spark/context/ai-workloads.md

id	title	status	source_sections	related_topics	key_equations	key_terms	images	examples	open_questions
ai-workloads	AI Workloads and Model Capabilities	established	Web research: NVIDIA newsroom, Dell product page, WCCFTech	[gb10-superchip memory-and-storage ai-frameworks multi-unit-stacking]	[model-memory-estimate]	[llm inference fine-tuning quantization fp4 fp8 fp16 parameter-count]	[]	[llm-memory-estimation.md]	[Actual tokens/sec benchmarks for common models (Llama 3.3 70B, Mixtral, etc.) Maximum batch size for inference at various model sizes Fine-tuning performance — how long to SFT a 7B model on this hardware? Stable Diffusion / image generation performance Training from scratch — is it practical for any meaningful model size?]

AI Workloads and Model Capabilities

The Dell Pro Max GB10 is designed primarily for local AI inference and fine-tuning, bringing capabilities previously requiring cloud or data center hardware to a desktop form factor.

1. Headline Capabilities

• Up to 200 billion parameter models locally (with quantization)
• 1 PFLOP (1,000 TFLOPS) at FP4 precision
• Llama 3.3 70B confirmed to run locally (single unit)
• Up to 400B parameter models with two-unit stacking (see multi-unit-stacking)

2. Model Size vs. Memory

With 128 GB of unified memory, the system can hold:

Precision	Bytes/Param	Max Params (approx)	Example Models
FP4	0.5 B	~200B+	Large quantized models
FP8/INT8	1 B	~100B	Llama 3.3 70B, Mixtral
FP16	2 B	~50-55B	Medium models at full prec
FP32	4 B	~25-28B	Small models, debugging

Note: Actual usable capacity is less than 128 GB due to OS, KV cache, framework overhead, and activation memory. Estimates assume ~85-90% of memory available for model weights.

3. Primary Use Cases

Local LLM Inference

• Run large language models privately, no cloud dependency
• Interactive chat, code generation, document analysis
• Privacy-sensitive applications (medical, legal, financial)

Fine-Tuning

• Supervised fine-tuning (SFT) of models using NVIDIA NeMo
• LoRA/QLoRA for parameter-efficient fine-tuning of larger models
• Custom domain adaptation

AI Prototyping

• Rapid iteration on model architectures
• Dataset preprocessing with RAPIDS
• Experiment tracking and evaluation

Data Science

• GPU-accelerated analytics with RAPIDS
• Large-scale data processing
• Graph analytics

4. Target Users

• AI researchers and developers
• Privacy-conscious organizations
• Academic institutions
• AI prototyping teams
• Independent developers building AI applications

Key Relationships

• Compute provided by: gb10-superchip
• Memory constraints: memory-and-storage
• Frameworks used: ai-frameworks
• Scaling beyond single unit: multi-unit-stacking