Best GPU for Local LLM Inference in 2026
Running large language models locally comes down to two numbers: VRAM and memory bandwidth. Get both wrong and you're waiting 30 seconds per response. Get them right and your local Llama 3.3 feels snappier than ChatGPT. This guide cuts through the spec sheet noise and tells you exactly which GPU to buy in 2026 — with real benchmark data, not synthetic scores.
What Actually Makes a GPU Good for LLMs?
Most GPU benchmarks measure gaming — rasterization, ray tracing, frame rates. None of that matters for LLM inference. The two specs that determine your tokens-per-second are:
- ▸VRAM capacity — determines the largest model you can load without CPU offloading. Running even one layer on the CPU tanks throughput by 5–10×.
- ▸Memory bandwidth — determines how fast tokens generate once the model is loaded. LLM inference is memory-bandwidth-bound, not compute-bound.
- ▸CUDA / ROCm ecosystem — software support matters. NVIDIA has the broadest compatibility; AMD on Linux with ROCm is a solid second.
VRAM determines what you can run. Bandwidth determines how fast. Both matter.
GPU Benchmark Comparison (2026)
| GPU | VRAM | Bandwidth | 7B t/s (est.) | Max Model | OS Support |
|---|---|---|---|---|---|
| RTX 5070 | 12 GB GDDR7 | 672 GB/s | ~120 t/s | 13B Q4 | Win/Linux |
| RTX 5070 SFF | 12 GB GDDR7 | 672 GB/s | ~115 t/s | 13B Q4 | Win/Linux |
| RX 9060 XT | 16 GB GDDR6 | ~384 GB/s | ~65 t/s | 13B Q4 | Linux+ROCm |
| RTX 4070 Super | 12 GB GDDR6X | 504 GB/s | ~90 t/s | 13B Q4 | Win/Linux |
| Mac Mini M4 Pro* | 24–64 GB Unified | 273–546 GB/s | 65 t/s | 70B Q4 | macOS |
*Mac Mini M4 Pro uses unified memory — listed for reference since many users compare it against discrete GPUs.
#1 Pick: NVIDIA GeForce RTX 5070 (Blackwell)
The RTX 5070 runs on NVIDIA's Blackwell architecture (GB205 die) and delivers 672 GB/s of GDDR7 memory bandwidth — that's 33% more than the RTX 4070 Super it replaces. For LLM inference, that bandwidth translates directly to tokens per second.
The 12 GB VRAM ceiling does limit you to 13B models at Q4 quantization without CPU offloading. For 70B models you'll need to offload layers to system RAM — still usable at 4–8 t/s, but not comfortable. If 70B is your target, you need 40+ GB of VRAM (look at dual-GPU setups or Apple Silicon).
- ▸Best-in-class bandwidth for under $700
- ▸Full CUDA support — llama.cpp, Ollama, LM Studio, KoboldCpp all work out of the box
- ▸DLSS 4 for Stable Diffusion upscaling
- ▸Requires a full desktop PC (not a standalone unit)
#2 Pick: RTX 5070 SFF (Compact Builds)
Same Blackwell GPU, same 672 GB/s bandwidth, but in a short-form-factor card that fits Mini-ITX builds. Performance is within 5% of the full-size Windforce version — thermal throttling only appears in extreme sustained workloads. If you're building a compact home AI server, this is the card.
#3 Budget Pick: AMD Radeon RX 9060 XT (RDNA 4)
The RX 9060 XT ships with 16 GB of GDDR6 — 4 GB more than the RTX 5070 — and that extra headroom matters: you can run 13B models fully in VRAM with room to spare. Bandwidth is lower (~384 GB/s vs 672 GB/s), so token generation is slower, but the larger VRAM capacity is a real advantage for certain workloads.
VRAM Guide: What Models Can You Actually Run?
| VRAM | Models That Fit | What Gets Cut Off |
|---|---|---|
| 8 GB | 7B Q4, 3B Q8, 1.5B fp16 | 13B requires offloading |
| 12 GB | 13B Q4, 7B Q8, 7B fp16 | 34B requires offloading |
| 16 GB | 13B Q8, 34B Q4 (tight) | 70B requires offloading |
| 24 GB | 34B Q8, 70B Q4 | 70B fp16 won't fit |
| 48 GB+ | 70B Q8, 70B fp16, MoE models | Nothing under 100B |
Should You Buy a GPU or a Mac Mini M4 Pro?
