Run popular 7B–13B models locally without breaking the bank.
GPU: RTX 4070 Ti
Best value Ada GPU for 7B–13B workloads.
CPU: Ryzen 7 5700X
Affordable 8-core chip that pairs well with mid-tier GPUs.
RAM: 32GB DDR4
Enough memory for inference stack plus monitoring tools.
Complete system
Ready to assemble with standard tools. Boots local AI workloads on day one.
Real-world throughput for popular models, plus how this build compares to our other configurations.
| Model tier | Example model | Budget (This build)This build | Recommended | Premium |
|---|---|---|---|---|
| Small (7B–8B) | Qwen 2.5 7B | ~65 tok/s | ~118 tok/s | ~156 tok/s |
| Llama 3.1 8B | ~58 tok/s | ~105 tok/s | ~142 tok/s | |
| Mistral 7B v0.2 | ~70 tok/s | ~125 tok/s | ~165 tok/s | |
| Medium (13B–32B) | DeepSeek 33B (Q4) Expect higher latency but big gains for reasoning | ~35 tok/s | ~62 tok/s | ~89 tok/s |
| Llama 3.1 13B | ~28 tok/s | ~52 tok/s | ~67 tok/s | |
| Large (70B) | Llama 3.1 70B Requires Q4 on budget builds | ~12 tok/s | ~25 tok/s | ~45 tok/s |
Benchmark figures represent Q4 quantization. Expect ~40% slower speeds for FP16 / full-precision runs.
Every component is intentionally chosen to balance performance, thermals, and future upgrades. Start with these essentials and expand as your workloads grow.
Speeds are based on Q4 quantization benchmarks. Use the filters to explore what runs best on this hardware.
| Model | Size | Min VRAM (Q4) | Est. speed | Context window | Best for |
|---|---|---|---|---|---|
| Llama 3.1 8B Instruct Meta | 8.0B | 4 GB | — | 8K | Fast chat |
| Qwen2.5 7B Instruct Alibaba | 7.0B | 4 GB | — | 8K | Fast chat |
| Mistral 7B Instruct V0.2 Mistral AI | 7.3B | 4 GB | — | 8K | Fast chat |
| Gemma 2 9b It Google | 9.0B | 5 GB | — | 8K | Fast chat |
| Phi 3 Mini 128k Instruct Microsoft | 3.8B | 2 GB | — | 128K | Fast chat |
| DeepSeek R1 Distill Qwen 7B DeepSeek | 7.0B | 4 GB | — | 128K | Reasoning & agents |
| DeepSeek R1 Distill Qwen 32B DeepSeek | 32.0B | 16 GB | — | 128K | Reasoning & agents |
| Llama 3.1 13B Instruct Meta | 13.0B | 7 GB | — | 8K | General chat |
| Phi 3 Medium 128k Instruct Microsoft | 14.0B | 7 GB | — | 128K | General chat |
| DeepSeek R1 Distill Llama 8B DeepSeek | 8.0B | 4 GB | — | 128K | Reasoning & agents |
Daily chat
Llama 3.1 8B Instruct (—)
Complex tasks
DeepSeek R1 Distill Qwen 32B (—)
Real-world scenarios where this hardware shines. Each card includes the model we recommend and what to expect for responsiveness.
Keep conversations private with models like Qwen 7B or Llama 3.1 8B running entirely offline.
Use DeepSeek Coder or similar local models for reliable completions that respect your codebase.
Draft blog posts, documentation, and emails quickly with Mistral 7B or Gemma 9B.
Swap models in minutes, experiment with quantizations, and build intuition for local AI.
Spot the trade-offs between tiers and know exactly when it makes sense to step up.
| Feature | Budget (This build)RTX 4070 Ti • — | RecommendedRTX 4080 • — | PremiumRTX 4090 • — |
|---|---|---|---|
| Total cost | $1,463 | $2,333 | $3,573 |
| GPU | RTX 4070 Ti | RTX 4080 | RTX 4090 |
| VRAM | — | — | — |
| System memory | 32GB DDR4 | 64GB DDR5 | 128GB DDR5 |
| 7B models | ~65 tok/s | ~118 tok/s | ~156 tok/s |
| 13B models | ~28 tok/s | ~52 tok/s | ~67 tok/s |
| 70B models | ~12 tok/s | ~25 tok/s | ~45 tok/s |
| Best for | Daily AI tasks, coding assistants | Power users, heavier experimentation | Production workloads, agents |
GPU: Jump to RTX 4080/4090
Adds 4–12GB of VRAM and unlocks much faster 13B+ inference (~$800–$1,500).
RAM: Expand to 64GB
Keeps large contexts and tooling responsive when multitasking (~$80).
Storage: Add 2TB NVMe
Room for multiple quantizations and datasets (~$150).
The three questions we hear most often about this build and who it's for.
Check the compatible models table. Anything up to 13B runs smoothly. 32B+ models work in Q4 quantization, with slower responses on budget hardware.
It's excellent for personal productivity and prototyping. For shared production workloads or enterprise SLAs, step up to the Premium build with RTX 4090.
If you've built a PC before, plan ~2 hours. First time? Budget 4 hours and follow our assembly guide. All parts are standard ATX with no proprietary connectors.
Still have questions? Join our Discord or read the full documentation.