local-llm-gpu-selection

name	local-llm-gpu-selection
description	Guide for selecting GPUs and hardware for local LLM inference on Dell R730 and comparing to Apple Silicon alternatives. Use when: (1) user asks about running local models (Ollama, llama.cpp), (2) user asks which GPU to buy for LLMs, (3) user wants to compare local models to Claude for coding, (4) user asks about quantized model selection, (5) user asks about Mac Mini/Studio vs GPU server for LLMs. Covers VRAM requirements, memory bandwidth as key metric, R730 GPU compatibility, multi-GPU considerations, and realistic quality comparisons to Claude models.
author	Claude Code
version	1.0.0
date	"2025-06-11T00:00:00.000Z"

Local LLM GPU Selection & Performance Guide

Problem

Choosing the right hardware for local LLM inference requires understanding the relationship between VRAM capacity, memory bandwidth, GPU compatibility with server chassis, and realistic model quality expectations.

Context / Trigger Conditions

• User asks about running quantized models locally (Ollama, llama.cpp)
• User wants to know which GPU fits their server (Dell R730 or similar 2U)
• User asks about Apple Silicon (Mac Mini/Studio) vs datacenter GPUs for LLMs
• User wants to compare local model quality to Claude (Opus/Sonnet/Haiku) for coding

Key Principle: Memory Bandwidth Is Everything

LLM token generation is memory-bandwidth bound, not compute bound. The formula:

approx tokens/sec = memory_bandwidth_GB_s / model_size_GB

This is why Apple Silicon (high bandwidth unified memory) competes with datacenter GPUs despite having less raw compute.

VRAM Requirements by Model Size

Model Size	Quant	VRAM Needed	Examples
7-8B	Q4_K_M	~5 GB	Llama 3.1 8B, Mistral 7B
7-8B	Q8_0	~8 GB
13-14B	Q4_K_M	~8 GB	Qwen 2.5 Coder 14B
22-24B	Q4_K_M	~13-14 GB	Mistral Small, Codestral
32B	Q4_K_M	~20 GB	Qwen 2.5 Coder 32B
32B	Q8_0	~34 GB
70B	Q4_K_M	~40 GB	Llama 3.1 70B
70B	Q8_0	~70 GB

Add ~1-2 GB overhead for KV cache and context. Longer conversations use more.

Dell R730 GPU Compatibility

Constraints

• 2U chassis: Full-height cards fit, but limited to dual-slot width
• PCIe 3.0 x16 slots: 2-3 usable slots depending on riser configuration
• Power: Needs Dell GPU power cable (P/N 0D4J0T) for GPUs >75W TDP
• PSU: Check wattage headroom (dual 750W or 1100W typical)

Compatible GPUs

No external power needed (<=75W):

• Tesla T4: 16 GB, 320 GB/s, 70W — best drop-in option
• Tesla P4: 8 GB, 192 GB/s, 75W — too little VRAM for modern LLMs
• NVIDIA L4: 24 GB, 300 GB/s, 72W — T4 successor, Ada Lovelace, expensive
• NVIDIA A2: 16 GB, 200 GB/s, 60W — worse than T4 in every way, avoid

Requires power cable (>75W):

• Tesla P40: 24 GB, 346 GB/s, 250W — best value per GB
• Tesla V100 PCIe: 32 GB, 900 GB/s, 250W — excellent bandwidth
• Tesla P100 PCIe: 16 GB, 732 GB/s, 250W — same VRAM as T4, not worth it

Won't fit:

• RTX 3090/4090: Too thick (3-slot), too long
• A100: Fits physically but very expensive
• Any consumer RTX: Generally too large for 2U

Multi-GPU Considerations

• Ollama splits model layers across GPUs automatically
• PCIe 3.0 cross-GPU transfer adds ~30-40% latency penalty
• Mismatched GPUs (e.g., T4 + P40) work but the slower card bottlenecks
• R730 PCIe 3.0 limits newer GPU bandwidth (L4 runs at half its rated speed)

Apple Silicon Comparison

Apple Silicon unified memory means ALL system RAM = VRAM with no bus penalty.

Device	Memory	Bandwidth	Advantage
Mac Mini M4 Pro 48 GB	48 GB	273 GB/s	Silent, 25W, no PCIe penalty
Mac Studio M4 Max 128 GB	128 GB	546 GB/s	Run 100B+ models
Mac Studio M4 Ultra 192 GB	192 GB	819 GB/s	Run anything

A Mac Mini M4 Pro 48GB often matches or beats a T4+L4 multi-GPU setup for LLM inference due to zero cross-GPU overhead and high unified bandwidth.

Best Coding Models (for Ollama)

For coding tasks specifically, prefer dedicated coding models:

1. Qwen 2.5 Coder 32B — best open-source coding model in this size class
2. Codestral 22B — Mistral's dedicated coding model
3. DeepSeek Coder V2 — good quality, efficient
4. Llama 3.1 70B — strong general purpose but needs ~40 GB

Realistic Quality Comparison to Claude

For Claude Code-style agentic coding workflows:

Capability	Opus/Sonnet	Haiku	Qwen 2.5 Coder 32B	70B General
Single function gen	Excellent	Good	Good	Decent
Multi-file refactoring	Excellent	Decent	Weak	Weak
Tool use / agentic loops	Excellent	Good	Poor	Poor
Long context (large codebases)	Excellent	Good	Weak	Weak

Local models work for simple completions and code questions. They struggle badly with Claude Code's complex multi-step tool-use workflows, long context windows, and self-correction capabilities.

Quantization Quality Guide

From best to worst quality (and largest to smallest):

• FP16: Full precision, baseline quality
• Q8_0: Near-lossless, ~50% size reduction
• Q6_K: Minimal quality loss
• Q5_K_M: Good balance
• Q4_K_M: Recommended default — best quality/size tradeoff
• Q3_K_M: Noticeable degradation on complex reasoning
• Q2_K: Significant quality loss, emergency only

Verification

• Check GPU compatibility: lspci | grep -i nvidia on the host
• Check available VRAM: nvidia-smi inside the GPU VM
• Check model fit: Ollama shows VRAM usage during ollama run
• Check inference speed: Count tokens/sec in Ollama output

Notes

• GPU prices fluctuate significantly in the used market; check current prices
• The T4 is PCIe 3.0 only; newer GPUs in PCIe 3.0 slots run at reduced bandwidth
• Power consumption matters for 24/7 homelab use (electricity cost)
• For Claude Code specifically, API-based Claude models remain significantly superior to any local model for agentic coding workflows