Best GPU for Local AI Coding
作者： Jakub Rusinowski · 最后更新： 2026年10月7日
Ranked for coding and software engineering: Writing, refactoring and debugging code in an editor or terminal, with the model reading real project files.
Best overall: Intel Arc Pro B70
32 GB VRAM at 608 GB/s. It runs 151 of the models that qualify for this workload; the strongest is Qwen 3.7 35B-A3B at an estimated 120.8 tokens/sec.
The picks
| GPU | VRAM | MSRP | Models that fit | Best model it runs | Est. speed | |
|---|---|---|---|---|---|---|
| Best overall | Intel Arc Pro B70 | 32 GB | $949 | 151 | Qwen 3.7 35B-A3B | ~120.8 tok/s |
| Best value | Intel Arc B580 | 12 GB | $249 | 113 | Qwen 3 14B | ~33 tok/s |
| Budget pick | Intel Arc B570 | 10 GB | $219 | 112 | Qwen3-Coder 8B | ~47.2 tok/s |
| Most memory | NVIDIA DGX Spark | 128 GB | $4,699 | 168 | Devstral-2 123B | ~2.7 tok/s |
Full ranking for coding and software engineering
| GPU | Score | VRAM | MSRP | Models fit | Est. speed | Tok/s per watt | Cost per model |
|---|---|---|---|---|---|---|---|
| Intel Arc Pro B70 | 82.3 | 32 GB | $949 | 151 | ~120.8 | 0.54 | $6 |
| AMD Radeon RX 7900 XTX | 81 | 24 GB | $999 | 151 | ~164.8 | 0.46 | $7 |
| AMD Radeon AI PRO R9700 | 80.2 | 32 GB | $1,299 | 151 | ~125.4 | 0.42 | $9 |
| NVIDIA GeForce RTX 3090 | 79.4 | 24 GB | $1,499 | 151 | ~162.2 | 0.46 | $10 |
| NVIDIA GeForce RTX 4090 | 78.8 | 24 GB | $1,599 | 151 | ~169.9 | 0.38 | $11 |
| NVIDIA GeForce RTX 3090 Ti | 78.2 | 24 GB | $1,999 | 151 | ~169.9 | 0.38 | $13 |
| NVIDIA GeForce RTX 5090 | 77.9 | 32 GB | $1,999 | 151 | ~233 | 0.41 | $13 |
| NVIDIA RTX A6000 | 77.6 | 48 GB | $4,649 | 155 | ~142.4 | 0.47 | $30 |
| NVIDIA RTX 6000 Ada Generation | 77.2 | 48 GB | $6,799 | 155 | ~164.8 | 0.55 | $44 |
| NVIDIA L40S | 76.9 | 48 GB | $7,499 | 155 | ~154.1 | 0.44 | $48 |
| Intel Arc B580 | 67.4 | 12 GB | $249 | 113 | ~33 | 0.17 | $2 |
| NVIDIA GeForce RTX 5070 | 62.6 | 12 GB | $549 | 113 | ~46.9 | 0.19 | $5 |
How these numbers are calculated
- GPUs are scored on four axes: capability (45%), speed (30%), value (20%) and efficiency (5%), each normalised across the whole ranking.
- Capability means the intrinsic strength of the best model the card can hold — not how many models fit, and not how fast it streams a small one.
- Speed is capped at 40 tok/s: past that, more throughput does not change how the model feels to use.
- Apple Silicon is excluded from this ranking. Those entries price a whole computer and rate a chip's package power, so on price-per-capability and performance-per-watt they would beat every add-in card by construction. Apple hardware is covered on the macOS platform page instead.
- Coding score carries 60% of the capability weight and reasoning the remaining 35%, because most real editor work is "understand this repo, then write correct code". Context is weighted heavily: below 16K tokens a model cannot hold a meaningful slice of a codebase, and 128K is treated as fully served. Latency matters (0.7) — a coding assistant slower than you type stops being used.
FAQ
What is the best GPU for coding and software engineering?
The Intel Arc Pro B70 — 32 GB of VRAM runs 151 qualifying models, the strongest being Qwen 3.7.
What is the cheapest GPU that works for coding and software engineering?
The Intel Arc B570 at $219, which runs 112 qualifying models.
How much VRAM do I need for coding and software engineering?
8 GB is the entry point at which a model for this workload will run at all. More memory buys a stronger model, not just a faster one.
What These GPUs Run
- Best models for the Intel Arc Pro B70
- Best models for the Intel Arc B580
- Best models for the Intel Arc B570
- Best models for the NVIDIA DGX Spark
GPU Reviews
- Intel Arc Pro B70 review
- AMD Radeon RX 7900 XTX review
- AMD Radeon AI PRO R9700 review
- NVIDIA GeForce RTX 3090 review