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DedeProGamesLlamaForCausalLM

Kiyo 230M Preview — Hardware Requirements & GPU Compatibility

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Kiyo 230M Preview is a 230M-parameter open language model from DedeProGames. It supports a context window of up to 2,048 tokens. At Q4_K_M it needs about 0.50 GB of VRAM — see which GPUs and Macs can run it below.

230M params · dense2K contextApache 2.0166 downloads3 likes
Based on SmolLM2 135M
S

Runs great on a 24 GB GPU

Runs from8 GB Q4_K_M · 2KBest on24 GB Q4_K_M · ~1310 tok/s · 2KMacs from8 GB Q4_K_M · ~143 tok/s

Apache 2.0

Reference GPU · RTX 4090 24 GB

Estimates, ±20% · how we estimate

Run Kiyo 230M Preview

One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.

Recommended

vLLM
one command · needs vLLM installed
$vllm serve DedeProGames/Kiyo-230M-Preview --max-model-len 2048

vLLM repo with its own quantisation

Download: Weights on Hugging Face

install, OS, other quants

Ways to runOther ways

each opens step-by-step

Run in cloud

Runs on RTX 3080 Ti (12 GB) (11 GB headroom) · Q4_K_M

Generation speed
~1186 tok/s
generation speed
Cost per 1M output tokens
$0.029
per 1M output tokens

How Much VRAM Does Kiyo 230M Preview Need?

Select a quantization level; the listed hardware adheres to it.

QuantizationVRAM+32KOn RTX 4090 (reference)
0.5 GB—2-bit · K-quant — 2-bit quantization with K-quant improvementsFits: 2K context · ~1424 tok/s
0.5 GB—3-bit medium — 3-bit medium quantizationFits: 2K context · ~1365 tok/s
0.5 GB—4-bit medium — 4-bit medium quantization — most popular sweet spotFits: 2K context · ~1310 tok/s
0.5 GB—5-bit medium — 5-bit medium quantization — good quality/size tradeoffFits: 2K context · ~1236 tok/s
0.6 GB—6-bit, very good — 6-bit quantization, very good qualityFits: 2K context · ~1170 tok/s
0.6 GB—8-bit, near-lossless — 8-bit quantization, near-losslessFits: 2K context · ~1092 tok/s
0.8 GB—16-bit · bfloat — Brain floating point 16 — preferred for trainingFits: 2K context · ~789 tok/s
est.calculated, no published file yet

Selected Q4_K_M

4-bit medium quantization

0.5GBat 2K context

Weights
0.1 GB
KV cache
0.1 GB
Overhead
0.3 GB

Fits RTX 4090 (reference) · 23.5 GB free · ~1310 tok/s

≥50 tok/s needs ≥38 GB/s · RTX 5090 ~2330, RTX 3090 Ti ~1310

Which Hardware Can Run Kiyo 230M Preview?

Q4_K_M · 0.5 GB

Kiyo 230M Preview (Q4_K_M) requires 0.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 1+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Kiyo 230M Preview, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).

By VRAM tier · typical GPUs and Macs at Q4_K_M
8 GB · RTX 4060Q4_K_M · 0.5 GB · ~354 tok/s · 2K8 GB tier →
12 GB · RTX 3060 12GBQ4_K_M · 0.5 GB · ~468 tok/s · 2K12 GB tier →
16 GB · RTX 5060 Ti 16GBQ4_K_M · 0.5 GB · ~582 tok/s · 2K16 GB tier →
24 GB · RTX 4090referenceQ4_K_M · 0.5 GB · ~1310 tok/s · 2K24 GB tier →
Mac 32 GB · Mac Mini M4Q4_K_M · 0.5 GB · ~168 tok/s · 2KMacs →
Mac 64 GB · Mac Studio M4 MaxQ4_K_M · 0.5 GB · ~764 tok/s · 2KMacs →

Alternatives to Kiyo 230M Preview

Similar models

3
ModelScoreVRAMtok/sWhy it is similar
LFM2.5 230MLiquid AI—0.6 GB~1310same size
Tiny MixtralTitanML—0.5 GB~2329same size
MiniCPM V 4.7 1B DSparkOpenBMB—0.5 GB~1489same size

Specifications

Publisher
DedeProGames
Parameters
230M
Architecture
LlamaForCausalLM
Context Length
2,048 tokens
Vocabulary Size
49,152
Release Date
2026-09-04
License
Apache 2.0

Frequently Asked Questions

How much VRAM does Kiyo 230M Preview need?

Kiyo 230M Preview requires 0.5 GB of VRAM at Q4_K_M, or 0.8 GB at BF16.

VRAM = Weights + KV Cache + Overhead

Weights = 230M × 4.8 bits ÷ 8 = 0.1 GB

KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)

Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.

VRAM usage by quantization

0.5 GB

Learn more about VRAM estimation →

What's the best quantization for Kiyo 230M Preview?

For Kiyo 230M Preview, Q4_K_M (0.5 GB) offers the best balance of quality and VRAM usage. Q5_K_M (0.5 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 0.5 GB.

VRAM requirement by quantization

Q2_K
0.5 GB
Q4_K_M ★
0.5 GB
Q5_K_M
0.5 GB
Q6_K
0.6 GB
Q8_0
0.6 GB
BF16
0.8 GB

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run Kiyo 230M Preview on a Mac?

Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Kiyo 230M Preview. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 0.5 GB of usable unified memory (RAM minus macOS overhead).

Can I run Kiyo 230M Preview locally?

Yes — Kiyo 230M Preview can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is Kiyo 230M Preview?

At Q4_K_M, Kiyo 230M Preview can reach ~9600 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~1310 tok/s. 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 ÷ 0.5 × 0.65 = ~10400 tok/s

Estimated speed at Q4_K_M (0.5 GB)

~10400 tok/s
~1310 tok/s
~10400 tok/s
~9600 tok/s

Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.

Learn more about tok/s estimation →

What's the download size of Kiyo 230M Preview?

At Q4_K_M, the download is about 0.14 GB. The full-precision BF16 version is 0.46 GB. The smallest option (Q2_K) is 0.10 GB.

Which GPUs can run Kiyo 230M Preview?

52 consumer GPUs can run Kiyo 230M Preview at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.

Which devices can run Kiyo 230M Preview?

163 devices with unified memory can run Kiyo 230M Preview at Q4_K_M (0.5 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, 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.