NVIDIA · Blackwell · 2025
NVIDIA GeForce RTX 5060 for local AI
Only 8GB, which is fine for 7B to 8B models; spend a little more for 16GB.
Updated · Speeds marked ~ are estimates; seemethodology
- VRAM
- 8GB
- Bandwidth
- 448 GB/s
- Power
- 145W
- Runs well
- 2 models
RTX 5060
Typically about $450
Can the RTX 5060 run LLMs?
The RTX 5060 has 8GB of GDDR7 and 448 GB/s of memory bandwidth, which decides how big a model fits and how fast it generates text. It comfortably runs models up to about 8B parameters: the largest in our catalog is Gemma 4 E4B at Q4_K_M (6.3GB with 8K context, ~62 tokens/sec). Our top all-round pick for it is Qwen3.5 9B (Q4_K_M, ~55 tokens/sec). Llama 3.3 70B Instruct only runs with partial CPU offload, at about 1.1 tokens/sec with CPU offload.
Recommendations
Best models for the RTX 5060
Best overall · also coding, reasoning, vision, largest that fits
Qwen3.5 9BQ4_K_M · 7GB · ~55 tokens/sec
Fits, tightRunner-up · also fastest
Gemma 4 E4BQ4_K_M · 6GB · ~62 tokens/sec
Runs wellStretch pick (CPU offload)
Qwen3.6 35B-A3BQ4_K_M · 24GB · about 28 tokens/sec with CPU offload
Partial offload
Compatibility
What runs on the RTX 5060
Every current model in our catalog, at the best quantization that fits in 8GB with 8K tokens of context. "Partial offload" means part of the model runs from system RAM (we assume 64GB), which is much slower.
| Model | Parameters | Best quant | Memory | Speed | Verdict |
|---|---|---|---|---|---|
| Gemma 4 E4B | 8B | Q4_K_M | 6.3 GB | ~62 tok/s | Runs well |
| Qwen3.5 9B | 9B | Q4_K_M | 7.2 GB | ~55 tok/s | Fits, tight |
| Phi-4 | 14.7B | Q4_K_M | 12 GB | ~9.4 tok/s | Partial offload |
| gpt-oss-20b | 20.9B (3.6B active) | MXFP4 | 15 GB | ~28 tok/s | Partial offload |
| Gemma 4 26B A4B | 26B (3.8B active) | Q4_K_M | 17 GB | ~26 tok/s | Partial offload |
| Qwen3.8 27B | 27B | Q4_K_M | 18 GB | ~4.0 tok/s | Partial offload |
| Gemma 4 31B | 31B | Q4_K_M | 22 GB | ~2.9 tok/s | Partial offload |
| Qwen3.6 35B-A3B | 35B (3B active) | Q4_K_M | 24 GB | ~28 tok/s | Partial offload |
| GLM-4.5-Air | 106B (12B active) | — | — | — | Won't fitWhat it needs |
| Llama 4 Scout | 109B (17B active) | — | — | — | Won't fitWhat it needs |
| gpt-oss-120b | 117B (5.1B active) | — | — | — | Won't fitWhat it needs |
| Qwen3.5 122B-A10B | 122B (10B active) | — | — | — | Won't fitWhat it needs |
| DeepSeek V4 Flash | 284B (13B active) | — | — | — | Won't fitWhat it needs |
| GLM-5.3-Flash | 320B (18B active) | — | — | — | Won't fitWhat it needs |
| GLM-5.3 | 744B (40B active) | — | — | — | Won't fitWhat it needs |
Measured
RTX 5060 benchmark results
| Model | Quant | Runtime | Generation | Prompt | Source |
|---|---|---|---|---|---|
| Llama 2 7B Chat | Q4_0 | llama.cpp (CUDA) | 97 tok/s | 3270 tok/s | llama.cpp discussion #15013: Performance of llama.cpp on NVIDIA CUDA · October 2026 |
Our take
Should you buy the RTX 5060 for AI?
The RTX 5060 has the same 448 GB/s GDDR7 bandwidth as the RTX 5060 Ti but only 8GB of memory. That fits 7B and 8B models at 4 to 5 bit with modest context, which is enough for basic chat and small coding assistants. Larger models spill into system RAM and slow down sharply. For local AI, the RTX 5060 Ti 16GB or a used RTX 3060 12GB is a better buy.
What we like
- Low price for a new Blackwell card
- Fast 448 GB/s memory for its class
- 145W board power
Watch out for
- 8GB is too little for most current models
- Long context quickly runs out of memory
Best for: Gaming PCs that occasionally run small models · Lightweight 7B to 8B assistants
Specifications
RTX 5060 specs that matter for AI
- Memory
- 8GB GDDR7
- Memory bandwidth
- 448 GB/s
- Memory bus
- 128-bit
- Architecture
- Blackwell
- Power
- 145W
- Interface
- PCIe 5.0 x8
- Released
- 2025
- Launch price
- $299
- Street price (new)
- ~$450 (2026-10)
- Street price (used)
- ~$360 (2026-10)
- Software
- CUDA, Vulkan
Cross-shop
Alternatives to the RTX 5060
Frequently asked questions
How much VRAM does the RTX 5060 have?
The NVIDIA GeForce RTX 5060 has 8GB of GDDR7 with 448 GB/s of memory bandwidth on a 128-bit bus.
What is the largest LLM the RTX 5060 can run?
Fully in GPU memory, the largest model in our catalog that runs comfortably is Gemma 4 E4B (8B) at Q4_K_M, using about 6GB with 8K context at ~62 tokens/sec.
Can the RTX 5060 run Llama 3.3 70B?
Only with partial CPU offload. At Q4_K_M the model needs about 47GB with 8K context, so part of it spills into system RAM and speed drops to about 1.1 tokens/sec with CPU offload. For usable 70B speeds you want 48GB of VRAM or more (for example two 24GB cards).
What is the best LLM to run on the RTX 5060?
Our top pick is Qwen3.5 9B at Q4_K_M, which fits in 8GB with room for 8K of context and runs at ~55 tokens/sec. See our full list of the best models for the RTX 5060 for coding, reasoning, and vision picks.
Is the RTX 5060 good for local AI in 2026?
Only 8GB, which is fine for 7B to 8B models; spend a little more for 16GB.