GPT S 5M — Hardware Requirements & GPU Compatibility
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GPT S 5M is a 5M-parameter open language model from AxiomicLabs. It supports a context window of up to 512 tokens. At BF16 it needs about 0.32 GB of VRAM — see which GPUs and Macs can run it below.
769 downloads
12 likesSpecifications
- Publisher
- AxiomicLabs
- Parameters
- 5M
- Architecture
- GPTX3ForCausalLM
- Context Length
- 512 tokens
- Vocabulary Size
- 4,096
- Release Date
- 2026-05-17
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does GPT S 5M Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 0.3 GB | — | 0.01 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run GPT S 5M?
BF16 · 0.3 GBGPT S 5M (BF16) 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. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run GPT S 5M?
BF16 · 0.3 GB59 devices with unified memory can run GPT S 5M, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomFrequently Asked Questions
- How much VRAM does GPT S 5M need?
GPT S 5M requires 0.3 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 5M × 16 bits ÷ 8 = 0 GB
KV Cache + Overhead ≈ 0.3 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
BF160.3 GB- Can I run GPT S 5M on a Mac?
GPT S 5M requires at least 0.3 GB at BF16, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run GPT S 5M locally?
Yes — GPT S 5M can run locally on consumer hardware. At BF16 quantization it needs 0.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is GPT S 5M?
At BF16, GPT S 5M can reach ~13750 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~2048 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 ÷ 0.3 × 0.65 = ~16250 tok/s
Estimated speed at BF16 (0.3 GB)
~16250 tok/s~2048 tok/s~16250 tok/s~13750 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of GPT S 5M?
At BF16, the download is about 0.01 GB.
- Which GPUs can run GPT S 5M?
50 consumer GPUs can run GPT S 5M at BF16 (0.3 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 50 GPUs have plenty of headroom for comfortable inference.
- Which devices can run GPT S 5M?
59 devices with unified memory can run GPT S 5M at BF16 (0.3 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, 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.