Too tight for full placement
Partial GPU offload is possible, but it moves work into slower system memory.
RTX 5090 · fast for smaller models70B home buyer's guide
Which PC is suitable for local 70B LLMs?
Memory capacity comes first for a quantized 70B model. A 32 GB GPU is fast but usually too small for model plus context. Realistic paths start with a tight 48 GB tier and become much more practical at 64 to 96 GB.
Why file size is not the whole story
Model weights plus context plus reserve
A rough Q4 estimate starts near half a byte per parameter. At 70 billion parameters, that is already about 35 GB before format overhead, KV cache, runtime, and the operating system.
70B with useful headroom
Suitable for many Q4/Q5 variants and normal contexts when enough of the pool is truly available.
Unified memory or professional GPURoom for the complete stack
Shared system memory leaves reserve for RAG, embeddings, and supporting services, but it is not all GPU memory.
Capacity focus · verify the UMA limitMoE models, multimodal encoders, extreme context lengths, and higher-bit quantizations can require considerably more memory. Always check the exact model file and runtime.
Three realistic paths
Trade capacity, speed, and cost deliberately
128 GB unified memory
Strength: substantial model capacity in a compact complete system. Compromise: less bandwidth than a large discrete GPU, while the usable pool and software path depend on the platform.
Best for: private 70B assistants and local knowledge systems. Choose between a Windows/Radeon route and an Arm/Linux/NVIDIA route before buying.
Compare exact 128 GB systems96 GB of discrete VRAM
Strength: model and large context can remain on a very fast GPU. Compromise: professional workstations cost far more and demand serious cooling and power.
Best for: intensive professional use, large contexts, and high throughput.
Review RTX PRO 6000 96 GB specs32 GB GPU plus system RAM
Strength: very fast for smaller models, and existing hardware can be reused. Compromise: 70B requires partial offload; PCIe and CPU memory reduce output speed.
Best for: occasional 70B use when 8B to 32B remains the daily workload.
Understand the RTX 5090 pathClear buying decision
Which path fits which goal?
| Priority | Recommended path | Most important check |
|---|---|---|
| Compact Windows 70B use | Ryzen AI Max with 128 GB unified memory | maximum UMA allocation and Radeon runtime support |
| CUDA-native 70B development | NVIDIA GB10 with 128 GB unified memory | Arm/Linux application compatibility and storage |
| High speed and sustained use | workstation with 96 GB VRAM | exact GPU, power, cooling, and support |
| Mostly 8B–32B, occasional 70B | RTX 5090 + 128 GB system RAM | offload support and expected speed |
| Lower budget, patience available | CPU inference with 128 GB RAM | memory bandwidth; accept much lower speed |
| Multiple simultaneous users | professional GPU/server class | KV cache per session and batching behavior |
Choose a complete system
Two practical 128 GB starting points
When local 70B use is the primary goal and peak speed is secondary, a 128 GB unified-memory system is the most direct complete-system path. Choose the operating system and acceleration stack first: Radeon on a conventional compact PC, or NVIDIA on a dedicated Arm/Linux appliance.
Windows-capable capacity path
GMKtec EVO-X2 · 128 GB
Compact Ryzen AI Max+ 395 system with Radeon 8060S and a large shared memory pool.
Why this path
The configuration avoids a multi-GPU build and provides enough total memory for many quantized 70B models, context, and system reserve. Dual M.2 storage makes it easy to separate the operating system from a growing model library.
Main compromise
The soldered memory cannot be expanded, and the Radeon 8060S does not match the speed of a large discrete GPU. Check maximum UMA allocation and HIP or Vulkan support in your intended runtime before buying.
- CPU / graphics
- Ryzen AI Max+ 395 / Radeon 8060S
- Memory
- 128 GB LPDDR5X, shared
- Storage expansion
- 2× M.2 PCIe 4.0; 2 TB included
- Networking
- 2.5GbE, Wi-Fi 7
- Ports
- 2× USB4, HDMI, DisplayPort, SD reader
- Size / power
- 193 × 186 × 77 mm; external 230 W adapter
NVIDIA-native development path
ASUS Ascent GX10 · 128 GB
GB10 Grace Blackwell appliance with coherent unified memory and an Ubuntu-based NVIDIA software stack.
Why this path
CUDA, PyTorch, TensorRT, Jupyter, and Ollama are part of the intended environment, reducing the setup gap for NVIDIA-oriented development. The 128 GB pool supports many quantized 70B workloads, while 10GbE and ConnectX-7 suit networked experiments.
Main compromise
The Arm/Linux platform is not a drop-in replacement for a Windows desktop, and application compatibility must be checked in advance. The linked 1 TB SSD leaves less room for model variants, and the limited hardware warranty is one year.
- Processor / graphics
- NVIDIA GB10 Grace Blackwell Superchip
- Memory
- 128 GB LPDDR5X coherent unified memory
- Storage
- 1 TB M.2 2242 PCIe 4.0 NVMe
- OS / stack
- NVIDIA DGX OS, Ubuntu, CUDA
- Networking
- 10GbE, Wi-Fi 7, ConnectX-7
- Size / power
- 150 × 150 × 51 mm; 180 W input
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Before choosing
Calculate model and context together
Our finder returns a conservative memory tier. For 70B, follow up by checking the actual GGUF file size and planned context in your runtime.