GPT Bigcode Santacoder — Hardware Requirements & GPU Compatibility
ChatCode
GPT Bigcode Santacoder is a 1.1B-parameter open language model from BigCode. It supports a context window of up to 2,048 tokens. At Q4_K_M it needs about 0.74 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 · ~885 tok/s · 2K)Macs from 8 GB (Q4_K_M · ~97 tok/s)
openrail
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run GPT Bigcode Santacoder
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 bigcode/gpt_bigcode-santacoder --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
- ~801 tok/s
- generation speed
- Cost per 1M output tokens
- $0.042
- per 1M output tokens
How Much VRAM Does GPT Bigcode Santacoder Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.5 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 2K context · ~1236 tok/s | |
| 0.6 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 2K context · ~1092 tok/s | |
| 0.7 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 2K context · ~885 tok/s | |
| 0.9 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 2K context · ~745 tok/s | |
| 1.0 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 2K context · ~642 tok/s | |
| 1.2 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 2K context · ~528 tok/s | |
| 2.5 GB | — | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 2K context · ~265 tok/s |
Selected Q4_K_M
4-bit medium quantization0.7GBat 2K context
- Weights
- 0.2 GB
- KV cache
- 0.2 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 23.3 GB free · ~885 tok/s
≥50 tok/s needs ≥57 GB/s · RTX 5090 ~1574, RTX 3090 Ti ~885
Which Hardware Can Run GPT Bigcode Santacoder?
Q4_K_M · 0.7 GBGPT Bigcode Santacoder (Q4_K_M) requires 0.7 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 GPT Bigcode Santacoder, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 7.3 GB free · up to 2K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~668Simulate output on RTX 3080RTX 2080 Ti~541Simulate output on RTX 2080 TiRTX 3070 Ti~534Simulate output on RTX 3070 TiM3*8 GB~97Simulate output on iMac 24" M3 (8 GB)Orin Nano~90Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~65Simulate output on iPhone 1720 GB
fits · 19.3 GB free · up to 2K ctx
1 GPU
RX 7900 XT~649Simulate output on RX 7900 XT32–40 GB
fits · 31.3 GB free · up to 2K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~1574Simulate output on RTX 5090M5 Max36 GB~435Simulate output on Mac Studio M5 Max (36 GB)M532 GB~145Simulate output on MacBook Air 13" M5 (32 GB)Orin~180Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~119Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| MiniCPM5 1BOpenBMB | — | 1.3 GB | ~662 | same size |
| HunyuanOCRTencent | — | 1.8 GB | ~612 | same size |
| TinyLlama 1.1B Chat v1.0TinyLlama | — | 1 GB | ~649 | same size |
| LLaMA3.1 8B Instruct DFlash UltraChatz-lab | — | 2.7 GB | ~269 | same size |
| LFM2.5 1.2B ThinkingLiquid AI | — | 1.2 GB | ~612 | same size |
| LFM2 1.2BLiquid AI | — | 1.2 GB | ~612 | same size |
Specifications
- Publisher
- BigCode
- Parameters
- 1.1B
- Architecture
- GPTBigCodeForCausalLM
- Context Length
- 2,048 tokens
- Vocabulary Size
- 49,280
- Release Date
- 2023-04-06
- License
- openrail
Frequently Asked Questions
How much VRAM does GPT Bigcode Santacoder need?
GPT Bigcode Santacoder requires 0.7 GB of VRAM at Q4_K_M, or 2.5 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 1.1B × 4.8 bits ÷ 8 = 0.7 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 GPT Bigcode Santacoder?
For GPT Bigcode Santacoder, Q4_K_M (0.7 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 0.5 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run GPT Bigcode Santacoder on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run GPT Bigcode Santacoder. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 0.7 GB of usable unified memory (RAM minus macOS overhead).
Can I run GPT Bigcode Santacoder locally?
Yes — GPT Bigcode Santacoder can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is GPT Bigcode Santacoder?
At Q4_K_M, GPT Bigcode Santacoder can reach ~6487 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~885 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 ÷ 0.74 × 0.65 = ~7027 tok/s
Estimated speed at Q4_K_M (0.7 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of GPT Bigcode Santacoder?
At Q4_K_M, the download is about 0.67 GB. The full-precision BF16 version is 2.25 GB. The smallest option (Q2_K) is 0.48 GB.
Which GPUs can run GPT Bigcode Santacoder?
52 consumer GPUs can run GPT Bigcode Santacoder 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 GPT Bigcode Santacoder?
163 devices with unified memory can run GPT Bigcode Santacoder at Q4_K_M (0.7 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.