Hardware

GPUs for local LLMs

Memory capacity decides what fits; memory bandwidth largely decides single-request speed. The throughput figures here are estimates from bandwidth, useful for orientation only.

Read this before buying Staff count does not size a server. Model size, quantisation, context length, concurrent requests and latency targets do. Use the sizing calculator for a provisional range, and measure your own workload before committing. We can run that measurement for you.

Evidence status for every figure on this page: Estimated Prices are approximate 2026 street prices from public listings.

GPUVRAMBandwidthEst. 8B Q4 tok/s~Price
NVIDIA B200 192 GB 8000 GB/s ~480 $40,000
Apple M3 Ultra 192 GB 800 GB/s ~48 $4,000
Apple M4 Ultra 128 GB 1000 GB/s ~60 $4,500
NVIDIA H100 80GB 80 GB 3350 GB/s ~201 $30,000
NVIDIA A100 80GB 80 GB 2039 GB/s ~122 $15,000
NVIDIA RTX 6000 Ada 48 GB 960 GB/s ~58 $6,800
Apple M4 Max 36 GB 410 GB/s ~25 $2,400
NVIDIA RTX 5090 32 GB 1792 GB/s ~108 $2,500
NVIDIA RTX 4090 24 GB 1008 GB/s ~60 $1,600
AMD Radeon RX 7900 XTX 24 GB 960 GB/s ~58 $900
NVIDIA RTX 3090 24 GB 936 GB/s ~56 $700
NVIDIA RTX 5070 Ti 16 GB 896 GB/s ~54 $750
NVIDIA RTX 4070 Ti SUPER 16GB 16 GB 672 GB/s ~40 $800
AMD Radeon RX 9070 XT 16 GB 640 GB/s ~38 $600
NVIDIA RTX 4060 Ti 16GB 16 GB 288 GB/s ~17 $450
Steam Deck OLED (AMD APU) 16 GB 89 GB/s ~5 $549
NVIDIA RTX 5070 12 GB 672 GB/s ~40 $550
NVIDIA RTX 3060 12GB 12 GB 360 GB/s ~22 $250
NVIDIA Jetson Orin Nano 8GB 8 GB 68 GB/s ~4 $199
Raspberry Pi 5 (8GB) 0 GB 25 GB/s — $80

Estimate: tokens/sec ≈ bandwidth × 0.06 for an 8B model at Q4_K_M, single request, 2,048-token context. Batched serving raises total throughput but lowers per-user speed. Long contexts need much more memory for the KV cache.

What we mean by a benchmark

A useful benchmark records the model and runtime versions, hardware, quantisation, input and output lengths, concurrency, time to first token, end-to-end latency, resource use and task quality. Our labelling rules are in the editorial policy.