TIPO 500M — Hardware Requirements & GPU Compatibility
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TIPO 500M is a 508M-parameter open language model from KBlueLeaf. It supports a context window of up to 2,048 tokens. At Q4_K_M it needs about 0.81 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 · 2K)Best on 24 GB (Q4_K_M · ~809 tok/s · 2K)Macs from 8 GB (Q4_K_M · ~89 tok/s)
Other
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run TIPO 500M
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 KBlueLeaf/TIPO-500M --max-model-len 2048vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3080 Ti (12 GB) (11 GB headroom) · Q4_K_M
- Generation speed
- ~732 tok/s
- generation speed
- Cost per 1M output tokens
- $0.046
- per 1M output tokens
How Much VRAM Does TIPO 500M Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.7 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 2K context · ~898 tok/s | |
| 0.8 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 2K context · ~862 tok/s | |
| 0.8 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 2K context · ~809 tok/s | |
| 0.9 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 2K context · ~753 tok/s | |
| 0.9 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 2K context · ~705 tok/s | |
| 1.0 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 2K context · ~642 tok/s | |
| 1.5 GB | — | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 2K context · ~428 tok/s |
Selected Q4_K_M
4-bit medium quantization0.8GBat 2K context
- Weights
- 0.3 GB
- KV cache
- 0.2 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 23.2 GB free · ~809 tok/s
≥50 tok/s needs ≥62 GB/s · RTX 5090 ~1438, RTX 3090 Ti ~809
Which Hardware Can Run TIPO 500M?
Q4_K_M · 0.8 GBTIPO 500M (Q4_K_M) requires 0.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ 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 TIPO 500M, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 7.2 GB free · up to 2K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~610Simulate output on RTX 3080RTX 2080 Ti~494Simulate output on RTX 2080 TiRTX 3070 Ti~488Simulate output on RTX 3070 TiM3*8 GB~89Simulate output on iMac 24" M3 (8 GB)Orin Nano~82Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~59Simulate output on iPhone 1720 GB
fits · 19.2 GB free · up to 2K ctx
1 GPU
RX 7900 XT~593Simulate output on RX 7900 XT32–40 GB
fits · 31.2 GB free · up to 2K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~1438Simulate output on RTX 5090M5 Max36 GB~398Simulate output on Mac Studio M5 Max (36 GB)M532 GB~133Simulate output on MacBook Air 13" M5 (32 GB)Orin~164Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~108Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Similar models
4| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Falcon H1 0.5B BaseTII UAE | — | 1.2 GB | ~950 | same size |
| Qwen2.5 0.5B InstructAlibaba | — | 0.8 GB | ~1057 | same size |
| Qwen2 0.5B InstructAlibaba | — | 0.8 GB | ~1057 | same size |
| Qwen3 4B DFlash B16z-lab | — | 0.8 GB | ~1008 | same size |
Specifications
- Publisher
- KBlueLeaf
- Parameters
- 508M
- Architecture
- LlamaForCausalLM
- Context Length
- 2,048 tokens
- Vocabulary Size
- 32,013
- Release Date
- 2024-09-28
- License
- Other
Frequently Asked Questions
How much VRAM does TIPO 500M need?
TIPO 500M requires 0.8 GB of VRAM at Q4_K_M, or 1.5 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 508M × 4.8 bits ÷ 8 = 0.3 GB
KV Cache + Overhead ≈ 0.5 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 TIPO 500M?
For TIPO 500M, Q4_K_M (0.8 GB) offers the best balance of quality and VRAM usage. Q5_K_M (0.9 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 0.7 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run TIPO 500M on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run TIPO 500M. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 0.8 GB of usable unified memory (RAM minus macOS overhead).
Can I run TIPO 500M locally?
Yes — TIPO 500M can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is TIPO 500M?
At Q4_K_M, TIPO 500M can reach ~5926 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~809 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 ÷ 0.81 × 0.65 = ~6420 tok/s
Estimated speed at Q4_K_M (0.8 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of TIPO 500M?
At Q4_K_M, the download is about 0.30 GB. The full-precision BF16 version is 1.02 GB. The smallest option (Q2_K) is 0.22 GB.
Which GPUs can run TIPO 500M?
52 consumer GPUs can run TIPO 500M 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 TIPO 500M?
163 devices with unified memory can run TIPO 500M at Q4_K_M (0.8 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.