Tiny LLM — Hardware Requirements & GPU Compatibility
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Tiny LLM is a 13M-parameter open language model from arnir0. It supports a context window of up to 1,024 tokens. At Q4_K_M it needs about 0.31 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 · ~2114 tok/s)Macs from 8 GB (Q4_K_M · ~231 tok/s)
MIT
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Tiny LLM
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
ollama run hf.co/mradermacher/Tiny-LLM-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 23.7 GB free · 1K context
Download: Tiny-LLM.Q4_K_M.gguf0.01 GBmradermacher
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (11 GB headroom) · Q4_K_M
- Generation speed
- ~755 tok/s
- generation speed
- Cost per 1M output tokens
- $0.026
- per 1M output tokens
How Much VRAM Does Tiny LLM Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.3 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: — context · ~2114 tok/s | |
| 0.3 GB | — | 3-bit small — 3-bit small quantization | Fits: — context · ~2114 tok/s | |
| 0.3 GB | — | 3-bit medium — 3-bit medium quantization | Fits: — context · ~2114 tok/s | |
| 0.3 GB | — | 3-bit large — 3-bit large quantization | Fits: — context · ~2114 tok/s | |
| 0.3 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: — context · ~2114 tok/s | |
| 0.3 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: — context · ~2114 tok/s | |
| 0.3 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: — context · ~2114 tok/s | |
| 0.3 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: — context · ~2114 tok/s |
Selected Q4_K_M
4-bit medium quantization0.3GBat 1K context
- Weights
- 0.1 GB
- KV cache
- 0.0 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 23.7 GB free · ~2114 tok/s
≥50 tok/s needs ≥24 GB/s · RTX 5090 ~3757, RTX 3090 Ti ~2114
Which Hardware Can Run Tiny LLM?
Q4_K_M · 0.3 GBTiny LLM (Q4_K_M) requires 0.3 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 Tiny LLM, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 7.7 GB free · up to — ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~1594Simulate output on RTX 3080RTX 2080 Ti~1292Simulate output on RTX 2080 TiRTX 3070 Ti~1276Simulate output on RTX 3070 TiM3*8 GB~231Simulate output on iMac 24" M3 (8 GB)Orin Nano~214Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~154Simulate output on iPhone 1720 GB
fits · 19.7 GB free · up to — ctx
1 GPU
RX 7900 XT~1548Simulate output on RX 7900 XT32–40 GB
fits · 31.7 GB free · up to — ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~3757Simulate output on RTX 5090M5 Max36 GB~1039Simulate output on Mac Studio M5 Max (36 GB)M532 GB~347Simulate output on MacBook Air 13" M5 (32 GB)Orin~429Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~283Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Specifications
- Publisher
- arnir0
- Parameters
- 13M
- Architecture
- LlamaForCausalLM
- Context Length
- 1,024 tokens
- Vocabulary Size
- 32,000
- Release Date
- 2024-11-03
- License
- MIT
Frequently Asked Questions
How much VRAM does Tiny LLM need?
Tiny LLM requires 0.3 GB of VRAM at Q4_K_M, or 0.3 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 13M × 4.8 bits ÷ 8 = 0 GB
KV Cache + Overhead ≈ 0.3 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
What's the best quantization for Tiny LLM?
For Tiny LLM, Q4_K_M (0.3 GB) offers the best balance of quality and VRAM usage. Q5_K_S (0.3 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 0.3 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Tiny LLM on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Tiny LLM. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 0.3 GB of usable unified memory (RAM minus macOS overhead).
Can I run Tiny LLM locally?
Yes — Tiny LLM can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Tiny LLM?
At Q4_K_M, Tiny LLM can reach ~15484 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~2114 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 ÷ 0.31 × 0.65 = ~16774 tok/s
Estimated speed at Q4_K_M (0.3 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Tiny LLM?
At Q4_K_M, the download is about 0.01 GB. The full-precision BF16 version is 0.03 GB. The smallest option (Q2_K) is 0.01 GB.
Which GPUs can run Tiny LLM?
52 consumer GPUs can run Tiny LLM 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 Tiny LLM?
163 devices with unified memory can run Tiny LLM at Q4_K_M (0.3 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.