Short answer: yes. Llama 3.1 8B runs entirely on the GPU of an RTX 4060 (8 GB) when you use the common Q4_K_M quantization. At an 8K context it needs roughly 7 GB in total, and you can expect somewhere around 35 to 40 tokens per second. Q6 and Q8 versions do not fit, and long contexts get tight quickly.
Want to try your own combination? Use our Can My GPU Run This AI Model? calculator. The numbers below come from the same data.
The RTX 4060 in one paragraph
The RTX 4060 has 8 GB of GDDR6 on a 128-bit bus, which gives 272 GB/s of memory bandwidth. VRAM size decides which models fit, and bandwidth decides how fast they generate text. The 4060 is limited on both: 8 GB is the entry level for local AI, and 272 GB/s is low next to the 504 GB/s of an RTX 4070 or the 1,008 GB/s of an RTX 4090.
How much VRAM does Llama 3.1 8B need?
Memory use has three parts: the model weights, the KV cache that grows with your context length, and about 1 GB of runtime overhead. For Llama 3.1 8B (32 layers, 8 KV heads, head size 128) the Q4_K_M file is about 4.9 GB, and the KV cache costs about 0.13 GB for every 1,000 tokens of context at the default FP16 precision.
| Quantization | Context | Weights | KV cache | Total (est.) | On 8 GB |
|---|---|---|---|---|---|
| Q4_K_M | 4K | 4.92 GB | 0.54 GB | 6.5 GB | Fits |
| Q4_K_M | 8K | 4.92 GB | 1.07 GB | 7.0 GB | Fits |
| Q4_K_M | 16K | 4.92 GB | 2.15 GB | 8.1 GB | Just over |
| Q5_K_M | 4K | 5.72 GB | 0.54 GB | 7.3 GB | Fits |
| Q5_K_M | 8K | 5.72 GB | 1.07 GB | 7.8 GB | Tight |
| Q6_K | 4K | 6.62 GB | 0.54 GB | 8.2 GB | No |
| Q8_0 | 8K | 8.53 GB | 1.07 GB | 10.6 GB | No |
The weight sizes match the published GGUF files (4.92 GB for Q4_K_M and 8.54 GB for Q8_0). The overhead and the “fits” threshold are our estimates, so leave some margin. Windows also keeps a little VRAM for the desktop.
How fast is it?
Generating each token means reading the whole model from memory, so speed is capped by bandwidth divided by model size. For the 4060 that is 272 GB/s ÷ 4.92 GB, about 55 tokens per second as a theoretical ceiling. Real llama.cpp results on several RTX cards land at roughly 60 to 80 percent of that ceiling, which gives about 33 to 44 tokens per second.
What have people actually measured? The crowdsourced LocalScore.ai benchmark lists 38.3 tokens per second for Llama 3.1 8B Instruct Q4_K on an RTX 4060, with about 1,544 tokens per second for prompt processing. That sits inside the estimated range. Treat it as one community result, not a lab test: results vary with drivers, software version and background load. A few other pages quote different figures, but they either compute them from bandwidth or do not explain how they were measured, so we do not use them.
For comparison, 38 tokens per second is faster than most people read, so chat feels responsive. The slow part is long prompts, not the typing speed.
What does not fit
- Q6_K and Q8_0 of Llama 8B: they need more than 8 GB once context is added.
- 16K to 32K context at Q4_K_M: the KV cache alone is 2 to 4 GB, so the total passes 8 GB. A Q8 KV cache halves that cost but 32K is still too much on this card.
- 12B to 14B models: Gemma 3 12B (about 9.3 GB at Q4 with 8K context) and Qwen3 14B (about 11.4 GB) are too big. They can run with some layers offloaded to system RAM, but speed drops sharply.
Smaller models do better. Qwen3 4B needs about 4.7 GB and should reach roughly 66 to 88 tokens per second, which is a good choice if you want long context.
Tips to get the most from 8 GB
- Stay on Q4_K_M. It is the best size and quality balance for this card.
- Keep context around 4K to 8K unless you really need more.
- Turn on flash attention and a Q8 KV cache in Ollama (the
OLLAMA_FLASH_ATTENTIONandOLLAMA_KV_CACHE_TYPEsettings) to roughly halve the cache size and gain context. - Close other GPU apps such as games or browsers with hardware acceleration while the model runs.
- Check that the model is 100 percent on GPU. In Ollama,
ollama psshows how much was offloaded to the CPU.
Is the RTX 4060 a good choice for local AI?
It works well for 4B to 8B models, which cover everyday chat, summaries and light coding help. It is not a good buy if you want 12B and larger models: the 8 GB limit becomes the problem long before the speed does. If local AI is the main goal, cards with 12 GB or 16 GB are far more flexible, even when they are slower per token.
Frequently asked questions
Can an RTX 4060 run Llama 3 8B?
Yes, at Q4_K_M with up to about 8K context, entirely on the GPU.
Can it run Llama 3.1 8B at full precision?
No. FP16 needs about 16 GB for the weights alone. Even Q8_0 does not fit.
Is the 4060 Ti 8GB faster?
Both cards use 128-bit memory, but the 4060 Ti has 288 GB/s versus 272 GB/s, so expect only a small gain. The 8 GB limit is the same.
Sources and method
- RTX 4060 specs: TheFPSReview RTX 4060 family specs and the Wikipedia GeForce 40 series table (8 GB, 128-bit, 272 GB/s).
- Model architecture: Llama 3.1 8B config on Hugging Face (mirror of the Meta repository, which requires a login) and Qwen3 4B config.
- GGUF file sizes: bartowski’s Llama 3.1 8B GGUF repository, as reported by Markaicode.
- Community speed: LocalScore.ai RTX 4060 results (crowdsourced).
- VRAM numbers are estimates from our calculator. Last updated 3 Nov 2026.
