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IFM · MODEL RECORD

K2 Horizon MoVA 36B-A4B

K2 Horizon MoVA 36B-A4B has 37.445 billion total parameters. The selected Q4_K_M GGUF file is 22.368 GB.

Data snapshot: . 3 published tests in this catalog.

Model file and details

Provider
IFM
Total parameters
37,444,792,020
Exact file size
22,368,011,616 bytes
Quantization / precision
Q4_K_M
Weight repository
IFM/K2-Horizon-MoVA-36B-A4B-GGUF
Original model repository
IFM/K2-Horizon-MoVA-36B-A4B
Weight revision
d8d6aba962f7c7321974bcf45a02c1ab519eb479
Model revision
cca48b6631d03c338a0e1d8beb0c392af8becbd3
Runtime notes
Confirm model architecture and quantization support in your runner.
Recorded release date
2026-09-01
Date evidence
Source repository createdAt; source card and official local package re-reviewed after the 1 October 2026 source update.

K2-Horizon-MoVA-36B-A4B-Q4_K_M.gguf

GGUF file and download on Hugging Face · Original model card

Published benchmark results

These scores were compiled from the linked sources. These published scores were not independently tested here. Different prompts, precision, thinking budgets and tools can change results.

GPQA Diamond

80.8 / 100

Hard science questions. This is a science reasoning test, not an overall intelligence score.

Benchmark efficiency: 3.612 published score points per GB of selected weights. This ratio is not an inference-speed measure; check the test settings to see whether the score covers the selected quantization.

Test settings: Publisher-reported GPQA Diamond score for the BF16 source checkpoint in high-reasoning mode, temperature 1.0 and top_p 0.95.

The selected Q4_K_M package was not independently retested. Evaluation settings and reasoning budgets can differ across publishers. The official GGUF currently requires IFM's K2 Horizon llama.cpp fork while upstream support is pending.

Read the GPQA Diamond source

SciCode

38.9 / 100

Writing code to solve scientific problems. Published results can depend on the test scaffold.

Benchmark efficiency: 1.739 published score points per GB of selected weights. This ratio is not an inference-speed measure; check the test settings to see whether the score covers the selected quantization.

Test settings: Publisher-reported SciCode score for the BF16 source checkpoint in high-reasoning mode, temperature 1.0 and top_p 0.95.

The selected Q4_K_M package was not independently retested. Publisher evaluation, not an independent head-to-head comparison; harness details and generation budgets affect results.

Read the SciCode source

Terminal-Bench 2.1

58.6 / 100

Agentic terminal-use task success. Publisher-reported percentages are retained, but agent scaffold, tools, timeouts and reasoning budget can materially affect results.

Benchmark efficiency: 2.62 published score points per GB of selected weights. This ratio is not an inference-speed measure; check the test settings to see whether the score covers the selected quantization.

Test settings: Publisher-reported Terminal-Bench 2.1 score for the BF16 source checkpoint in high-reasoning mode, temperature 1.0 and top_p 0.95.

The selected Q4_K_M package was not independently retested. Agent scaffold, tools, timeouts and reasoning budget can materially affect Terminal-Bench results; compare cautiously.

Read the Terminal-Bench 2.1 source

Memory and comparison limits

Context and version evidence: The repository and file name identify the selected model. Revision hashes are shown above when recorded. Tested context length, full hardware configuration and run date are not consistently recorded in this snapshot. Only settings explicitly stated in each benchmark record are known.

The 22.368 GB figure covers model weights in this file. It excludes the context cache, runtime, operating system and any separate multimodal files. It is not a measured RAM requirement or a token-generation speed.

Use the model ranking chart to compare one test at a time. Use the 8, 16 and 32 GB memory guide. Check hardware memory and bandwidth before choosing a model.

Need a computer for K2 Horizon MoVA 36B-A4B?

These complete configurations pass the initial weight-size check for the selected 22.37 GB file. Allow extra memory for context and software. Check the model’s runtime notes and your inference software before buying.

CPU inference

GMKtec K15 · 32GB

Core Ultra 5 125U, 32GB DDR5-4800 (2 x 16GB), 512GB PCIe 4.0 SSD

$669.99Dated reference · not a live offer

32 GB system RAM · CPU planning

Low-power CPU and shared graphics limit heavy inference.

Check price on Amazon ↗Specifications & model limits
Listing and price evidence

Listing evidence: 2026-09-25. Amazon.com base price visible in an owner-supplied screenshot reviewed 25 Sep 2026; capture time not supplied. Dated reference, not a live quote. Excludes card signup offers, coupons, tax and shipping.

Apple unified memory

Apple Mac Studio M5 Max · 36GB

18-core CPU, 32-core GPU, 36GB unified memory, 512GB SSD

$2,499.00Dated reference · not a live offer

36 GB unified memory

Fixed 36GB memory and internal storage; no CUDA support; price is a manufacturer reference rather than a verified Amazon offer.

Check price on Amazon ↗Specifications & model limits
Listing and price evidence

Listing evidence: 2026-10-02. Apple US launch price for the base M5 Max Mac Studio, published 22 September 2026: USD 2,499. Dated manufacturer reference, not an Amazon offer; tax and shipping may differ.

AMD unified memory

GMKtec EVO-X2

Ryzen AI Max+ 395, 64GB LPDDR5X-8000, 1TB PCIe 4.0 SSD

$2,199.99Dated reference · not a live offer

64 GB unified memory

Memory is soldered and shared with the operating system.

Check price on Amazon ↗Specifications & model limits
Listing and price evidence

Listing evidence: 2026-09-24. GMKtec, EVO-X2 / 64GB / 1TB, recorded 22 Sep 2026; exact ASIN capacity identified 24 Sep. This is a dated reference, not a current Amazon price; tax, shipping and bundle contents may differ.

Memory matches are estimates, not tested compatibility or speed. As an Amazon Associate I earn from qualifying purchases.

Compare more computers for this model

RAM and VRAM planning for K2 MoVA 36B-A4B

Confirm model architecture and quantization support in your runner. The selected weights use 22.368 GB; this excludes context cache, runtime buffers and other programs.

System RAM (CPU inference)
32 GB planning tier · 22.4 GB initial weight budget
Dedicated VRAM
32 GB planning tier · 25.6 GB initial weight budget
Unified memory
32 GB planning tier · 22.4 GB initial weight budget

These are the first listed tiers that pass our reserve policy, not measured minimum requirements. System RAM and VRAM remain separate. Use the memory tool to change the reserve for your context and runner.

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