b" data-quantization="Q4_K_M"
  data-hardware="nvidia-geforce-rtx-4090" data-context="8192"></script>

No key needed. Unbranded, with your own buy button, on Pro and Business →

Citing this page

LLM Bottleneck. “K2-Horizon-0.9B VRAM and hardware requirements.” Architecture from IFM/K2-Horizon-0.9B at revision 9fa6faa55fe1, retrieved 2026-09-20. https://llmbottleneck.com/models/ifm-k2-horizon-0-9b

Every figure above is either the published value or a reconstruction whose measured error is on the accuracy page.