AI & LLM systems
Plan a local AI workstation
Work out whether a model fits in your memory, what the runtime really needs beyond the weight file, and which hardware gives the most VRAM per euro. Every model in the Ollama library's top families is included, with shapes read from the model files. Calculations are deterministic; speed figures are only shown when a sourced benchmark exists.
Workload
Pick a model and context length; VRAM decides everything.
Model memory calculator
The most common download. Small quality loss, big memory saving.
Back ends: CUDA, ROCm, Vulkan, Metal, CPU
Hardware
Fits entirely in VRAM
- 6.4 GB needed (weights 4.5 GB, KV cache 1.0 GB, runtime 0.9 GB) with 15.2 GB usable VRAM.
Memory breakdown
- Model weights (Q4_K_M (GGUF, 4-bit))15.0 GB at FP16
- 4.53 GB
- KV cache (8192 tokens x 1)
- 1.00 GB
- Runtime overhead (activations, buffers, CUDA context)
- 0.90 GB
- Display and driver reserve
- 0.80 GB
- Total runtime requirement
- 7.24 GB
Weight size is the theoretical file size; the total is what the runtime needs. KV cache = 2 x layers x KV heads x head dim x bytes x tokens x concurrency. Engines and versions differ, so treat the overhead as an estimate.
Speed
Measured tokens per second for this GPU from public benchmark pages. Model, quantization and engine are shown because they change the result completely.
- Llama 2 7B Q4_0 · llama.cpp (Vulkan) commit 4fdbc1e · generation (tg128)118 tok/s
- Llama 2 7B Q4_0 · llama.cpp (Vulkan) commit 4fdbc1e · prompt processing (pp512)2017 tok/s
- Llama 2 7B Q4_0 · llama.cpp (ROCm/HIP) commit 00131d6 · generation (tg128)101 tok/s
- Llama 2 7B Q4_0 · llama.cpp (ROCm/HIP) commit 00131d6 · prompt processing (pp512)2152 tok/s
Theoretical single-stream ceiling from memory bandwidth: 128 tok/s (624 GB/s divided by 8.03B active parameters at this quantization). Real results are usually 50 to 80% of this. An upper bound, not a benchmark.
Recommended directions
Based on catalog specs and example prices. Open the builder to turn any of these into a full parts list.
Affordable local AI
A 12 to 16 GB card runs 8B to 14B models at 4-bit entirely in VRAM. Pair with 32 GB RAM.
Intel Arc B580 Limited Edition12 GB · 456 GB/s · 190 W · € 299 example
Gaming and AI hybrid
Choose the strongest gaming card with at least 16 GB so both workloads are served.
NVIDIA GeForce RTX 5090 Founders Edition32 GB · 1792 GB/s · 575 W · € 2.399 example
Maximum VRAM per euro
The best ratio in the catalog right now.
Intel Arc B580 Limited Edition12 GB · 456 GB/s · 190 W · € 299 example · 0.040 GB per euro
Efficient inference
Most memory bandwidth per watt: decode speed per watt is what matters for always-on assistants.
NVIDIA GeForce RTX 5090 Founders Edition32 GB · 1792 GB/s · 575 W · € 2.399 example
Larger local models
24 GB or more lets you run 32B models at 4-bit or 70B with two cards.
AMD Radeon RX 7900 XTX (reference)24 GB · 960 GB/s · 355 W · € 999 example
AI development and experimentation
CUDA still has the broadest framework support; pick the biggest NVIDIA card your budget allows and 64 GB RAM.
NVIDIA GeForce RTX 5090 Founders Edition32 GB · 1792 GB/s · 575 W · € 2.399 example
Framework and model support vary by hardware, operating system, driver and software version. Check the engine's documentation for your exact combination.
Plan an AI build with the guided builder