The GuageLLM 23M — Hardware Requirements & GPU Compatibility
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The GuageLLM 23M is a 23M-parameter open language model from Hai929. It supports a context window of up to 64 tokens. At Q4_K_M it needs about 0.02 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M)Best on 24 GB (Q4_K_M · ~32760 tok/s)Macs from 8 GB (Q4_K_M · ~3584 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run The GuageLLM 23M
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 serve Hai929/The_GuageLLM_23M --max-model-len 64vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3060 12GB (12 GB headroom) · Q4_K_M
- Generation speed
- ~11700 tok/s
- generation speed
- Cost per 1M output tokens
- $0.0015
- per 1M output tokens
How Much VRAM Does The GuageLLM 23M Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.0 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: — context · ~65520 tok/s | |
| 0.0 GB | — | 3-bit medium — 3-bit medium quantization | Fits: — context · ~65520 tok/s | |
| 0.0 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: — context · ~32760 tok/s | |
| 0.0 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: — context · ~32760 tok/s | |
| 0.0 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: — context · ~32760 tok/s | |
| 0.0 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: — context · ~21840 tok/s | |
| 0.1 GB | — | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: — context · ~13104 tok/s |
Selected Q4_K_M
4-bit medium quantization0.0GBat 0K context
- Weights
- 0.1 GB
- KV cache
- 0.0 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 24.0 GB free · ~32760 tok/s
≥50 tok/s needs ≥2 GB/s · RTX 5090 ~58240, RTX 3090 Ti ~32760
Which Hardware Can Run The GuageLLM 23M?
Q4_K_M · 0.0 GBThe GuageLLM 23M (Q4_K_M) requires 0.0 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 The GuageLLM 23M, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 8.0 GB free · up to — ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~24710Simulate output on RTX 3080RTX 2080 Ti~20020Simulate output on RTX 2080 TiRTX 3070 Ti~19770Simulate output on RTX 3070 TiM3*8 GB~3584Simulate output on iMac 24" M3 (8 GB)Orin Nano~3315Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~2387Simulate output on iPhone 1720 GB
fits · 20.0 GB free · up to — ctx
1 GPU
RX 7900 XT~24000Simulate output on RX 7900 XT32–40 GB
fits · 32.0 GB free · up to — ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~58240Simulate output on RTX 5090M5 Max36 GB~16100Simulate output on Mac Studio M5 Max (36 GB)M532 GB~5376Simulate output on MacBook Air 13" M5 (32 GB)Orin~6656Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~4388Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Specifications
- Publisher
- Hai929
- Parameters
- 23M
- Architecture
- GPT2LMHeadModel
- Context Length
- 64 tokens
- Vocabulary Size
- 32,000
- Release Date
- 2025-12-12
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does The GuageLLM 23M need?
The GuageLLM 23M requires 0.0 GB of VRAM at Q4_K_M, or 0.1 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 23M × 4.8 bits ÷ 8 = 0 GB
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
What's the best quantization for The GuageLLM 23M?
For The GuageLLM 23M, Q4_K_M (0.0 GB) offers the best balance of quality and VRAM usage. Q5_K_M (0.0 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 0.0 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run The GuageLLM 23M on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run The GuageLLM 23M. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 0.0 GB of usable unified memory (RAM minus macOS overhead).
Can I run The GuageLLM 23M locally?
Yes — The GuageLLM 23M can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is The GuageLLM 23M?
At Q4_K_M, The GuageLLM 23M can reach ~240000 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~32760 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 ÷ 0.02 × 0.65 = ~260000 tok/s
Estimated speed at Q4_K_M (0.0 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of The GuageLLM 23M?
At Q4_K_M, the download is about 0.01 GB. The full-precision BF16 version is 0.05 GB. The smallest option (Q2_K) is 0.01 GB.
Which GPUs can run The GuageLLM 23M?
52 consumer GPUs can run The GuageLLM 23M 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 The GuageLLM 23M?
163 devices with unified memory can run The GuageLLM 23M at Q4_K_M (0.0 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.