GPU Lab — map your hardware to LLM inference and fine-tuning capacity

GPU Lab · Public hardware → LLM fit checker
Built by PYXON.AI · Research · Labs · Production AI · Beta · v.2026

Before you download a 70B model or rent an H100, do you actually have the VRAM for it? Open GPU Lab →

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What this is

GPU Lab is a free, public tool from PYXON AI Labs that answers a practical question every edge-AI and local-LLM builder hits:

Given my GPU (or my laptop’s unified memory), which model sizes can I run for inference — and which can I fine-tune?

It combines:

1. A catalog of 120+ GPUs sourced from Flopper.io spec sheets (NVIDIA, AMD, Intel, Apple Silicon, Huawei Ascend, Google TPU, and more).
2. A six-tier performance ladder from consumer entry (T1) through hyperscale / national-AI class hardware (T6).
3. A terminal GPU detector that auto-detects your OS, shows the right command, parses pasted output, and matches your machine to the catalog.
4. An LLM fit matrix for popular open models (1B–405B) covering Q4 inference, FP16 inference, QLoRA fine-tuning, and full fine-tuning estimates.

No login required — live at pyxon.ai/gpu-lab.

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Why we built it

PYXON’s on-device research line — including Pyxon SLLM v0 and our RAG edge benchmark — depends on matching model size to real hardware.

Teams constantly ask:

• Can a 24 GB RTX 4090 run Llama 3.1 70B quantized?
• Does Apple M4 Pro with 24 GB unified memory behave like a 64 GB catalog entry?
• Is QLoRA on Qwen2.5 14B realistic on a 16 GB card?

Cloud pricing pages and model cards rarely answer this in one place. GPU Lab consolidates hardware specs, tier context, and VRAM math into a single interactive view.

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Performance tier ladder (T1 → T6)

Every GPU in the catalog gets a tier ID and performance score derived from VRAM, FP32/FP16/FP8 throughput, form factor, and product class.

Tier	Label	Typical class
T1	Consumer Entry	Budget / small VRAM cards
T2	Consumer Mid	Mainstream 8–16 GB gaming GPUs
T3	Consumer High	Enthusiast desktop (e.g. RTX 4090 class)
T4	Workstation / Pro	RTX A-series, creator / pro GPUs
T5	Datacenter GPU	H100, A100, MI300, SXM/OAM accelerators
T6	Hyperscale / National AI	B200, GH200, Rubin, largest superchip configs

The catalog sorts weak → strong so you can browse from a GTX-class card up to datacenter-scale silicon on one page.

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Detect your GPU from the terminal

GPU Lab detects your browser OS and shows the appropriate commands:

macOS

system_profiler SPDisplaysDataType
system_profiler SPHardwareDataType | grep -E "Chip|Memory"

Linux (NVIDIA)

nvidia-smi --query-gpu=name,memory.total --format=csv,noheader

Windows (PowerShell)

Get-CimInstance Win32_VideoController | Select-Object Name, AdapterRAM, DriverVersion | Format-List

Paste the output into GPU Lab and click Match to catalog GPU. The parser extracts chip name, VRAM, and vendor — then fuzzy-matches against the catalog.

Example: Apple M4 Pro + 24 GB unified memory → matches Apple M4 Pro in the catalog and automatically uses 24 GB for LLM estimates (even when the catalog lists a higher max configuration).

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LLM fit matrix

For each selected GPU, GPU Lab estimates whether popular open models fit under a single-GPU, modest-context assumption:

Workload	Rough VRAM model
Q4 inference	~0.62× params (B) + overhead
FP16 inference	~2.1× params (B) + overhead
QLoRA fine-tune	~0.72× params (B) + ~4.5 GB
Full fine-tune	~14× params (B) + ~8 GB

Models in the matrix include TinyLlama 1.1B through Llama 3.1 405B, plus sizes aligned with our edge research (Qwen2.5 1.5B, Mistral 7B, Llama 3.1 8B/70B, etc.).

A 1.5 GB driver/OS reserve is subtracted from raw VRAM before fitting — so results skew conservative rather than optimistic.

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Example scenarios

RTX 4090 · 24 GB

• Q4 inference: through Llama 3.1 70B class models
• QLoRA fine-tune: through Qwen2.5 14B / Llama 13B class
• Full fine-tune: generally impractical beyond very small models

Apple M4 Pro · 24 GB (detected)

• Strong fit for Pyxon SLLM v0, Qwen2.5 1.5B, Llama 3.2 3B, Mistral 7B, Llama 3.1 8B inference
• Pairs naturally with on-device RAG from our edge benchmark research

NVIDIA H100 · 80 GB

• T5 datacenter tier
• FP16 inference and QLoRA on 70B class models; multi-GPU needed for 405B or full fine-tunes

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Data sources and limits

• GPU specs are ingested from Flopper.io spec sheets via an internal scraper (scripts/gpu_all/). Re-run the catalog builder after refreshing scraped data.
• VRAM estimates are heuristics — actual usage varies with context length, batch size, framework (Ollama, llama.cpp, vLLM, PyTorch), and quantization scheme.
• Apple unified memory is treated as usable VRAM for LLM workloads; real pressure from CPU/GPU sharing is not modeled.
• The tool is informational, not a substitute for profiling your exact stack.

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Related PYXON AI Labs work

Resource	Link
GPU Lab (live tool)	/gpu-lab
Pyxon SLLM v0 (1.6 GB Arabic edge model)	/blogs/pyxon-sllm-v0
On-device RAG benchmark	/blogs/rag-edge-weak-llm-benchmark
Reasoning search (Arabic)	/blogs/embedding-semantic-search-arabic-reasoning

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Try it

1. Open GPU Lab
2. Run the terminal command for your OS and paste the output
3. Review matched tier, VRAM, and LLM fit table
4. Cross-check with Pyxon SLLM v0 on Ollama if you are targeting Arabic edge deployment

Research that actually works — know your hardware before you pick your model.

→ www.pyxon.ai
→ Labs / careers: info@pyxon.com · hr@pyxon.com

© 2026 PYXON Systems · Built by the Labs · MENA · v.2026