This question comes up constantly. The Mac Mini M4 Pro with 24 GB unified memory delivers 65 tokens/second on Llama 3.1 8B — competitive with the RTX 5070 on that model size. But the Mac Mini can also run 70B models at Q4 quantization because unified memory is accessible to the GPU without a bandwidth penalty. No discrete GPU under $700 can do that.
The GPU wins on pure bandwidth (672 GB/s vs 273 GB/s) and on Stable Diffusion performance. The Mac Mini wins on 70B support, simplicity, low power draw (30W vs 200W+), and zero driver headaches. Choose based on your primary use case.
Final Recommendation
- ▸Best overall GPU: RTX 5070 (Gigabyte Windforce or ASUS SFF) — fastest LLM inference under $700 on Windows/Linux
- ▸Best for 16 GB VRAM: RX 9060 XT on Linux — extra VRAM headroom at lower cost
- ▸Best all-in-one AI machine: Mac Mini M4 Pro — runs 70B, dead simple, 30W idle
- ▸Skip: GPUs with 8 GB VRAM — they're already too small for 2026 models
Frequently Asked Questions
Q1What is the minimum VRAM for running LLMs locally in 2026?
8 GB VRAM is the practical minimum — it fits 7B models at Q4 quantization. But 12 GB is the sweet spot: you get 13B models fully in VRAM, which is a significant quality jump. Anything under 8 GB requires heavy quantization or CPU offloading that makes the experience painful.
Q2Is the RTX 5070 good for Stable Diffusion as well as LLMs?
Yes. The RTX 5070 handles SDXL, FLUX.1, and SD 3.5 Large comfortably within 12 GB VRAM. DLSS 4 upscaling also works with Stable Diffusion via ComfyUI plugins, giving you near-free quality upscaling on generations.
Q3Can I run a 70B model on an RTX 5070?
Not fully in VRAM — 70B at Q4 quantization needs about 40 GB. With 12 GB VRAM you'd offload most layers to system RAM, dropping throughput to roughly 2–4 tokens/second. Usable for slow inference, not for comfortable chat. You need the Mac Mini M4 Pro (24 GB unified) or a multi-GPU setup for 70B at reasonable speed.
Q4Is AMD RX 9060 XT good for LLMs on Windows?
ROCm support on Windows is still limited in 2026 — most tools (llama.cpp, Ollama) work better on Linux with AMD GPUs. If you're on Windows and want plug-and-play, NVIDIA is the safer choice. On Linux, the RX 9060 XT is excellent value.
Q5Is the RTX 5070 good for Stable Diffusion and FLUX.1?
Yes — the RTX 5070's 12 GB GDDR7 is the minimum sweet spot for FLUX.1 Dev, which needs 12 GB to run at full precision. SDXL runs comfortably at 1024×1024 in 3–6 seconds. The 672 GB/s bandwidth also helps with batched image generation. For FLUX.1 Schnell, 8 GB is sufficient but 12 GB gives you full quality without memory compression.
Q6Can the RX 9060 XT run LLMs on Windows?
Yes, but with caveats. AMD's ROCm runtime is production-stable on Linux but experimental on Windows via HIP. The RX 9060 XT runs llama.cpp with Vulkan on Windows — functional but 15–25% slower than ROCm on Linux. For Windows users who primarily want LLM inference, the RTX 5070 is easier to set up. If you use Linux, the RX 9060 XT's 16 GB at a lower price point is compelling.
Q7What GPU do I need to run 70B models locally?
No consumer GPU has 40 GB+ VRAM needed to run a 70B Q4 model fully in GPU memory. Your options: (1) Mac Mini M4 Pro with 24 GB unified memory + CPU offloading — runs 70B at 10–18 t/s; (2) Two GPUs with NVLink (enterprise territory); (3) CPU + RAM offloading on a high-RAM system — functional but slow (2–5 t/s). For most users, 13B at Q8 or 32B at Q4 is the practical ceiling on consumer hardware.
Q8Should I buy a GPU or a Mac Mini M4 Pro for local AI?
Depends on your OS and use case. Mac Mini M4 Pro (24 GB, $1,399): best plug-and-play experience, no driver configuration, silent, runs 70B models. RTX 5070 ($549, needs a PC): 2× faster on 7B/13B models, better for Stable Diffusion and FLUX.1, requires Windows or Linux setup. If you're on macOS or want zero friction: Mac Mini. If you want raw speed and don't mind configuration: RTX 5070 in a PC.