K2 Horizon 32B — Hardware Requirements & GPU Compatibility
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K2 Horizon 32B is a 34.8B-parameter open language model from IFM. It supports a context window of up to 524,288 tokens. At BF16 it needs about 70.19 GB of VRAM — see which GPUs and Macs can run it below.
3.4K downloads
37 likesSpecifications
- Publisher
- IFM
- Parameters
- 34.8B
- Architecture
- K2HorizonForCausalLM
- Context Length
- 524,288 tokens
- Vocabulary Size
- 250,624
- Release Date
- 2026-09-01
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does K2 Horizon 32B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 70.2 GB | 155.8 GB | 69.56 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run K2 Horizon 32B?
BF16 · 70.2 GBK2 Horizon 32B (BF16) requires 70.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 92+ GB is recommended. Using the full 524K context window can add up to 85.6 GB, bringing total usage to 155.8 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run K2 Horizon 32B?
BF16 · 70.2 GB19 devices with unified memory can run K2 Horizon 32B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio (M3 Ultra, 96GB).
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightFrequently Asked Questions
- How much VRAM does K2 Horizon 32B need?
K2 Horizon 32B requires 70.2 GB of VRAM at BF16. Full 524K context adds up to 85.6 GB (155.8 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 34.8B × 16 bits ÷ 8 = 69.6 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 86.2 GB (at full 524K context)
VRAM usage by quantization
BF1670.2 GBBF16 + full context155.8 GB- Can NVIDIA GeForce RTX 5090 run K2 Horizon 32B?
No — K2 Horizon 32B requires at least 70.2 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run K2 Horizon 32B on a Mac?
K2 Horizon 32B requires at least 70.2 GB at BF16, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run K2 Horizon 32B locally?
Yes — K2 Horizon 32B can run locally on consumer hardware. At BF16 quantization it needs 70.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is K2 Horizon 32B?
At BF16, K2 Horizon 32B can reach ~68 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 70.2 × 0.65 = ~74 tok/s
Estimated speed at BF16 (70.2 GB)
~74 tok/s~74 tok/s~68 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of K2 Horizon 32B?
At BF16, the download is about 69.56 GB.
- Which GPUs can run K2 Horizon 32B?
No single consumer GPU has enough VRAM to run K2 Horizon 32B at BF16 (70.2 GB). Multi-GPU or professional hardware is required.
- Which devices can run K2 Horizon 32B?
19 devices with unified memory can run K2 Horizon 32B at BF16 (70.2 GB), including ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.