Tiny Starcoder Py — Hardware Requirements & GPU Compatibility
ChatCode
Tiny Starcoder Py is a 164M-parameter open language model from BigCode in the StarCoder family. It supports a context window of up to 8,192 tokens. At Q4_K_M it needs about 0.11 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 · 8K)Best on 24 GB (Q4_K_M · ~5956 tok/s · 8K)Macs from 8 GB (Q4_K_M · ~652 tok/s)
bigcode-openrail-m
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Tiny Starcoder Py
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
ollama run hf.co/mradermacher/tiny_starcoder_py-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 23.5 GB free · 8K context
Download: tiny_starcoder_py.Q4_K_M.gguf0.13 GBmradermacher
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 24 GB GPU
- The easiest way to try this model
- Running in a few minutes, easy setup
- Chat right here or connect any app
$5+ bonus for new users
Runs on RTX 3080 Ti (12 GB) (11 GB headroom) · Q4_K_M
- Generation speed
- ~5392 tok/s
- generation speed
- Cost per 1M output tokens
- $0.0063
- per 1M output tokens
How Much VRAM Does Tiny Starcoder Py Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.1 GB | — | 3-bit small — 3-bit small quantization | Fits: 8K context · ~8190 tok/s | |
| 0.1 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 8K context · ~8190 tok/s | |
| 0.1 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 8K context · ~7280 tok/s | |
| 0.1 GB | — | 4-bit · legacy — 4-bit legacy quantization | Fits: 8K context · ~7280 tok/s | |
| 0.1 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 8K context · ~5956 tok/s | |
| 0.1 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 8K context · ~5040 tok/s | |
| 0.1 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 8K context · ~4368 tok/s | |
| 0.2 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 8K context · ~3640 tok/s |
Selected Q4_K_M
4-bit medium quantization0.5GBat 8K context
- Weights
- 0.1 GB
- KV cache
- 0.1 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 23.5 GB free · ~5956 tok/s
≥50 tok/s needs ≥8 GB/s · RTX 5090 ~10589, RTX 3090 Ti ~5956
Which Hardware Can Run Tiny Starcoder Py?
Q4_K_M · 0.1 GBTiny Starcoder Py (Q4_K_M) requires 0.1 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 Starcoder Py, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 7.5 GB free · up to 8K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~4493Simulate output on RTX 3080RTX 2080 Ti~3640Simulate output on RTX 2080 TiRTX 3070 Ti~3595Simulate output on RTX 3070 TiM3*8 GB~652Simulate output on iMac 24" M3 (8 GB)Orin Nano~603Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~434Simulate output on iPhone 1720 GB
fits · 19.5 GB free · up to 8K ctx
1 GPU
RX 7900 XT~4364Simulate output on RX 7900 XT32–40 GB
fits · 31.5 GB free · up to 8K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~10589Simulate output on RTX 5090M5 Max36 GB~2927Simulate output on Mac Studio M5 Max (36 GB)M532 GB~978Simulate output on MacBook Air 13" M5 (32 GB)Orin~1210Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~798Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Tiny Starcoder Py
StarCoder family · 5 models →Similar models
5| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Nandi Mini 150MFrontiersMind | — | 0.6 GB | ~1040 | same size |
| Llm Jp 3 150Mllm-jp | — | 0.5 GB | ~1489 | same size |
| Llama 160MJackFram | — | 0.5 GB | ~1394 | same size |
| DialoGPT SmallMicrosoft | — | 0.1 GB | ~5460 | same size |
| Rwkv 4 169M PileRWKV | — | 1 GB | ~5956 | same size |
Specifications
- Publisher
- BigCode
- Family
- StarCoder
- Parameters
- 164M
- Architecture
- GPTBigCodeForCausalLM
- Context Length
- 8,192 tokens
- Vocabulary Size
- 49,152
- Release Date
- 2023-05-15
- License
- bigcode-openrail-m
Frequently Asked Questions
How much VRAM does Tiny Starcoder Py need?
Tiny Starcoder Py requires 0.1 GB of VRAM at Q4_K_M, or 0.4 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 164M × 4.8 bits ÷ 8 = 0.1 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 Tiny Starcoder Py?
For Tiny Starcoder Py, Q4_K_M (0.1 GB) offers the best balance of quality and VRAM usage. Q5_K_S (0.1 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 0.1 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Tiny Starcoder Py on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Tiny Starcoder Py. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 0.1 GB of usable unified memory (RAM minus macOS overhead).
Can I run Tiny Starcoder Py locally?
Yes — Tiny Starcoder Py can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Tiny Starcoder Py?
At Q4_K_M, Tiny Starcoder Py can reach ~43636 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~5956 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 ÷ 0.11 × 0.65 = ~47273 tok/s
Estimated speed at Q4_K_M (0.1 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Tiny Starcoder Py?
At Q4_K_M, the download is about 0.10 GB. The full-precision BF16 version is 0.33 GB. The smallest option (IQ2_XXS) is 0.05 GB.
Which GPUs can run Tiny Starcoder Py?
52 consumer GPUs can run Tiny Starcoder Py 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 Starcoder Py?
163 devices with unified memory can run Tiny Starcoder Py at Q4_K_M (0.1 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.